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31 | Completed (31) | 1,317 | Economics | -99 | M.Sc. in Economics | -99 | Data Scientist in Datacamp | -99 | Brazil | 1,001-2,000 employees | Business Analyst | -99 | 0 | 2 | 1 | 0 | 0 | not 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 | 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 | -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 | not quoted | not quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 20 | 30 | 25 | 12 | 5 | 3 | Client often don't know what they want to learn about data. | Sometimes it's not possible to do what they want. | The client lack computational resources to tackle the problem | Where we can find the information? | Who can authorize this type of data colletcion? | Not enough time to collect the sample with the appropriate size | Too much missing values | Prolems with outliers | Problems with formats | Time to read the literature about theme | Understand qhat the model fits in the situation | Verify if the model is accurate | Overfit | Verify again the accuracy. | The model is simple enough for the user? | Show the model in a didatic way | Expose the features with care | Display the model in a easy mode to read | Verify the results | Retrain the model if necessary | Feeding the model in appropriate way | -99 | -99 | -99 | Problems with data collection and cleaning | Others tasks which competes the time | Search the appropriate methodology | Frequently | 70 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | 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 | not 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 | not quoted | -77 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 0 | -99 | https://ww2.unipark.de/uc/seml/ |
34 | Completed (31) | 854 | -99 | Management | No | No | No | No | Brazil | More than 2,000 employees | Business Analyst | -99 | 2 | 2 | 1 | 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 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Human Resources | 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 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | High Relevance | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | Complex | Complex | Very Complex | Very Complex | Complex | Very Complex | Neutral | 30 | 12 | 12 | 12 | 12 | 12 | 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 | not quoted | 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 | 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 | 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 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 70 | not quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
36 | Completed (31) | 1,593 | Mathematics | Informatics | MSC Computer Science | PhD computer Science | Vários cursos in Coursera | -99 | Brazil | 51-250 employees | Project Lead / Project Manager | -99 | 20 | 5 | 5 | 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 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | Meteorology | quoted | not quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | Temperatura, Precipitation, COVID-19 patient outcome | quoted | Plant species | not quoted | -99 | quoted | Hospitals, time-series | not quoted | -99 | not quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | GNN | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Very Complex | Complex | Complex | Neutral | Neutral | Complex | 20 | 10 | 15 | 20 | 5 | 15 | 15 | Learning the problem domain | Identify the task to be solved | Check the accuracy of findings | Find the relevant data sources | Build data extractors | -99 | Data cleaning | Data reconciliation | -99 | Select the best learning algo | Hyper-parametrization | Select the right data | Build test dataset | -99 | -99 | Prepare production environment | -99 | -99 | Assess metricts | Identify concept drifts | -99 | -99 | -99 | -99 | Data preparation | Prediction Task identification | Selecionar of learning algo | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | quoted | not quoted | quoted | Literature | 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 | 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 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 60 | quoted | not quoted | quoted | not quoted | not quoted | -99 | Yes, Please, specify | Own approach | -99 |
57 | Completed (31) | 4,238 | Computer Science | Data science specialization | -99 | -99 | -99 | -99 | Germany | More than 2,000 employees | Solution Architect | -99 | 8 | 4 | 6 | 6 | 6-10 members | not quoted | not quoted | not 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 | not quoted | not quoted | -99 | not quoted | -99 | quoted | -99 | not quoted | -99 | quoted | Using clusterization to find groups of credit card numbers potentially leaked | not quoted | -99 | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | Low Relevance | Neutral | High Relevance | Extremely Relevant | Neutral | Complex | Easy | Easy | Neutral | Complex | Very Complex | Easy | 30 | 10 | 10 | 25 | 10 | 15 | 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 | Sometimes | 50 | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not 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 | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | -99 | not quoted | -99 | quoted | 100 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
46 | Completed (31) | 2,821 | Actuarial Science | Post Graduation in Data Science | M Sc in Data Science -ML models | no Ph D | no other certifications | -99 | Brazil | 501-1,000 employees | Data Scientist | -99 | 6 | 3 | 23 | 18 | 1-5 members | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | Balanced between agile and traditional | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | innovation | not quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | quoted | income models, claim and premium insurance models | quoted | probability models and scores, fraud and low default models | not quoted | -99 | quoted | KNN Apriori algortm and others | quoted | inbalanced dataset techniques | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | LGBM Catboosting | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Neutral | Neutral | Complex | Complex | Very Complex | Very Complex | Neutral | 15 | 10 | 10 | 15 | 15 | 25 | 10 | understand the pain and identify if ML is really needed to solve it | brainstorm the solution with ML | propose deadline to that project | is that data enough to that ML solution? | quality data validation | frequency and period (time) validation | wich event are we analysing ? | we need to cut or we need to cluster some kind of data? | we can work with all periods? why? (ex: test and validation data) | is that a classidication or regression problem? | what kind of technique seems bether to use for modelling? (metrics) | present and discuss metrics and distribution of results in this modelling | are metrics estable across the periods? (time validation) | confusion matrix | test and validation comparison | what kind of deploy is better? | how long it takes? | profit analysis | after deploy, data have the same ML results as predicted? | it will be necessary to review this ML solution? | -99 | -99 | -99 | -99 | understand the pain and identify if ML is really needed to solve it | we need to cut or we need to cluster some kind of data? | present and discuss metrics and distribution of results in this modelling | Sometimes | 20 | 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 | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | 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 | not quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | -99 | quoted | -99 | not quoted | 80 | not quoted | quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
53 | Completed (31) | 2,097 | Information System | -99 | M.Sc. in Applied Informatics | -99 | -99 | -99 | Brazil | 1,001-2,000 employees | Project Lead / Project Manager | -99 | 6 | 5 | 2 | 0 | 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 | not quoted | quoted | not quoted | not quoted | 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 | not quoted | -99 | quoted | Using classification to idenfify food in the 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 | High Relevance | High Relevance | Extremely Relevant | High Relevance | Extremely Relevant | High Relevance | High Relevance | Neutral | Complex | Complex | Complex | Complex | Complex | Neutral | 10 | 15 | 15 | 25 | 10 | 15 | 10 | not know about the problem | not knowing how to apply ML to the problem | -99 | insufficient amount of data | generate a meaningful data sample | -99 | sort the data | label the data | -99 | understand the models | apply the models | runtime | Select the best metrics | -99 | -99 | Not knowing how to deploy | -99 | -99 | I didn't apply it to my project | -99 | -99 | -99 | -99 | -99 | insufficient amount of data | apply the models | Not knowing how to deploy | Sometimes | 50 | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | not quoted | quoted | not quoted | quoted | 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 | quoted | not quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 30 | quoted | quoted | not quoted | not quoted | not quoted | -99 | No | -99 | -99 |
58 | Completed (31) | 1,696 | Computer Science | -99 | Computer Science | -99 | Microsoft Professional Program Data Science & ML specialization | -99 | Germany | 1-10 employees | Developer | -99 | 5 | 2 | 3 | 0 | 6-10 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 | 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 | quoted | C# | quoted | Injury Prediction | quoted | Pedestrian Detection, Image Label Classification | not quoted | -99 | not quoted | -99 | not quoted | -99 | 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 | -99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 35 | 20 | 5 | 5 | 5 | 5 | -99 | -99 | -99 | Availability | Quantity | Data Privacy | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Cost | Setup Difficulty | -99 | -99 | -99 | -99 | -99 | -99 | -99 | Data Availability | Sufficient Data Quantity | Deployment Costs for non-trivial ML projects | Sometimes | 30 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | not quoted | not quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | 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 | not quoted | not quoted | not quoted | -99 | quoted | not quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | 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://t.co/ |
64 | Completed (31) | 1,250 | Electrical and Electronics Engineering | -99 | M.Sc. in AI and Software Engineering | Computer science | Azure Associate AI Engineer, Azure Data Science Associate | -99 | Sweden | More than 2,000 employees | Other, which one? | Enterprise, system, solution architect | 40 | 15 | 5 | 1 | 50+ members | not quoted | not 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 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Data analysis for autonomous driving | quoted | not quoted | 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 | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | Neutral | Extremely Relevant | High Relevance | Extremely Relevant | Very Complex | Neutral | Complex | Easy | Very Complex | Complex | Complex | 30 | 10 | 20 | 5 | 20 | 5 | 10 | understand the context: domain, what decisions are made based on data in what activities to achieve what goals by what roles | reach consensus | resolve conflicts | Given context, define appropriate metrics | If humans are involved, how to avoid bias, motivate them to do it correctly etc. | Handling sensitive data, necessary to detect indirect discrimination | Handling disproportional classes of data | Perform data augmentation | -99 | Choose candidate approached | Partitition data, especially time series or unstructured data | -99 | Evaluate indirect/direct bias (e.g., indirect/direct discrimination), indirect is the hardest | Determine what metrics to use | -99 | Choose apropriate framework | -99 | -99 | Determine what metrics to monitor | Define alerters, thresholds | -99 | -99 | -99 | -99 | Basic: no proper engagement from management, no specific funding or no metrics to measure success | Understand the context | -99 | Sometimes | 50 | quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | not quoted | not quoted | -99 | not quoted | not quoted | quoted | quoted | quoted | quoted | not quoted | not quoted | not quoted | quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | -99 | quoted | quoted | quoted | quoted | quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | quoted | not quoted | quoted | not quoted | -99 | quoted | -99 | not quoted | 0 | not quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | https://www.linkedin.com/ |
65 | Completed (31) | 106 | -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 | -99 |
69 | Completed (31) | 79 | -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://ww2.unipark.de/uc/seml/ |
70 | Completed (31) | 1,301 | Computer Science | -99 | -99 | -99 | -99 | -99 | Colombia | 51-250 employees | Other, which one? | Consultant | 6 | 1 | 2 | 0 | 1-5 members | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | Balanced between agile and traditional | quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | -99 | not quoted | not quoted | 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 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | Extremely Relevant | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | High Relevance | Neutral | Complex | Very Complex | Complex | Neutral | Complex | Neutral | 15 | 20 | 15 | 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 | Frequently | 23 | 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 | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | not quoted | -99 | not quoted | quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | not quoted | 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 | 10 | quoted | not quoted | not quoted | not quoted | not quoted | -99 | No | -99 | http://m.facebook.com |
72 | Completed (31) | 2,128 | Statistics | Data Science | -99 | -99 | -99 | -99 | Brazil | 251-500 employees | Data Scientist | -99 | 0 | 1 | 1 | 5 | 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 | 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 | not quoted | quoted | quoted | SAS | not quoted | -99 | quoted | -99 | quoted | -99 | quoted | -99 | quoted | Experimental designs, sampling methods to improve the test confiabilty, survival probabilities. | not quoted | not quoted | not quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | quoted | Kaplan Meyer, PCA, LDA, ANOVA, ARIMA seasonal, IRT | Extremely Relevant | Extremely Relevant | Extremely Relevant | High Relevance | High Relevance | Low Relevance | Neutral | Very Complex | Complex | Complex | Very Easy | Neutral | Neutral | Complex | 25 | 10 | 30 | 5 | 10 | 10 | 10 | Different concepts of same thing in the Communication | Different way to think about the problem | Different way to establish what can be the answer of the problem | Trash Data structures | No Data avaliable | Trash Data information | To many NULLs | Wrong data type | -99 | Choose the best model | Choose best sampling training data | Understand what the really model does | Choose correct technique to evaluate | See if it really answers the question problem | -99 | Some methods are expensive | Hard to make it accessible to specific group of people with login | -99 | Monitoring a model of another person (that u didn't work) | -99 | -99 | Understand statistics behind the models | Check if the model is really adequate using statiscal methods | Check is the sample is really representative and what is the confiability | Understand the problem | Data collection | Pre processing | Frequently | 70 | quoted | 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 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | quoted | Notion/ Git hub | not quoted | 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 | quoted | not quoted | -99 | not quoted | 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 | not quoted | -99 | quoted | 60 | quoted | quoted | quoted | quoted | not quoted | -99 | Yes, Please, specify | automl in R / driveML in R / databricks Jobs | https://lm.facebook.com/ |
75 | Completed (31) | 1,936 | -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 | -99 |
105 | Completed (31) | 2,183 | Mechanical Engineering | Control Theory, Information Technologies, Mechatronics | M.Sc. Robotics, Cognition, Intelligence | -99 | -99 | -99 | Germany | 1-10 employees | Project Lead / Project Manager | -99 | 12 | 6 | 4 | 1 | 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 | quoted | not quoted | not quoted | not quoted | not 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 | identify properties and causes of human gait patterns | not quoted | -99 | quoted | extract step positions and times from floor sensor data | not quoted | -99 | not quoted | not quoted | quoted | not quoted | quoted | not quoted | quoted | quoted | not quoted | not quoted | quoted | quoted | quoted | not quoted | -99 | Neutral | Extremely Relevant | High Relevance | High Relevance | Extremely Relevant | Extremely Relevant | Extremely Relevant | Complex | Easy | Easy | Complex | Very Complex | Neutral | Neutral | 5 | 40 | 15 | 10 | 15 | 10 | 5 | estimating development time | converging expectations and possibilities | overthinking requirements | technical problems | communicating recording procedure | finding probands | keeping code understandable | missing values | -99 | finding a good input encoding | hyperparameter optimisation | -99 | bad real-world performance | validation leaking | -99 | creating robust APIs | -99 | -99 | graceful fault handling | -99 | -99 | -99 | -99 | -99 | converging expectations and possibilities | overthinking requirements | bad real-world performance | Frequently | 100 | quoted | quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | not quoted | quoted | 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 | not 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 | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | -99 | quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | not quoted | -99 | quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 80 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | https://www.google.com/ |
77 | Completed (31) | 930 | Physics | -99 | Particle physics | Particle physics | Artificial Intelligence applied to Geosciences at UFMG | -99 | Brazil | More than 2,000 employees | Data Scientist | -99 | 20 | 5 | 5 | 1 | 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 | 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 | 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 | Extremely Relevant | High Relevance | High Relevance | Neutral | Very Complex | Complex | Easy | Easy | Easy | Complex | Neutral | 25 | 20 | 10 | 25 | 5 | 10 | 5 | Identifying the opportunity | -99 | -99 | Getting permission from the data owners | Identifying the best first oil field to use | Selecting the actual data | SEGY reading | -99 | -99 | Time to train with huge amounts of data (TB+) | Hyperparameter tuning | Model design | Selecting metrics | -99 | -99 | UI | Data reading constraints | -99 | UI | Metrics | -99 | -99 | -99 | -99 | Getting permission from the data owners | Time to train with huge amounts of data (TB+) | UI | Rarely | 20 | 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 | 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 | 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 | https://statics.teams.cdn.office.net/ |
86 | Completed (31) | 1,509 | Engineering | Data Science, Aeroderivative Turbines, Database architecture | -99 | -99 | -99 | -99 | Brazil | 1-10 employees | Other, which one? | Director | 15 | 2 | 1 | 1 | 1-5 members | not quoted | not quoted | not 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 | 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 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | not quoted | not quoted | -99 | Extremely Relevant | Neutral | High Relevance | High Relevance | Neutral | Neutral | High Relevance | Neutral | Easy | Neutral | Neutral | Neutral | Neutral | Neutral | 20 | 15 | 10 | 20 | 15 | 10 | 10 | understand what to achieve | -99 | -99 | access to data | -99 | -99 | analyze data | -99 | -99 | discover better model | -99 | -99 | is the evaluation right? | -99 | -99 | deploy cheap as possible | -99 | -99 | develop tools to monitor | -99 | -99 | team integration | -99 | -99 | Dumb team lider | acess to data | team integration | I don't know | 50 | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | not quoted | quoted | Director | not quoted | quoted | not quoted | quoted | not quoted | quoted | Beer please | 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 | 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 | not quoted | -99 | quoted | quoted | not 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 | 100 | quoted | not quoted | quoted | not quoted | not quoted | -99 | No | -99 | -99 |
111 | Completed (31) | 1,582 | Electrical and Electronics Engineering | Business information | Logistics | -99 | IBM Data science on Coursera | -99 | Brazil | More than 2,000 employees | Business Analyst | -99 | 2 | 2 | 2 | 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 | not quoted | not quoted | 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 | Predict the usage of a kind of vessel | 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 | quoted | not quoted | -99 | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Extremely Relevant | Very Complex | Very Complex | Complex | Very Complex | Neutral | I don't know | Very Complex | 10 | 50 | 20 | 10 | 5 | 5 | 0 | Deal with different interpretations of the problem | -99 | -99 | Discover where to get the data | Get all permissions | Conect with the bases | Understand the meaning of each data | Conect different data based | Different names or orthography for the same vessel | To decide te best model | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | To get the data to keep monitoring | -99 | -99 | -99 | -99 | -99 | Get all permissions | -99 | -99 | I don't know | -77 | 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 | -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 | 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 | not quoted | quoted | not quoted | not quoted | -99 | not quoted | -99 | quoted | 0 | not quoted | not quoted | not quoted | not quoted | quoted | null | No | -99 | -99 |
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