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380 | Completed (31) | 893 | Economics | Inequality Measurement | Economics | -99 | Data Science with R, Python, SQL Bootcamp | -99 | Austria | 501-1,000 employees | Data Scientist | -99 | 4 | 4 | 5 | 2 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Time Series Forecasting | not quoted | -99 | not quoted | -99 | quoted | Customer Segmentation | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Complex | Complex | Neutral | 0 | Very Complex | Easy | 15 | 15 | 20 | 15 | 15 | 15 | 5 | DIffculty reaching and communicating with stakeholders | -99 | -99 | Access to data either for organisational or technical reasons | -99 | -99 | Low quality and transparency of available data | -99 | -99 | Available infrastructure often not enough | -99 | -99 | Often times is the evaluation not budgeted either in time or financially | -99 | -99 | Missing skills from data engineer or unavailable infrastruture | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Missing alignment between expectations and work from data scientists | Missing infrastructure for a sustainable operation in production | -99 | Frequently | 70 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | not quoted | quoted | not quoted | -99 | Yes, Please, specify | -99 | -99 |
383 | Completed (31) | 1,897 | Economics | Econometrics, Finance | M.Sc. in Data Science + M.Sc. in Finance | -99 | -99 | -99 | Austria | More than 2,000 employees | Developer | -99 | 4 | 2 | 3 | 2 | 6-10 members | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Typescript | quoted | Sales discounts, costs | not quoted | -99 | not quoted | -99 | quoted | Customers | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Neutral | High Relevance | Neutral | Neutral | Neutral | Complex | Complex | Easy | Neutral | Complex | Easy | 20 | 20 | 15 | 10 | 10 | 15 | 10 | Value Creation | End-to-end process | Roles & Responsibles | Roles & Responsibles | Data Sharing | -99 | Scope Limitation | Guidelines | -99 | Overengineering | -99 | -99 | Benchmarking | Context | -99 | Tech-Stack | CI/CD | -99 | Access Rights | -99 | -99 | Alignment of Departments | Speed of First Results | -99 | Speed of First Results | Value Creation | Roles & Responsibles | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
385 | Completed (31) | 1,328 | Computer Science | -99 | computer science | -99 | -99 | -99 | Austria | 11-50 employees | Other, which one? | teacher | 35 | 10 | 10 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Complex | Very Complex | Complex | Complex | Complex | Complex | 20 | 10 | 30 | 10 | 10 | 15 | 5 | different languages | -99 | -99 | technical issues | -99 | -99 | various sources of errors, uncertainty | -99 | -99 | select right model | -99 | -99 | chosing right methodology | -99 | -99 | user acceptance | -99 | -99 | sustained effort necessary | -99 | -99 | -99 | -99 | -99 | data collection and preprocessing | deployment issues | -99 | Frequently | 80 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
386 | Completed (31) | 901 | Software Engineering | Media Informatics | Visual Computing M.Sc. | -99 | -99 | -99 | Austria | More than 2,000 employees | Other, which one? | Researcher | 10 | 3 | 2 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | surface reconstruction | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Behavior Trees | High Relevance | Neutral | Neutral | High Relevance | Neutral | Low Relevance | Low Relevance | Easy | Neutral | Neutral | Complex | Neutral | Easy | Neutral | 10 | 10 | 10 | 30 | 20 | 10 | 10 | find problem | -99 | -99 | get sensors | data protection | -99 | high-performance computing | -99 | -99 | long iterations | -99 | -99 | code quality | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | long iterations | data protection | code quality | Frequently | 50 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
390 | Completed (31) | 990 | Mathematics | pattern recognition, image processing, computer vision | technical mathematics, maître d'informatique | mathematics | none | -99 | Austria | More than 2,000 employees | Project Lead / Project Manager | -99 | 40 | 3 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | computer vision | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | segmentation | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | Neutral | I don't know | Very Complex | Neutral | Complex | Very Complex | Very Complex | Complex | Complex | 5 | 30 | 5 | 30 | 10 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Always | 95 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | none | not quoted | -99 | quoted | 5 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
393 | Completed (31) | 1,760 | Software Engineering | -99 | M.Sc in Computer Science | PhD in Computer Science | -99 | -99 | Sweden | 11-50 employees | Other, which one? | CEO | 30 | 20 | 10 | 5 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Energy | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Extremely Relevant | High Relevance | Very Complex | Neutral | Neutral | Neutral | Complex | Complex | Neutral | 35 | 10 | 20 | 15 | 5 | 10 | 5 | Lack of domain knowledge | -99 | -99 | Lack of tagged data | System integration | -99 | Merging different formats | -99 | -99 | Identifing appropriate algorithms/parameters | -99 | -99 | -99 | -99 | -99 | Poor system capacity in the field | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Lack of domain knowledge for project participants | Lack of tagged data | Poor system capacity in operations | Sometimes | 80 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
394 | Completed (31) | 2,056 | Physics | -99 | Physics | Physics | Machine Learning course Coursera | -99 | Germany | 51-250 employees | Other, which one? | Machine Learning Engineer | 2 | 2 | 1 | -99 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | landmarks on medical images | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Complex | Complex | Neutral | Neutral | Very Complex | I don't know | 10 | 5 | 30 | 20 | 10 | 20 | 5 | understand domain-specific language | -99 | -99 | get high-quality and representative data | data cleansing | -99 | adapt preprocessing pipeline to the specific problem | -99 | -99 | develop generic approach to be useful to a wider range of problems | -99 | -99 | -99 | -99 | -99 | meet high regulatory requirements in medical sector | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 80 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://ww2.unipark.de/uc/seml/ |
397 | Completed (31) | 3,027 | -99 | -99 | -99 | Ph.D. in Mathematics | -99 | -99 | Sweden | 1-10 employees | Data Scientist | -99 | 10 | 5 | 10 | 2 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Energy | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Neutral | Neutral | Neutral | Very Complex | Very Complex | Complex | Neutral | Easy | Neutral | Neutral | 15 | 15 | 15 | 25 | 25 | 5 | 0 | Project management tend to lack understanding | -99 | -99 | Out of touch with reality | Doesn't factor in the monetary cost of data collection | -99 | Tend to reveal plenty of human errors in the data collection process | Overlooked in budget | -99 | Tend to lack a full set of data due to faults in data collection | -99 | -99 | Less of a problem | -99 | -99 | Mostly an afterthought, running on some server somewhere | -99 | -99 | Nonexistent | -99 | -99 | -99 | -99 | -99 | Grossly underestimated cost for data collection | Grossly underestimated cost for data pre-processing (due to artefacts in the data that has more to due with the human factor than the problem domain) | Unwillingness to change model creation and training to fit the data available (poor project management) | Always | 5 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Wishful thinking in the project proposal | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 5 | not quoted | not quoted | not quoted | not quoted | quoted | Occational ocular inspection of graphs and other graphics by human expert. | Yes, Please, specify | Auto-sklearn, Auto-PyTorch | -99 |
404 | Completed (31) | 284 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://www.linkedin.com/ |
410 | Completed (31) | 2,124 | Computer Engineering | Software Engineering | Computer Engineering | -99 | -99 | Ph.D. Student in Computer Engineering ( machine learning in eHealth domain) | Turkey | 11-50 employees | Solution Architect | -99 | 20 | 2 | 2 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | to score the degree of disease | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | High Relevance | Neutral | Neutral | Neutral | Complex | Neutral | Complex | I don't know | 10 | 5 | 10 | 25 | 25 | 10 | 15 | object oriented analysis | -99 | -99 | privacy issues | -99 | -99 | scheme unification | -99 | -99 | hyperparameter optimisation | -99 | -99 | -99 | -99 | -99 | compatilibity & environment problems | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | object oriented analysis | -99 | -99 | Never | 30 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
417 | Completed (31) | 3,259 | -99 | -99 | Informatics | Computer Sciences | -99 | -99 | Austria | More than 2,000 employees | Project Lead / Project Manager | -99 | 20 | 10 | 10 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | Production | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Very Complex | Neutral | Complex | Complex | Complex | Neutral | 15 | 25 | 10 | 20 | 15 | 10 | 5 | Complex problem domains | Not clear requirements | -99 | Avaliability of necessary information | Form in which data are stored | -99 | Quality of data | -99 | -99 | Efficiency | -99 | -99 | Selection of appropriate performance metrics | -99 | -99 | Explainability of results | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data collection | Quality of data | -99 | Rarely | 30 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Auto-sklearn | -99 |
419 | Completed (31) | 902 | Computer Science | project management specialization | M. SC. Computer science | -99 | Coursera course on ML | Applied data science Ericsson | Lebanon | More than 2,000 employees | Solution Architect | -99 | 15 | 1 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Neutral | Complex | Neutral | Complex | Neutral | Complex | 20 | 10 | 5 | 40 | 15 | 5 | 5 | -99 | -99 | -99 | -99 | -99 | X | -99 | X | -99 | -99 | -99 | -99 | X | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Model Evaluation | Data collection | Data pre-processing | Sometimes | 30 | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
421 | Completed (31) | 1,310 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://www.linkedin.com/ |
423 | Completed (31) | 1,058 | Computer Science | Data Science specialization | M.Sc. in Computer Science | Computer Science | Udacity course on ML | -99 | Japan | 1-10 employees | Data Scientist | -99 | 4 | 3 | 4 | 3 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Using prediction to evaluate value of room price | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Neutral | Neutral | Neutral | Neutral | Easy | Easy | 25 | 25 | 20 | 15 | 5 | 5 | 5 | the problem faced the schedulee | -99 | -99 | need more data | -99 | -99 | improve quality | -99 | -99 | need more time | -99 | -99 | arrive performance metric | -99 | -99 | platform | -99 | -99 | data split | -99 | -99 | -99 | -99 | -99 | Problem Understanding and Requirements | Data Collection | Model Deployment | Sometimes | 70 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 60 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
425 | Completed (31) | 2,068 | Computer Engineering | Data Engineering | Computer Engineering | -99 | Google Cloud Professional Data Engineer | -99 | Turkey | 51-250 employees | Solution Architect | -99 | 17 | 7 | 10 | 3 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Not Relevant at All | Not Relevant at All | Not Relevant at All | Not Relevant at All | Not Relevant at All | Not Relevant at All | Not Relevant at All | Very Complex | Complex | Neutral | Complex | Complex | Neutral | Neutral | 20 | 25 | 30 | 20 | 5 | 0 | 0 | 1 | -99 | -99 | -99 | 1 | -99 | -99 | -99 | 1 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem Understanding and Requirements | Data Collection | Data Pre-Processing | Frequently | 30 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 5 | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Yes, Please, specify | Google AutoML, IBM Watson | https://www.google.com/ |
426 | Completed (31) | 1,066 | Industrial Engineering | -99 | M.Sc. in Computer Science | -99 | Google Professional ML Engineer and Data Engineer Certification | -99 | Turkey | 51-250 employees | Data Scientist | -99 | 1 | 3 | 3 | 1 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Neutral | Very Complex | Easy | Complex | Neutral | Complex | 10 | 5 | 50 | 5 | 10 | 10 | 10 | communication | -99 | -99 | non-reliability | -99 | -99 | data complexity | non structured dbs | -99 | long training hours | -99 | -99 | business objective | -99 | -99 | platform restrictions | -99 | -99 | architecture design | -99 | -99 | -99 | -99 | -99 | data complexity | non-reliability | communication | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | quoted | not quoted | -99 | No | -99 | https://www.google.com/ |
428 | Completed (31) | 606 | Computer Science | AI & ML | MSc. in Computer Science in AI | -99 | -99 | -99 | Turkey | 251-500 employees | Solution Architect | -99 | 10 | 5 | 8 | 8 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | Neutral | Neutral | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Neutral | Complex | Very Complex | Very Complex | Complex | Neutral | 25 | 5 | 25 | 25 | 5 | 10 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data Preperation problems | Training failures | not enough know how | Frequently | 70 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | quoted | Dataiku | not quoted | 90 | not quoted | not quoted | quoted | quoted | not quoted | -99 | Yes, Please, specify | Dataiku | -99 |
433 | Completed (31) | 1,249 | Statistics | Data science and data engineer | -99 | -99 | -99 | -99 | Turkey | 251-500 employees | Solution Architect | -99 | 10 | 10 | 50 | 20 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Sas | quoted | Value prediction | quoted | Churn | not quoted | -99 | quoted | Segmentation | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Neutral | High Relevance | Extremely Relevant | Very Complex | Complex | Complex | Neutral | Neutral | Very Complex | Very Complex | 10 | 10 | 50 | 10 | 5 | 10 | 5 | End users can not focus problems that is occured into business process | Getting resource from business | -99 | Metadata | -99 | -99 | Feature and business mapping | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | There is no framework | -99 | -99 | This is not prioritized | -99 | -99 | -99 | -99 | -99 | focusing algorithms not process | -99 | -99 | Sometimes | 80 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
437 | Completed (31) | 2,037 | Computer Science | -99 | M.Sc. in Computer Science | -99 | -99 | -99 | Brazil | 251-500 employees | Data Scientist | -99 | 3 | 10 | 20 | 15 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Very Complex | Complex | Very Complex | Complex | Neutral | Neutral | Neutral | 20 | 20 | 25 | 20 | 5 | 5 | 5 | clarity in information | lack of a specialist business professional to support the step. | -99 | lack of useful information in the available data sources. | -99 | -99 | errors, inconsistencies and absence of relevant information in the datasets. | -99 | -99 | large number of models and parameterizations. | computational resource limitation. | -99 | lots of metrics to evaluate. | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | errors, inconsistencies and absence of relevant information in the datasets. | lack of a specialist business professional to support the step. | computational resource limitation. | Rarely | 30 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Auto-sklearn | -99 |
438 | Completed (31) | 1,243 | Mathematics, Physics | Mathematical Physics | Msc in Physics | Ph.D. in Mathematical Physics | -99 | -99 | Austria | 11-50 employees | Data Scientist | -99 | 19 | 3 | 10 | 5 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Scala | not quoted | -99 | quoted | Classifying various sources of text | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | High Relevance | Neutral | Neutral | Complex | Neutral | Easy | Neutral | Neutral | 20 | 20 | 20 | 10 | 5 | 10 | 15 | Not missing a hidden bias in the data | -99 | -99 | Ensure a good unbiased sample | -99 | -99 | Data Processing can be tricky and crucial for a working model | -99 | -99 | Finding the correct hardware to run on | Configuring parameters | -99 | -99 | -99 | -99 | Scalability, efficient usage of resources | -99 | -99 | Creating a good feedback loop | Finding good references to compare to | -99 | -99 | -99 | -99 | Ensure a good unbiased sample | Data Processing can be tricky and crucial for a working model | Configuring parameters | Rarely | 90 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | wiki | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
473 | Completed (31) | 1,496 | Civil Engineering | -99 | M.Sc. in Computational/Civil Engineering | D.Sc. in Computational/Civil Engineering | Deep Learning Specialization (Coursera), Data Scientist NanoDegree (Udacity), Deep Learning NanoDegree (Udacity), Artificial Intelligence NanoDegree (Udacity) | Many other courses on Udemy, and Full Stack Deep Learning Bootcamp (Berkeley Universit) | Brazil | More than 2,000 employees | Project Lead / Project Manager | -99 | 5 | 5 | 20 | 3 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Very Complex | Neutral | Easy | Neutral | Neutral | Neutral | 15 | 25 | 15 | 15 | 10 | 10 | 10 | Lack of knowledge about the business | -99 | -99 | Poor data governance in traditional companies | -99 | -99 | ok | -99 | -99 | ok | -99 | -99 | For traditional engineering applications, it shall be stablished an adequate metric threshold | -99 | -99 | ok | -99 | -99 | ok | -99 | -99 | -99 | -99 | -99 | Poor data governance in traditional companies | For traditional engineering applications, it shall be stablished an adequate metric threshold | -99 | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | Subject Matter Expert | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | DataRobot | -99 |
448 | Completed (31) | 979 | -99 | -99 | M.Sc. in Data Visualization | -99 | AWS Machine Learning Specialty | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 18 | 6 | 5 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | quoted | Regression | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Neutral | Extremely Relevant | Extremely Relevant | High Relevance | Low Relevance | Complex | Complex | Easy | Neutral | Neutral | Very Complex | Complex | 10 | 40 | 5 | 20 | 5 | 15 | 5 | Client engagement | -99 | -99 | No annotated data | Not enought data | -99 | Data versioning | -99 | -99 | Manage of libs | Easyness of train distribution | Experiment controll | Find a good metric that maps to business success | -99 | -99 | Standard way to deploy (on premissis or on cloud) | -99 | -99 | No live annotaded data | -99 | -99 | -99 | -99 | -99 | No annotated data | Not enought data | Find a good metric that maps to business success | I don't know | 0 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | Use some cloud solution | not quoted | 5 | not quoted | not quoted | not quoted | not quoted | quoted | Performance logs (CPU, memory, etc) | No | -99 | https://statics.teams.cdn.office.net/ |
447 | Completed (31) | 1,087 | Telecommunications Engineering | -99 | System and Computer Engineering, PESC/UFRJ | Informatics, PPGI/UFRJ | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 10 | 8 | 3 | 3 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Complex | Complex | Complex | Very Complex | Complex | 20 | 20 | 10 | 20 | 10 | 10 | 10 | -99 | Problem Understanding and Requirements | -99 | Data Collection | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Model Deployment | -99 | -99 | -99 | -99 | -99 | -99 | Missing Data | Problem Understanding | Lack of human resources | Frequently | 60 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://statics.teams.cdn.office.net/ |
443 | Completed (31) | 1,584 | Computer Engineering | Data Science specialization | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 5 | 5 | 10 | 6 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | predict ROP in drilling, predict investment in drilling projects, prediction | quoted | predict domain on wells | quoted | similarity among wells and reservatories | quoted | predict anomaly detection on drilling | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | Very Complex | Complex | Very Complex | Complex | Complex | Very Complex | 5 | 25 | 15 | 20 | 25 | 10 | 0 | Knowledge domain | -99 | -99 | Data Quality | Data Integration | -99 | Knowledge domain | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | User's feedback | -99 | -99 | -99 | -99 | -99 | Data Collection | Problem Understanding and Requirements | Data Pre-Processing | Sometimes | 50 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 40 | quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Auto-sklearn | https://statics.teams.cdn.office.net/ |
446 | Completed (31) | 801 | Mathematics | Data Science and Machine Learning | -99 | -99 | Several Coursera, Udacity and Microsoft Azure certificates on Data Science topics | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 12 | 4 | 5 | 3 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Estimate discharded water total oil and grease; estimate time-to-fail of offshore heat exchange equipment | quoted | analogue oil reservoirs | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Neutral | High Relevance | High Relevance | Very Complex | Complex | Neutral | Complex | Neutral | Very Complex | Neutral | 30 | 20 | 20 | 10 | 5 | 10 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem Understanding and Requirements | -99 | -99 | I don't know | -77 | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | Specific container | quoted | We are trying... | not quoted | 30 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://statics.teams.cdn.office.net/ |
444 | Completed (31) | 1,765 | -99 | -99 | Mestre em Engenharia Eletrônica e Computação | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 2 | 8 | 2 | 1 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | 1 | quoted | 1 | not quoted | -99 | quoted | 1 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | PCA | Extremely Relevant | Extremely Relevant | 0 | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Very Complex | Complex | Easy | Neutral | Complex | Complex | 10 | 30 | 10 | 10 | 10 | 20 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 70 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://teams.microsoft.com/ |
445 | Completed (31) | 2,643 | Systems Analysis | Especialização em BI e ML | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 36 | 8 | 150 | 12 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | reinforcement learning | High Relevance | Neutral | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | Easy | Neutral | Complex | Neutral | Neutral | Neutral | Neutral | 5 | 10 | 30 | 10 | 5 | 10 | 30 | expectativa inflada | especialista no domínio | indefinição do alvo | falta de diversidade | anotação | falta de dados | dados faltantes | engenharia de features | -99 | disponibilidade de GPU | otimização de parâmetros | controle de experimentos | escolha das métricas | eliminação de viés | explicabilidade | escalabilidade | performance | -99 | critérios para retreinamento | recursos dedicados | -99 | -99 | -99 | -99 | Expectativa inflada do cliente para problema mal definido ou sem um alvo claro. | Falta de diversidade e quantidade de dados anotados com qualidade | Disponibilidade de especialista no domínio do problema | Sometimes | 30 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 50 | not quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Várias | https://statics.teams.cdn.office.net/ |
450 | Completed (31) | 1,226 | Engineering | -99 | Master in Business Administration | Computer Science Candidate | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 1 | 1 | 2 | 2 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Complex | Complex | Very Complex | Neutral | Neutral | Neutral | Neutral | 20 | 20 | 30 | 10 | 5 | 10 | 5 | Many times problems are not well understood | -99 | -99 | -99 | -99 | Data many times not available | -99 | IMportant to have data ready to be processed | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data Pre processing | Problem Understanding | -99 | I don't know | 20 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | Meeting Notes, mostly | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | quoted | Being Implemented | not quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://statics.teams.cdn.office.net/ |
454 | Completed (31) | 629 | Computer System Administration | Project Management Specialization | -99 | -99 | -99 | -99 | Colombia | 501-1,000 employees | Business Analyst | -99 | 5 | 3 | 10 | 7 | 11-20 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Low Relevance | High Relevance | Not Relevant at All | Neutral | Neutral | Neutral | Very Complex | Easy | Easy | Easy | Easy | 20 | 10 | 50 | 5 | 5 | 5 | 5 | Access to domain experts | -99 | -99 | Permission to access the data | -99 | -99 | The task is complex by nature | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | What monitoring? There are many sources | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 30 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | Partially. There are some pipelines but .... | not quoted | 20 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
462 | Completed (31) | 2,406 | Computer Science | -99 | MSc Computer Science | -99 | UC Berkeley Data Science (10-Week Course) | -99 | United States | 51-250 employees | Data Scientist | -99 | 15 | 5 | 10 | 4 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Predicting net revenue of financial operations | quoted | Classifying risks of financial trades and trading portfolios | not quoted | -99 | not quoted | -99 | quoted | Pattern recognition in values of financial assets | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Very Complex | Complex | Complex | Very Complex | Complex | Complex | Very Complex | 10 | 15 | 30 | 20 | 10 | 10 | 5 | Low customer and end-user involvement | Limited business understanding | No standardized way to clearly describe the needs | Heterogeneity of data sources | Limited data availability (volume) | No documentation and limited organization of existing data sources | Several formats of data | Identifying and eliminating outliers | Limited knowledge on statistics to understand when to securely apply pre-processing operations | Selecting the right machine learning algorithm (to address what is needed, which is typically not explicitly documented) | Hyperparameter optimization | Defining how and when the model should be updated | Selecting the right evaluation metrics | Having enough data to allow a good evaluation | -99 | Choosing the most appropriate way of deploying the model | Automating the update of the model (retrain) and its deployment (MLOps) | -99 | Knowing what to monitor | Preparing live feedback on the model's effectiveness | -99 | Automating the model training and deployment | -99 | -99 | Selecting the right machine learning algorithm (to address what is needed, which is typically not explicitly documented) | Limited business understanding | Automating the model training and deployment | Frequently | 70 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | Product Owner | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | not quoted | not quoted | quoted | Actually the whole model monitoring is quite limited within my organization | No | -99 | https://machine-learning-survey.web.app/ |
471 | Completed (31) | 2,202 | -99 | -99 | -99 | -99 | I have completed some courses on ML and Data Science, mainly on Alura (a Brazilian e-learning platform) and less frequently on Udemy. | I also took a technical course on Electronics during High School. | Brazil | More than 2,000 employees | Data Scientist | -99 | 0 | 0 | 3 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Fraud Prevention | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Very Complex | Very Complex | Complex | Complex | Complex | Neutral | 10 | 20 | 25 | 15 | 15 | 10 | 5 | Understanding what are the relevant variables to model our problem. | -99 | -99 | Dealing with inconsistent data between different data bases. | Working around technical limitations regarding computational power. | Structuring a good and efficient pipeline for the data. | Dealing with high dimensional data and the computing limitations we often face. | Finding ways to make the data appropriate for the ML model (e.g.: converting strings, applying standardization, etc) | Dealing with NULL values and finding the best way to work them out. | Dealing with imbalanced data. | Calibrating the model. | -99 | Evaluate the model's performance and assess if it fulfills its requirements. | -99 | -99 | Since I recently started working with ML models, I haven't had the opportunity to deploy a model yet. | -99 | -99 | Same as above. | -99 | -99 | -99 | -99 | -99 | Dealing with imbalanced data. | Working around technical limitations regarding computational power. | Dealing with high dimensional data and the computing limitations we often face. | I don't know | 0 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
463 | Completed (31) | 1,068 | Electrical and Electronics Engineering | Data Science Especialization | M.Sc. in Business Administration | -99 | Google Professional ML Engineer Certification | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 3 | 7 | 20 | 5 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Easy | Neutral | Neutral | Neutral | Complex | Neutral | 20 | 10 | 15 | 10 | 10 | 30 | 5 | Access to stakeholders | Communication | -99 | Data dispersion | -99 | -99 | Cleaning data | -99 | -99 | Choosing the best algorithm | -99 | -99 | Define the acceptable level of error | -99 | -99 | Infrastructure | Integration with application | -99 | Monitoring tools | setting alarms | -99 | -99 | -99 | -99 | Access to stakeholders | Integration with application | -99 | Frequently | 30 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | SageMaker Auto -Pilot | https://machine-learning-survey.web.app/ |
485 | Completed (31) | 912 | Production Engineering | Executive MBA | -99 | -99 | DS4A - Correlation One | -99 | Brazil | More than 2,000 employees | Other, which one? | FP&A coordinator | 5 | 5 | 5 | 5 | 1-5 members | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Complex | Very Complex | Complex | Complex | Neutral | Easy | Very Easy | 20 | 15 | 10 | 15 | 10 | 20 | 10 | Business compreehension | Operation complexity | Desirable goals | Offline databases | Databases with inputs manually | -99 | Databases with wrong informations | -99 | -99 | Need a lot of tests, re-evaluate the model | Sometimes we need go back to understanding and requirements | -99 | Understanding if the results are reliable | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Business compreehension | Offline databases | Desirable goals | Frequently | 50 | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 70 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
470 | Completed (31) | 730 | Information Systems | Software Architecture | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Developer | -99 | 10 | 0 | 1 | 1 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Complex | Neutral | Very Complex | Very Complex | Complex | Neutral | Neutral | 15 | 5 | 30 | 30 | 10 | 5 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 10 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
474 | Completed (31) | 1,026 | Computer Science | Data Management | Computer Engineering | Computer Engineering | No certification | -99 | Turkey | More than 2,000 employees | Other, which one? | Professor | 28 | 5 | 3 | 1 | 6-10 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Ad marketing, ERP, production management | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Predicting the eCPM value | quoted | İmbalanced classification of production data | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Neutral | Low Relevance | Neutral | Complex | Neutral | Complex | Complex | Neutral | Complex | 25 | 15 | 15 | 20 | 10 | 5 | 10 | 1 | -99 | -99 | -99 | 1 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | 1 | -99 | -99 | -99 | Problem Understanding and Requirements | Data Collection | Model Monitoring | Frequently | 50 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 25 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
478 | Completed (31) | 991 | Computer Science | -99 | M.Sc. in Computer Science | -99 | -99 | -99 | Sweden | 1,001-2,000 employees | Other, which one? | Research student | 2 | 1 | 2 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Smart Grid | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Low Relevance | Low Relevance | Complex | Complex | Complex | Very Complex | Complex | Neutral | Neutral | 20 | 10 | 40 | 20 | 10 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | Not applicable, it was for research purposes. | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | quoted | none | No | -99 | https://machine-learning-survey.web.app/ |
487 | Completed (31) | 3,030 | Computer Science | null | null | null | Coursera course on ML | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 7 | 1 | 3 | 1 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Predict cashflow. Predict loan value. | not quoted | -99 | not quoted | -99 | quoted | Clustering clients | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Neutral | Neutral | Easy | Easy | Complex | Neutral | 15 | 15 | 30 | 10 | 10 | 10 | 10 | Broad scope | Requirements not so clear | -99 | Data governance | Many origins | -99 | Data quality | Data pattern | -99 | -99 | -99 | -99 | Define measures | -99 | -99 | Pipeline not defined | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data governance | Data quality | Broad scope | Frequently | 70 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
481 | Completed (31) | 749 | Physics | -99 | Master in physics | -99 | Data Science, Machine learning and Big Data specialization on Udemy | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 1 | 3 | 20 | 15 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Prediction of the probability of a telemarketing cold-call ending on a sale; prediction of the probability of a telemarketing cold-call collecting a debt | quoted | Classifying telemarketing representatives according to their efficiency against a propensity-scored list of names to call (patented) | not quoted | -99 | quoted | Clustering personas for custom telemarketing strategies | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | Custom tensor-calculus algorithm | Extremely Relevant | Neutral | High Relevance | Neutral | Extremely Relevant | Neutral | High Relevance | Very Complex | Easy | Neutral | Very Easy | Easy | Complex | Neutral | 50 | 5 | 15 | 5 | 10 | 10 | 5 | People unwilling to share information | Finding who knows what | Having to quickly learn new stuff | Finding where is the data | Joining multiple data sources | -99 | NaN imputation | Data normalization | Data cleansing | Training times (for SVMs and higher-dimensional algorithms) | -99 | -99 | Finding good metrics for unbalanced problems | Comparing different metrics | -99 | Find a platform flexible enough to host multiple different kinds of models | Hooking up APIs and ensuring the model is scalable and accesible | -99 | -99 | -99 | -99 | -99 | -99 | -99 | People unwilling to share information | Data normalization | Find a platform flexible enough to host multiple different kinds of models | Always | 50 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 75 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
482 | Completed (31) | 2,075 | Statistics | -99 | Mestrado em Engenharia de Produção | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 10 | 10 | 10 | 10 | 11-20 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | SAS / SQL | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Easy | 0 | Complex | Easy | Easy | Very Complex | Very Complex | 5 | 20 | 10 | 20 | 15 | 15 | 15 | Pensamento analítico | Disponibilidade de tempo das equipes | Prioridade para resolução com a cultura data driven | Falta de histórico | Armazenamento - falta de espaço | Diferentes dontes de informação | Tempo de processamento | Armazenamento - falta de espaço | Tratamento de inconsistências | Convergência do modelo | Omissão de variáveis importantes devido a falta de histórico | Software adequado e mais rápido | Tradução para área de negócio | Aplicação na prática | Disponibilidade da equipe de BI | Falta de priorização da diretoria | -99 | -99 | Escassez de tempo | Escassez de recurso | falta de priorização da diretoria | -99 | -99 | -99 | Falta de recurso para monitoramento | Despriorização por falta da cultura data driven | -99 | Rarely | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
483 | Completed (31) | 522 | Computer Science | -99 | Computer Science | -99 | Coursera - Data Scientist | -99 | Brazil | More than 2,000 employees | Project Lead / Project Manager | -99 | 2 | 2 | 3 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Very Complex | Neutral | Very Easy | Very Complex | Easy | 30 | 20 | 20 | 13 | 2 | 10 | 5 | Not understand | -99 | -99 | Data in different places | Data with missing data | Lack of pattern | Lack of a rule | -99 | -99 | Choose the best one | -99 | -99 | Not have | -99 | -99 | Dont Know | Need dev support | -99 | Easy nowadays | -99 | -99 | -99 | -99 | -99 | Grab the incorrect Data | Prepare it wrongly | Overfitting | Frequently | 40 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | AutoSklearn, Autopilot from AWS | https://machine-learning-survey.web.app/ |
486 | Completed (31) | 852 | -99 | -99 | CEFET/RJ | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 6 | 0 | 1 | 0 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Classify Cancer | quoted | Detect Client behavior | not quoted | -99 | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Very Complex | Very Complex | Neutral | Complex | Complex | Complex | 5 | 5 | 60 | 10 | 10 | 5 | 5 | Client desires | -99 | -99 | Choose data | Representative Data | -99 | Bad Pre-processing | -99 | -99 | Bias Model | Choose a algorithm | -99 | -99 | -99 | -99 | Choose a platform | Platforms Costs | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Bias Model | Choose data | Representative Data | I don't know | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://ww2.unipark.de/uc/seml/ |
488 | Completed (31) | 2,150 | Computer Science | Managent Analytics | Data Science | -99 | -99 | -99 | Canada | More than 2,000 employees | Requirements Engineer | -99 | 6 | 4 | 3 | 2 | 11-20 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Neutral | Neutral | Very Complex | Complex | Very Complex | Complex | 30 | 5 | 5 | 20 | 25 | 5 | 10 | business understanding | unreasonable timelines | unreasonable expectations | business understanding | data quality | -99 | -99 | -99 | -99 | -99 | -99 | -99 | relevance of recommendations | feasibility of recommendations | -99 | lack of production support | lack of understanding of ML in general | -99 | lack of production support | lack of understanding of ML in general | -99 | -99 | -99 | -99 | unreasonable expectations | unreasonable timelines | lack of production support | Frequently | 40 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
489 | Completed (31) | 1,717 | Civil Engineering | MBA in Logistics, MBA in Data Science (USP - ICMC) | -99 | -99 | Data Science For All Graduate | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 1 | 1 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | SQL | not quoted | -99 | quoted | Churned users classification | not quoted | -99 | quoted | Unsupervised users clustering | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Extremely Relevant | Complex | Neutral | Complex | Neutral | Easy | Complex | Very Complex | 10 | 10 | 20 | 30 | 10 | 10 | 10 | lack of business knowledge | short deadlines | goal too wide | -99 | -99 | -99 | outlier treatment | -99 | -99 | -99 | -99 | -99 | lack of a defined goal | -99 | -99 | no structured pipeline | -99 | -99 | -99 | -99 | -99 | no feature store | early stage ML pipeline | -99 | lack of business knowledge | goal too wide | no structured pipeline | Frequently | 100 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | SRE Team, mainly the Data Lead | not quoted | not quoted | not quoted | not quoted | quoted | quoted | Squad's OKRs | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Google Docs shared between stakeholders | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
491 | Completed (31) | 2,855 | Statistics | CRM Specialization | -99 | -99 | -99 | Alura Data Science Courses | Portugal | More than 2,000 employees | Other, which one? | Data Analyst | 0 | 8 | 8 | 8 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | Kaizen | Mostly traditional | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | SAS Guide, SAS Miner, Knime, IBM Modeler, SPSS Clementine | quoted | Using market and competitor data for pricing | quoted | Using the combinatination of Traditional and ML techniques for Churn Prevention and Retention/Recovery, Fraud Detection, Judicial Claims Management, Credit and Behavior Scoring; | not quoted | -99 | quoted | Using market and competitor data to clustering pricing regions; | quoted | RFV for pre-paid telecommunication services | not quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | Ridge, LASSO/LARS Regressions | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Easy | Neutral | Complex | Easy | Easy | Very Complex | Complex | 5 | 30 | 30 | 10 | 5 | 15 | 5 | Difficulty defining the problem and directing the solution to it | -99 | -99 | Combining different data sources, processing difficulties (lack of space and capacity) | -99 | -99 | Data Cleaning, Analysis and a qualified Feature Engineering | -99 | -99 | Hyperparameter tuning | -99 | -99 | Compare different models and their performance metrics | -99 | -99 | Difficulty transcribing model codes for legacy/outdated systems | Many different environments to deploy | -99 | Combining the best way to monitor explanatory variables with possible business impacts | -99 | -99 | Prioritize the use of advanced techniques over best practices in bivariate analysis and feature engineering | -99 | -99 | Data Collection | Data Pre-Processing | Model Deployment | Rarely | 20 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Formal Documentation in Confluence for Models Validation and Audit (Internal/External) | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 90 | quoted | quoted | quoted | quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
492 | Completed (31) | 876 | Computer Science | Informatins | M.Sc in Informatics | PH.D in Informatics | -99 | -99 | Portugal | More than 2,000 employees | Data Scientist | -99 | 20 | 4 | 4 | 2 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Neutral | Neutral | Neutral | Complex | Neutral | Neutral | 30 | 10 | 30 | 15 | 5 | 5 | 5 | Meet customers expectatives | Clearness of business rules | Linking constraints | Retrieve data from different sources | -99 | -99 | Lack of information | Unbalanced data | -99 | Overfitting / Underfitting | Choice of different algorithms | -99 | Understanding which is the best indicative (recall, accuracy) | -99 | -99 | -99 | -99 | -99 | Baseline creation to compare the performance | -99 | -99 | -99 | -99 | -99 | Meet customers expectatives | Clearness of business rules | Lack of information | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | Design documents | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
495 | Completed (31) | 706 | Information Systems | -99 | M.Sc em Sistema de Informação | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 1 | 4 | 5 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | quantidade de falhas na linha de produção, quantidade de materiais a ser utilizado para fabricação de um produto, quantidade de vendas do produto no próximo mês | not quoted | -99 | quoted | quais falhas na linha de produção acontecem em conjunto | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Complex | Complex | Neutral | Easy | Easy | Neutral | 30 | 10 | 10 | 20 | 10 | 10 | 10 | Conversa com o cliente | -99 | -99 | Difícil acesso | -99 | -99 | Não estruturados | -99 | -99 | Linguagem de programação | -99 | -99 | Linguagem de programação | -99 | -99 | Linguagem de programação | -99 | -99 | Linguagem de programação | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Rarely | 60 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
498 | Completed (31) | 714 | Information Systems | Machine Learning | -99 | -99 | Deep Learning specialization on Coursera; NLP and DS Nanodegrees on Udacity | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 4 | 5 | 5 | 2 | 6-10 members | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Content Moderation | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | Product Recommendation | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Complex | Neutral | Neutral | Neutral | Complex | Very Complex | Very Complex | 20 | 20 | 25 | 10 | 10 | 10 | 5 | Business expectation | -99 | -99 | Spread data sources | -99 | -99 | Data volume | Data variety | -99 | Performance | -99 | -99 | Relevant metrics | -99 | -99 | Scalability | -99 | -99 | Tools | Infrastructure | -99 | -99 | -99 | -99 | Business expectation | Spread data sources | Relevant metrics | Sometimes | 60 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | quoted | Time-based retraining | not quoted | 10 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
504 | Completed (31) | 810 | Computer Science | -99 | -99 | -99 | -99 | -99 | Brazil | 1-10 employees | Other, which one? | Student | 3 | 1 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Accessibility | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | C# | not quoted | -99 | quoted | Classifying hand gestures to identify the Libras' alphabet | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Complex | Very Complex | Complex | Neutral | Neutral | Neutral | Easy | 10 | 30 | 20 | 20 | 10 | 5 | 5 | Defining the requirements | -99 | -99 | Modeling the data | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | I don't know | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Me, student and developer | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://machine-learning-survey.web.app/ |
505 | Completed (31) | 1,349 | Electrical and Electronics Engineering | -99 | Complex Systems | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 4 | 3 | 8 | 2 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Neutral | Complex | Complex | Complex | Very Complex | Very Complex | Neutral | 15 | 20 | 20 | 15 | 10 | 15 | 5 | abrupt changes in project scope | Lack of alignment and convergence in project expectations | Understanding the business context | Data from various sources | Data in different formats | Data availability | Null values | Outliers | Biaeses | Which model to use | Benchmark between models | -99 | Which metric use | Hyperparameters tuning | -99 | API use | Correct versioning | Infrastructure | Negative feedback | Deploying new versions | -99 | Documentation | -99 | -99 | Abrupt changes in project scope | Which model to use | Which metric use | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | quoted | none | No | -99 | https://machine-learning-survey.web.app/ |
508 | Completed (31) | 1,725 | Management and Industrial Engineering | Post Grad in Data Science and Business Analytics | Management and Strategy | -99 | -99 | -99 | Portugal | More than 2,000 employees | Other, which one? | Data Analyst | 1 | 1 | 1 | 0 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | internal expenses classification (ex: travel, real estate, insurance, technology) | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Complex | Complex | Very Complex | Complex | Very Complex | Complex | 10 | 20 | 15 | 30 | 10 | 15 | 0 | Difficulty to simplify | -99 | -99 | Lack of data quality | -99 | -99 | -99 | -99 | -99 | Low knowledge of actual advantages and disadvantages of each model | Training time | -99 | Very godd results or very bad results | -99 | -99 | -99 | -99 | -99 | Few indicators | -99 | -99 | -99 | -99 | -99 | Lack of data quality | Low knowledge of actual advantages and disadvantages of each model | Few indicators | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
511 | Completed (31) | 1,072 | Robotics Engineering | Autonomous Vehicle Navigation | Information Engineering | -99 | -99 | -99 | Brazil | 251-500 employees | Data Scientist | -99 | 5 | 1 | 3 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | Agriculture | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | CNN | Extremely Relevant | High Relevance | Extremely Relevant | Neutral | High Relevance | High Relevance | High Relevance | Complex | Neutral | Easy | Neutral | Complex | Complex | Complex | 20 | 10 | 15 | 15 | 20 | 10 | 10 | classification vs regression | -99 | -99 | timestamp, noise, missing data | -99 | -99 | pre-processing cost | -99 | -99 | which model | -99 | -99 | which metrics | -99 | -99 | deployment costs | -99 | -99 | not sure | -99 | -99 | -99 | -99 | -99 | poor data representation | incorrect problem framing | -99 | I don't know | 50 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | software engineer | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Confluence docs | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | i am not sure | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
512 | Completed (31) | 1,191 | Statistics | Data Science specialization | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Other, which one? | Data Manager | 0 | 20 | 100 | 10 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Newspaper sale | quoted | Credit Score, Behaviour Score, Prediction Sale | quoted | cross sales | quoted | Geograph Segmentation, Behaviour Segmentation | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Complex | Complex | Complex | Complex | Complex | 20 | 30 | 10 | 10 | 20 | 10 | 0 | understand | -99 | -99 | where is data | put togheter | its is ok | -99 | -99 | -99 | find the best | -99 | -99 | -99 | -99 | -99 | put in file not priority | -99 | -99 | infra | -99 | -99 | -99 | -99 | -99 | bad understanding | buid a good dataset | not priority to deploy | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | quoted | quoted | not quoted | -99 | Yes, Please, specify | H2O, DataRobot | https://machine-learning-survey.web.app/ |
515 | Completed (31) | 572 | -99 | I have specialization in Big Data | I haven't | I haven't | I haven't | I haven't | Brazil | More than 2,000 employees | Data Scientist | -99 | 1 | 1 | 1 | 4 | 50+ members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Classification in sales and net financials | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | 10 | 25 | 10 | 10 | 5 | 20 | 20 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 75 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 40 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
516 | Completed (31) | 1,138 | Statistics | Data Science specialization | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Business Analyst | -99 | 3 | 6 | 5 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | performance metric prediction applied to logistics | quoted | sales website recommendation algorithm | not quoted | -99 | quoted | grouping of logistics agencies to understand performance and profiles | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Neutral | Neutral | Very Complex | Neutral | Complex | Neutral | 20 | 15 | 25 | 20 | 5 | 10 | 5 | communication | -99 | -99 | security | -99 | -99 | dirty data | -99 | -99 | choice of model | -99 | -99 | unbalanced classes | choice of Evaluation | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | communication | security | dirty data | Sometimes | 60 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | 20 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://machine-learning-survey.web.app/ |
517 | Completed (31) | 4,083 | Mechatronics Engineering | -99 | M.Sc. in Theoretical Physics | Ph.D in Theoretical Physics | Machine Learning on Coursera | -99 | Brazil | More than 2,000 employees | Business Analyst | -99 | 3 | 3 | 6 | 3 | 6-10 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | scala | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | quoted | anomaly detection | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | Isolation Forest | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Complex | Very Complex | Complex | Very Complex | Very Complex | Neutral | 5 | 5 | 60 | 5 | 10 | 10 | 5 | Expectations alignment | business understanding | business metric evaluation | data inconsistency | data integration | data leakage | missing data | outliers | feature engineering | unbalanced dataset | model selection | feature selection | evaluation metric | hyperparameter tuning | model interpretation | library versioning | real time solutions | well defined pipeline | features drifting | -99 | -99 | -99 | -99 | -99 | evaluation metric | business metric evaluation | data leakage | Frequently | 50 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Confluence pages | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | quoted | quoted | quoted | quoted | quoted | features drifting | Yes, Please, specify | Azure ml | https://machine-learning-survey.web.app/ |
521 | Completed (31) | 1,036 | Civil Engineering | Data Science | Management information systems/ Computer Engineering | -99 | IBM AI Professional Certificate, Coursera Python for Everybody's Specialization, DeeplearingAI Coursera | -99 | Turkey | 1,001-2,000 employees | Data Scientist | -99 | 3 | 3 | 10 | 5 | 1-5 members | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | r&d | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | NLP | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | Neutral | 40 | 30 | 10 | 10 | 3 | 4 | 3 | aim | scope | method | broken data | platforms | pipelines | data flow | hardware | pipelines | algorithms | performance | metrics | metrics | performance | algorithms | java-python integration | -99 | -99 | domain expertise | hardware | -99 | -99 | -99 | -99 | business understanding | data flow | methods | Frequently | 50 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | Yes we are using an automated ML tool | not quoted | 30 | quoted | quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Our in house developed AutoML tool | https://machine-learning-survey.web.app/ |
523 | Completed (31) | 1,872 | Electrical and Electronics Engineering | -99 | M.Sc. in Engineering | Ph.D. in Computer Science | Coursera/edX courses on ML and data science | -99 | Brazil | More than 2,000 employees | Developer | -99 | 3 | 6 | 2 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Neutral | High Relevance | High Relevance | High Relevance | Very Complex | Complex | Neutral | Easy | Complex | Very Complex | Very Complex | 15 | 10 | 5 | 25 | 10 | 25 | 10 | Unclear functional requirements and target success metrics | Poor understanding of the risks and benefits trade-offs | -99 | Getting frictionless access to many data sources | Input pipelines not properly prepared for handling massive data consumption | -99 | ETL systems implemented in an ad-hoc | Basic support from data teams | -99 | Not enough expertise in the company | Lack of professional tools for tracking experiments | Costs associated with cloud computing and storage | Unclear evaluation protocol and metrics | Lack of a culture of thorough evaluation before rollouts | -99 | Lack of automated and easy-to-use deployment pipelines | Not enough expertise in the company | -99 | Not enough expertise in the company | -99 | -99 | Getting the necessary support and sponsorship from upper management | Unrealistic or distorted expectations from upper management | -99 | Unclear functional requirements and target success metrics | Unrealistic or distorted expectations from upper management | Lack of automated and easy-to-use training and deployment pipelines | Frequently | 70 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | Design docs | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
529 | Completed (31) | 2,533 | Computer Engineering | -99 | -99 | -99 | IA HCIA huawei, ML HCIA huawei, ML HCIA huawei nv2 | data science udemy | Brazil | More than 2,000 employees | Other, which one? | data analyst | 0 | 1 | 1 | 1 | 20-50 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | Complex | Neutral | Complex | Easy | Very Complex | Complex | Easy | 20 | 5 | 20 | 10 | 20 | 10 | 15 | -99 | -99 | -99 | malformed data | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | discover best validation metric | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | malformed data | discover best validation metric | Never | 40 | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 95 | not quoted | quoted | quoted | quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
533 | Completed (31) | 1,299 | -99 | -99 | Pontifícia Universidade Católica | -99 | -99 | -99 | Brazil | 1,001-2,000 employees | Data Scientist | -99 | 1 | 0 | 0 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | TMVA - Toolkit for Multivariate Data Analysis | I don't know | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Science | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | To estimate the best values for minimazing background events number of a decay | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | Extremely Relevant | Neutral | High Relevance | I don't know | I don't know | Neutral | Complex | Very Complex | Very Complex | Complex | Neutral | Neutral | 5 | 10 | 30 | 30 | 10 | 10 | 5 | Client needs | Strategy | -99 | Engineering complexity | -99 | -99 | the correct pre-cuts | Gathering all samples | -99 | The best model | -99 | -99 | If everything went right | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | the correct pre-cuts | If everything went right | Engineering complexity | I don't know | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | My master's program mentor | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | I don't know | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | I don't know | not quoted | -99 | quoted | -77 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
536 | Completed (31) | 1,155 | Computer Engineering | -99 | M.Sc. in Computer Science | -99 | -99 | -99 | Brazil | 251-500 employees | Developer | -99 | 1 | 1 | 1 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | Clustering packages to reduce their size input and provide a smaller one to the mathematical model | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | High Relevance | High Relevance | Extremely Relevant | Neutral | High Relevance | Neutral | Complex | Complex | Complex | Easy | Neutral | Neutral | 30 | 10 | 15 | 15 | 20 | 5 | 5 | The problem is not well defined | -99 | -99 | Lack of data | Unstructured data | -99 | -99 | -99 | -99 | Deciding which ML method is going to be used | -99 | -99 | Defining a metric if the existing ones do not capture what you are measuring | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | The problem is not well defined. | Lack of data | Defining a metric if the existing ones do not capture what you are measuring | Frequently | 70 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Creating a document in Confluence. | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
538 | Completed (31) | 2,248 | Information Systems | not applied | M.Sc. in Electrical Engineering at Federal University of Minas Gerais (UFMG) | Ph. D. in Electrical Engineering at Federal University of Minas Gerais (UFMG) | not applied | -99 | Brazil | More than 2,000 employees | Developer | -99 | 2 | 1 | 0 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Complex | Complex | I don't know | Easy | Easy | I don't know | Neutral | 10 | 10 | 20 | 20 | 20 | 10 | 10 | Problem should be properly explained in order to be developed | -99 | -99 | Achieve data for the modeling | -99 | -99 | Handling attributes and sample selection for the model | Missing attributes | -99 | Setting parameters | Overfitting | -99 | Errors handling | Model redeveloping | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Problem should be properly explained | Errors handling | Achieve data for the modeling | Sometimes | 55 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
541 | Completed (31) | 655 | Electrical and Electronics Engineering | Signal Processing | M.Sc. in Electrical and Electronics Engineering | Ph.D. in Electrical and Electronics Engineering | Coursera Deep Learning Specialization | -99 | Turkey | 11-50 employees | Project Lead / Project Manager | -99 | 10 | 5 | 4 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Predicting company's customer using classification to identify premium or not | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | Detecting possible fradulent phone calls performed by employees by using the silence information in call center recordings. | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Extremely Relevant | Complex | Very Complex | Neutral | Complex | Easy | Neutral | Easy | 15 | 40 | 10 | 15 | 5 | 10 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | 65 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://machine-learning-survey.web.app/ |
543 | Completed (31) | 893 | Mechanical Engineering | Mechanical Dynamics | M.Sc. in Computer Science | Ph.D. in Information Technologies | Coursera | Udemy | Turkey | More than 2,000 employees | Data Scientist | -99 | 18 | 5 | 3 | 3 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Easy | Easy | Very Complex | Complex | Complex | Neutral | 10 | 10 | 20 | 30 | 15 | 10 | 5 | conflicting requirements | -99 | -99 | if data is not available this step takes extremely long time | -99 | -99 | preprocessing combinations shall be performed well | -99 | -99 | developing a better model | hyperparameter optimization | -99 | evaluation metrics shall be determined well | -99 | -99 | if deployed to other departments, model shall be well documented | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | developing a better model | hyperparameter optimization | preprocessing combinations shall be performed well | Rarely | 50 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
546 | Completed (31) | 1,293 | Data Base | MBA - Gestão de Projetos | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Other, which one? | Especialista em Prevenção a Fraudes | 2 | 1 | 1 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Fraud prevention | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Complex | Neutral | Complex | Complex | Complex | Complex | Complex | 30 | 10 | 10 | 20 | 10 | 10 | 10 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 40 | not quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
29 | Completed after break (32) | -1 | Statistics | Data Science specialization | -99 | -99 | -99 | -99 | Brazil | 501-1,000 employees | Data Scientist | -99 | 26 | 1 | 2 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Government | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | High Relevance | High Relevance | Very Complex | Complex | Very Complex | Complex | Complex | Neutral | Complex | 25 | 15 | 15 | 20 | 10 | 5 | 10 | User Response | User Language | User Understanding | what are the data | where is the data | What are the meanings of data | what formats | What kind of data standardization | How to reduce dimensionality | Which algorithms best fit | Which hyperparameters | What are the numbers of Training, Validation and Testing | What performance indicators | What is the baseline for comparison? | -99 | How to couple the model call to the transactional system | -99 | -99 | What is the periodicity of evaluation | What is the acceptable error limit? | -99 | -99 | -99 | -99 | User Understanding | User Response | Which algorithms best fit | Sometimes | 40 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
80 | Completed after break (32) | -1 | Computer Science | -99 | MSC Computer Science | phd in Technical Sciences | Data Science Specialications (Stanford, Harvard, Datacamp) | Teaching Data Science for 3 Years | Austria | 51-250 employees | Data Scientist | -99 | 10 | 5 | 4 | 3 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | predict and extract information from images | quoted | classify network traffic | not quoted | -99 | not quoted | -99 | quoted | find assocations together with patterns | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Very Complex | Neutral | Neutral | Easy | Complex | Complex | Complex | 15 | 15 | 20 | 10 | 15 | 15 | 10 | Available Time/Ressources compared to Target | -99 | -99 | Precollected, unflexible Datacollection | -99 | -99 | Data Quality, | Governance | Accessibility | Compatibility | Compute Ressources | -99 | Metrics and Target dissonance | Definition of done | -99 | Technical Framework | API Standards | -99 | Continuity | Failure Detection | -99 | -99 | -99 | -99 | Human Misunderstanding | Technical Debt | -99 | Frequently | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | -99 | quoted | Continous Integration and Monitoring | not quoted | 20 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
102 | Completed after break (32) | -1 | Computer Science | none | Master in Software Engineering | PhD in Computing Science and Telecomunications | none | -99 | Poland | More than 2,000 employees | Project Lead / Project Manager | -99 | 17 | 2 | 5 | 4 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Very Complex | Neutral | Neutral | Very Complex | Very Complex | Neutral | Complex | 17 | 10 | 10 | 25 | 25 | 7 | 6 | Elicit problem from custmer | Understand the domain | -99 | NDA | Contract negotiation | -99 | Understand the data schema | -99 | -99 | Understand what criteria (factors) are | Select the algorithm | -99 | Choosing measures | -99 | -99 | Technical issues connected with using the infracturure | New environement buying or development | -99 | -99 | -99 | -99 | -99 | -99 | -99 | NDA and Contract | Understand the data schema | Understand what criteria (factors) are | I don't know | 30 | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | quoted | analysis of artifacts | not quoted | not quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Explaining to customer that ML-based system will not be a perfect box that will just take their data and predict everything | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 20 | not quoted | quoted | not quoted | quoted | not quoted | -99 | No | -99 | https://t.co/ |
152 | Completed after break (32) | -1 | Computer Science | -99 | Computer Science | Computer Science | -99 | -99 | Brazil | 51-250 employees | Project Lead / Project Manager | -99 | 10 | 5 | 9 | 5 | 1-5 members | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | Law, Agribusiness | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Neutral | Neutral | Complex | Complex | Complex | Complex | 20 | 10 | 40 | 10 | 10 | 5 | 5 | Disponibilidade para conversar com a pessoa que entende do domínio | Alinhar expectativa do que será entregue no final | -99 | Encontrar dados representativos | Encontrar dados estruturados | -99 | Estruturar dados | Limpeza dos dados | -99 | Escolher o modelo adequado para o problema | Interpretar o aprendizado do modelo | -99 | Atingir uma acurácia adequada | Validar o resultado com o cliente | -99 | Disponibilizar no servidor ou no serviço de nuvem | -99 | -99 | Configurar ferramentas para esse fim | Estabelecer threshehold de normalidade | -99 | -99 | -99 | -99 | Encontrar dados representativos | Limpeza dos dados | Escolher o modelo adequado para o problema | Sometimes | 50 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
163 | Completed after break (32) | -1 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | Neutral | Very Complex | Very Complex | Neutral | Complex | Neutral | Complex | 5 | 15 | 25 | 15 | 10 | 15 | 15 | Inaccurate explanation of the problem | Low interaction between those who actually face the problem and those who will solve the problem | -99 | Quality of the data (Lot of problems, NaN, non numerical values, Features with very few data, etc) - Data Cleansing | Small dataset after filtering problems in data | -99 | Defining criterion for data filling without proper knowledge of the process | -99 | Overly confidence on AutoML frameworks | Poor Tuning of Hyperparameters | -99 | Inaccurate understanding of the goal of the model | Defining only one KPI for evaluation | -99 | Negligence of problems in real-time data | -99 | -99 | Not tuning a Model Monitoring framework correctly | Perceiving drifts in data as a reason for constant retraining of the models, and not as a reason to look deeper into a model's performance. Drift Detection should only help decision making and not guide them | -99 | -99 | -99 | -99 | Quality of the data (Lot of problems, NaN, non numerical values, Features with very few data, etc) - Data Cleansing | Low interaction between those who actually face the problem and those who will solve the problem | -99 | Sometimes | 20 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 20 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Yes, Please, specify | AutoML | -99 | |
169 | Completed after break (32) | -1 | Computer Engineering | Project Management | -99 | -99 | -99 | -99 | Turkey | 11-50 employees | Project Lead / Project Manager | -99 | 18 | 2 | 3 | 1 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Very Complex | Very Complex | Neutral | Complex | Neutral | Neutral | 10 | 20 | 25 | 20 | 15 | 5 | 5 | defining success metrics | defining milestones and planning a timeline | -99 | data samples are unavailable | data collected from multiple sources is heterogeneous | -99 | Data inconsistency | Existence of manipulated data | -99 | Lack of domain expertise | Issues with annotation | -99 | -99 | -99 | -99 | Choosing the right production requirements | Navigating organizational structure for machine learning operations | -99 | changes in data and relationships between input and output variables. | -99 | -99 | -99 | -99 | -99 | defining success metrics | data samples are unavailable | data samples are unavailable | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
209 | Completed after break (32) | -1 | Computer Science | Business Data Analysis | -99 | -99 | -99 | -99 | Jordan | More than 2,000 employees | Project Lead / Project Manager | -99 | 0 | 0 | 3 | 3 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Identify user errors during data entries | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Neutral | Neutral | High Relevance | Neutral | High Relevance | Extremely Relevant | High Relevance | Complex | Neutral | Neutral | Neutral | Complex | Neutral | Neutral | 15 | 25 | 10 | 15 | 10 | 20 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Sometimes | 20 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
245 | Completed after break (32) | -1 | Computer Science | -99 | -99 | -99 | -99 | -99 | Italy | 11-50 employees | Project Lead / Project Manager | -99 | 11 | 5 | 5 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Brand reputation and virtual assistant | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | Neutral | Neutral | Low Relevance | Complex | Complex | Complex | Neutral | Complex | Easy | Easy | 25 | 15 | 20 | 20 | 10 | 5 | 5 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | I don't know | 50 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
273 | Completed after break (32) | -1 | Industrial Engineering | Operations Research | Industrial Engineering | Computational Science | Udacity Data Scientist | -99 | Turkey | 1-10 employees | Other, which one? | Founder | 15 | 19 | 30 | 15 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Energy - Electricity Generation | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Scala | quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Very Complex | Neutral | Neutral | Complex | Neutral | Neutral | Neutral | 10 | 20 | 35 | 20 | 5 | 5 | 5 | Insufficent or flawed problem formulation | Irrelevant business objectives | Limited domain knowledge | Limited data availibility | Limited data accesibility | -99 | High degree of need for cleansing | Poor understanding and knowledge of data | -99 | Selection of appropriate modeling techniques | Requirement of deep understanding of techniques | -99 | Defining evaluation criteria where accuracy, hit rate recall rate etc. are not enough.. | -99 | -99 | Integration requirements with other systems | -99 | -99 | Poor data and model governance | -99 | -99 | -99 | -99 | -99 | Insufficient business understanding and problem formulation | Inappropriate or poor model building and development | Bad integration and deployment | Frequently | 30 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 80 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
323 | Completed after break (32) | -1 | Chip designer | SW Architectures | -99 | -99 | ADSCAMP | -99 | Hungary | More than 2,000 employees | Other, which one? | SW Architect | 24 | 6 | 4 | 4 | 6-10 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | Trends | quoted | Anomalies | not quoted | -99 | quoted | User/network behavior | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Very Complex | Neutral | Neutral | Neutral | Neutral | Easy | 20 | 30 | 10 | 20 | 10 | 5 | 5 | not well specified | hypothesis is not valid | -99 | no proper data available | data is too noisy, or missing | -99 | scaling issues on big data | -99 | -99 | time to test different solutions | -99 | -99 | proper data set | -99 | -99 | -99 | -99 | -99 | access to metrics | -99 | -99 | -99 | -99 | -99 | missing proper data | lack of specifications | invalid hypothesis | Always | 38 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | MXE | not quoted | 70 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
329 | Completed after break (32) | -1 | Computer Science | Machine Learning,NLP | -99 | -99 | -99 | -99 | Turkey | 1-10 employees | Developer | -99 | 4 | 1 | 1 | 20 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | C# | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | LUIS.AI from Microsoft Cognitive Services was used (NLP) | Neutral | High Relevance | 0 | Extremely Relevant | High Relevance | Low Relevance | Neutral | Neutral | Neutral | Complex | Very Complex | Neutral | Very Easy | Very Easy | 12 | 13 | 20 | 30 | 15 | 5 | 5 | changing demands | - | - | low number of people to enter data | - | - | - | - | - | time spent understanding the system | - | - | more data entry | - | - | integration with skype business | - | - | - | - | - | - | - | - | employee who will use it get used to talking with chatbot | - | - | Sometimes | 75 | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | by submitting my undergraduate thesis to the institution | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | using LUIS.AI api | not quoted | 45 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | LUIS.AI | -99 |
344 | Completed after break (32) | -1 | Mathematics | Numerical Simulation & Non-Linear Optimization | Applied Mathematics | -99 | -99 | -99 | Austria | 1-10 employees | Other, which one? | Managing Director & Team Lead Data Science | 1 | 1 | 3 | 1 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | High Relevance | High Relevance | High Relevance | Very Complex | Complex | Complex | Complex | Neutral | Complex | Neutral | 20 | 30 | 20 | 10 | 5 | 10 | 5 | -99 | -99 | x | x | -99 | -99 | -99 | x | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Schlechte Datenqualität | Ungenaue Requirements | Deployment | Frequently | 40 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
353 | Completed after break (32) | -1 | Computer Engineering | -99 | MSc in E-commerce and Internet Computing | -99 | -99 | -99 | Hong Kong | 251-500 employees | Project Lead / Project Manager | -99 | 10 | 5 | 6 | 3 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Very Complex | Very Complex | Neutral | Complex | Neutral | Complex | 20 | 30 | 15 | 10 | 10 | 15 | 0 | Manage client expectation on we what ML can do | -99 | -99 | Select the right data source | Process with non digitalize data | Consolidate data from difference source | Data formating | -99 | -99 | Select the right model | Computation power resources limitation | -99 | Select the right matrics | Select the right validation data set | -99 | Deployment with legacy system | UAT accpetance with client | -99 | Performance drop after certain period | -99 | -99 | -99 | -99 | -99 | Manage client expectation on we what ML can do | Process with non digitalize data | UAT accpetance with client | Sometimes | 70 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 50 | quoted | quoted | quoted | quoted | not quoted | -99 | No | -99 | -99 |
357 | Completed after break (32) | -1 | Computer Science | Software Engineering | M.Sc. in Knowledge Extraction | -99 | -99 | -99 | Bulgaria | 51-250 employees | Solution Architect | -99 | 14 | 7 | 5 | 4 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Advertising | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | High Relevance | Low Relevance | High Relevance | Complex | Neutral | Neutral | Neutral | Complex | Neutral | Neutral | 15 | 5 | 20 | 20 | 20 | 10 | 10 | communication barrier | -99 | -99 | privacy policies | -99 | -99 | -99 | -99 | -99 | plugging custom logic into the training framework | -99 | -99 | choosing the right metrics to focus on | -99 | -99 | response latency | -99 | -99 | understandability (esp. for black box models) | -99 | -99 | -99 | -99 | -99 | communication barrier when extracting requirements | not having enough or the right data collected | properly evaluating the models | Sometimes | 50 | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | quoted | We have pipelines to regularly retrain,update and deploy our models in production, taking the latest released code. In this process we store the output models alongside a snapshot of their training data for explainability reasons. | not quoted | 75 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
413 | Completed after break (32) | -1 | Nuclear Engineering | Mathematical modeling, optimization, machine learning | M.Sc. in Mechanical Engineering | Ph.D. in Mechanical Engineering | -99 | -99 | Turkey | 1,001-2,000 employees | Project Lead / Project Manager | -99 | 255 | 4 | 10 | 2 | 11-20 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | Energy | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | recommendation (treatment, portfolio) | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | High Relevance | High Relevance | Very Complex | Very Complex | Complex | Complex | Complex | Very Complex | Complex | 25 | 20 | 15 | 15 | 10 | 10 | 5 | Lack of domain knowledge | Unclear criteria for success | -99 | Insufficient training data | Non-representative training data | Privacy and legal issues | Weak semantics of data | Poor data quality | -99 | Identifying best algorithm | Overfitting and underfitting | -99 | Identifying correct metrics | -99 | -99 | Integration with existing systems | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Insufficient training data | Lack of domain knowledge | Overfitting and underfitting | Sometimes | 20 | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 25 | quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
418 | Completed after break (32) | -1 | Computer Science | Software Engineering | M.Sc. in Computer Science | -99 | Data Scientist Basic | -99 | Germany | 51-250 employees | Project Lead / Project Manager | -99 | 5 | 3 | 3 | 0 | 1-5 members | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | agile style (iterative, regular meetings,...) - but not fully Scrum | Mostly agile | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Production | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Neutral | I don't know | High Relevance | I don't know | Neutral | Neutral | Complex | Complex | Neutral | Very Complex | I don't know | 12 | 8 | 35 | 20 | 5 | 18 | 2 | Convincing people of the need of doing right | Involvement of the right persons | -99 | too less data | bad data quality | no (technical) experience | Bad data quality | Misleading understanding | Too many people working at the same thing (or have different opinions) | -99 | -99 | -99 | -99 | -99 | -99 | Runtime requirements | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Bad data quality | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | -99 |
439 | Completed after break (32) | -1 | Physics | -99 | Game Design | -99 | -99 | -99 | Turkey | 11-50 employees | Developer | -99 | 4 | 3 | 2 | 1 | 6-10 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | Advertisement | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Very Complex | Easy | Neutral | Neutral | Complex | Neutral | Easy | 20 | 30 | 10 | 10 | 20 | 5 | 5 | Defining problem properly | -99 | -99 | Collecting necessary data consistently | -99 | -99 | Filling missing data | -99 | -99 | Finding correct model | -99 | -99 | Hyperparameter optimization | -99 | -99 | Implementing model without damaging working system | -99 | -99 | Comparison | -99 | -99 | -99 | -99 | -99 | Defining problem properly | Finding correct model | Implementing model without damaging working system | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
451 | Completed after break (32) | -1 | Industrial Engineering | -99 | M.Sc. in Operational Research | D.Sc. in Operational Research | -99 | -99 | Brazil | More than 2,000 employees | Other, which one? | Operational Research Analyst | 16 | 16 | 32 | 30 | 1-5 members | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | Systems Simulation | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Optimisation Metaheuristics, Reinforcement Learning | Extremely Relevant | Extremely Relevant | I don't know | Extremely Relevant | I don't know | Extremely Relevant | I don't know | Complex | Complex | I don't know | Neutral | I don't know | Complex | I don't know | 0 | 40 | 0 | 15 | 15 | 30 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Frequently | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Operational Research Analyst | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Mathematical Models | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | Technical Reports | not quoted | -99 | quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://statics.teams.cdn.office.net/ |
449 | Completed after break (32) | -1 | Computer Science | Systems Analysis | Data Science | -99 | Udacity Deep Learning Nanodegree, Udacity Data Engineer Nanodegree, Coursera Deep Learning Specialization, Azure Data Scientist Certification, Azure AI Engineer Certification | -99 | Brazil | More than 2,000 employees | Project Lead / Project Manager | -99 | 18 | 3 | 10 | 2 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | High Relevance | Complex | Very Complex | Easy | Complex | Neutral | Neutral | Neutral | 15 | 35 | 10 | 20 | 5 | 10 | 5 | Specific Business Scenarios can be complex | Lack of similar problems in the academia and the market in general | -99 | Annotated data | Data Availability | Data in Volume Enough | Unstructured data | Unlabeled data | Data Volume | Algorithm complexion | Data Volume | Hardware limitation | Problem, data and model interpretability | Model size | -99 | Infrastructure available | Tipically, data scientists don't know or want to do that | -99 | Lack of tools ready for monitoring | Need to implement from scratch | -99 | -99 | -99 | -99 | Data Availability | Annotated data | Hardware limitation | Frequently | 50 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 10 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Only to establish a baseline | -99 |
468 | Completed after break (32) | -1 | -99 | -99 | -99 | -99 | -99 | Electronic engineering CEFET-RJ | Portugal | More than 2,000 employees | Developer | -99 | 2 | 0 | 1 | 0 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | Categorizing skin cancer images | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | Neutral | Neutral | Neutral | Easy | Complex | Neutral | Complex | Neutral | Easy | 15 | 5 | 30 | 20 | 15 | 5 | 10 | Complex scenarios | -99 | -99 | Many data sources | -99 | -99 | Normalizing the data | cleaning data | -99 | Finding best approach/algorithm | -99 | -99 | Analyse core parameters | -99 | -99 | Where/ how to deploy | -99 | -99 | Build the tool | -99 | -99 | -99 | -99 | -99 | Cleaning data | Normalizing the data | Analyse core parameters | I don't know | 60 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
480 | Completed after break (32) | -1 | Computer Engineering | Data Science Bootcamp / Udacity MLE Nanodegree | -99 | -99 | -99 | I dropped Master twice | Germany | More than 2,000 employees | Developer | -99 | 11 | 1 | 2 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | sql | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | ??? Filtering | High Relevance | High Relevance | Extremely Relevant | Neutral | Extremely Relevant | Neutral | High Relevance | Very Complex | Easy | Complex | Neutral | Very Complex | Neutral | Complex | 30 | 5 | 30 | 10 | 15 | 5 | 5 | Ill defined problem | Unclear goal | -99 | Source access | Data format | -99 | Dirty data | missing data | -99 | -99 | -99 | -99 | Choosing correct metric | -99 | -99 | -99 | -99 | -99 | Metric | -99 | -99 | Updating model | -99 | -99 | Ill defined problem | Choosing correct metric | Unclear goal | I don't know | 100 | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
494 | Completed after break (32) | -1 | Computer Science | -99 | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 2 | 2 | 1 | 0 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Politics | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | Extremely Relevant | High Relevance | High Relevance | Complex | Complex | Neutral | Complex | Very Complex | Very Complex | Very Complex | 20 | 15 | 10 | 15 | 20 | 10 | 10 | What's the scope of the problem? | What's the objective of the task | -99 | Where is the data? | Is it already structured? | -99 | How the data needs to look like so we can use it to solve the problem defined? | -99 | -99 | Which approach are we gonna use to solve our problem? | Which architeture are gonna be used to build the model | -99 | Which metrics are we monitoring? | What are the tresholds that will be considered for the metrics choosen | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | What's the scope of the problem? | Which approach are we gonna use to solve our problem? | What are the thresholds that will be considered for the metrics chosen | Frequently | 70 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 75 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
496 | Completed after break (32) | -1 | Information Engineering | -99 | Information Engineering | -99 | -99 | -99 | Brazil | More than 2,000 employees | Business Analyst | -99 | 1 | 1 | 2 | 1 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Easy | Neutral | Complex | Complex | Complex | Neutral | 30 | 10 | 10 | 20 | 10 | 10 | 10 | Correct understanding of the problem | Setting the correct parameters | -99 | Unorganized data | -99 | -99 | Data is not clean and structured | -99 | -99 | Model definition and training | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Correct understanding of the problem | Setting the correct parameters | Model definition and training | Sometimes | 40 | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
527 | Completed after break (32) | -1 | Information Systems | MBA EM BIG DATA (DATA SCIENCE) | -99 | -99 | -99 | -99 | Brazil | 1,001-2,000 employees | Other, which one? | Analista de Segurança | 3 | 1 | 7 | 3 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | Python | quoted | Clientes fraudadores | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Complex | Complex | Complex | Complex | Complex | Neutral | Neutral | 10 | 20 | 30 | 15 | 10 | 10 | 5 | Combinar dados para variável respostas | ok | ok | Descrição das varáveis | Variáveis históricas | ok | Limitações de processamento real-time | veracidade dos dados ao longo do tempo | ok | qual o período ideal para a amostragem | ok | ok | Dificuldade quando precisa de um tempo para maturar a variável resposta | ok | ok | Produtizar a operacionalização | ok | ok | ok | ok | ok | ok | ok | ok | Limitações de processamento real-time | veracidade dos dados ao longo do tempo | Produtizar a operacionalização | Frequently | 80 | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | Analistas de segurança | not quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 70 | not quoted | not quoted | quoted | quoted | not quoted | -99 | No | -99 | https://machine-learning-survey.web.app/ |
109 | Suspended (22) | -1 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://l.facebook.com/ |
124 | Suspended (22) | 25 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Never | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | https://l.facebook.com/ |
30 | Suspended (22) | 0 | Computer Science | Data Science | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Developer | -99 | 15 | 0 | 0 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -99 |
32 | Suspended (22) | -1 | Electrical and Electronics Engineering | Data Science specialization | M.Sc. in Control Engineering | -99 | -99 | MBA | Brazil | 1,001-2,000 employees | Other, which one? | Technology Advisor | 10 | 1 | 1 | 1 | 1-5 members | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | I don't know | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -99 |
54 | Suspended (22) | 0 | Computer Science | Data engineering | - | - | - | - | Brazil | More than 2,000 employees | Other, which one? | Data Engineer | 12 | 0 | 1 | -99 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -99 |
35 | Suspended (22) | 0 | Computer Science | -99 | M.Sc. in Computer Science | Ph.D. in Computer Science | -99 | -99 | Brazil | 1,001-2,000 employees | Data Scientist | -99 | 0 | 15 | 100 | 70 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | I don't know | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | Risk rating | quoted | Profile matching | quoted | Recommendation | quoted | Client wallet segmentation | quoted | Anomaly detection to fraud prevention | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | android-app://com.linkedin.android/ |
37 | Suspended (22) | 0 | -99 | -99 | -99 | -99 | -99 | -99 | 0 | 0 | 0 | -99 | -99 | -99 | -99 | -99 | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Totally traditional | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | https://t.co/UwNVKmgHWq |
40 | Suspended (22) | 0 | Information Systems | -99 | Computer Science | Computer Science | Data Science | -99 | Brazil | 51-250 employees | Data Scientist | -99 | 7 | 6 | 15 | 12 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | Recruiting | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | delivery time, battery remaining time, space availability, covid cases, ... | quoted | product category, healthcare plan payment, document type, exame type, ... | quoted | smart home appliances usage | quoted | patient clustering | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | https://www.linkedin.com/ |
44 | Suspended (22) | 0 | Computer Science | -99 | Master in Computer Science² | -99 | -99 | -99 | Canada | 51-250 employees | Other, which one? | Data Analyst | 5 | 1 | 1 | 0 | 1-5 members | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Mostly agile | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Hierarchical clustering of time series | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | android-app://com.linkedin.android/ |
59 | Suspended (22) | 0 | Information Systems | -99 | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 5 | 1 | 1 | 0 | 1-5 members | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -99 |
52 | Suspended (22) | 193 | Statistics | -99 | -99 | -99 | -99 | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 5 | 2 | 6 | 3 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Totally traditional | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Manufacturing | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | -99 | quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | not quoted | quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Complex | Neutral | Easy | Easy | Neutral | Neutral | 10 | 15 | 30 | 15 | 20 | 8 | 2 | End to end communication | -99 | -99 | Carelessness | Analytical Irrelevance | Lack of standartization | Underappreciation | Hastiness | -99 | That one guy that goes like we need a neural network to every single problem | -99 | -99 | The balance between precision and necessity | -99 | -99 | Unreliable deploy environment | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Communication | Everything regarding data | -99 | Rarely | 30 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | https://www.linkedin.com/ |
55 | Suspended (22) | 0 | Computer Science | -99 | Computer Science | Computer Science | -99 | Habillitation in Computer Science | Brazil | More than 2,000 employees | Project Lead / Project Manager | -99 | 35 | 32 | 100 | 20 | 6-10 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Mostly traditional | quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Medical diagnosis | quoted | Medical diagnosis | not quoted | -99 | quoted | bioinformatics | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | not quoted | -99 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -77 | -77 | -77 | -77 | -77 | -77 | -77 | -66 | -77 | -66 | -99 |
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