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. g. record the final loss and accuracy of the training step as well as the accuracy score of the model on the testing set. how did your model do? based on the accuracy score, how confident would you be in using this model to diagnose cirrhosis? project b : recognizing digits there are many real - world applications th... | openstax_principles-of-data-science-web | [
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with the narrative and experimenting with different artistic styles and techniques to create a visually appealing storybook. the group should refine and select the best illustrations for inclusion in the final product. c. for added interest, use ai - based music composition tools to create background music for the stor... | openstax_principles-of-data-science-web | [
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are only relevant in the context of language translation and have no significant impact on everyday life. c. speech recognition is primarily used for recording audio, while text - to - speech is used mainly for entertainment purposes, such as audiobooks. d. these algorithms are important because they enable hands - fre... | openstax_principles-of-data-science-web | [
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understanding the general sentiment of each review. b. an investment firm is developing a model to predict future stock prices based on historical price data. the firm wants to account for trends, patterns, and time - based dependencies in the data. c. a technology company is building a model to classify images into va... | openstax_principles-of-data-science-web | [
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that a neural network has been trained to classify students as likely to graduate or not likely to graduate based on various input parameters. there is a single output neuron having four inputs, 378 7 • quantitative problems access for free at openstax. org and one output,. the output is used to make the classification... | openstax_principles-of-data-science-web | [
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dissemination ). ( credit : modification of work " data security breach " by https : / / www. blogtrepreneur. com, cc by 2. 0 ) chapter outline 8. 1 ethics in data collection 8. 2 ethics in data analysis and modeling | openstax_principles-of-data-science-web | [
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8. 3 ethics in visualization and reporting introduction data science is a rapidly growing field that has revolutionized numerous industries by providing an exceptional amount of data for analysis and interpretation. however, the existence of plentiful amounts of data, along with increased access to it, also raises many... | openstax_principles-of-data-science-web | [
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8. 1 ethics in data collection learning outcomes by the end of this section, you should be able to : • 8. 1. 1 discuss data protection and regulatory compliance considerations in data science. • 8. 1. 2 explain the importance of privacy and informed consent in data science. • 8. 1. 3 identify data security and data sha... | openstax_principles-of-data-science-web | [
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org / r / iadss ) ), dsa ( data science association ( https : / / openstax. org / r / datascienceassn ) ), and adasci ( association of data scientists ( https : / / openstax. org / r / adasci ) ). in addition, one must be fully aware of governmental regulations and industry standards. regulatory compliance before begin... | openstax_principles-of-data-science-web | [
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states, with its amendment, cpra ( 2023 ). these regulations demand that organizations collect, process, and accumulate confidential data securely and 382 8 • ethics throughout the data science cycle access for free at openstax. org transparently. figure 8. 2 and figure 8. 3 summarize the gdpr and ccpa / cpra principle... | openstax_principles-of-data-science-web | [
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8. 1 • ethics in data collection 383 electronic documents act [ pipeda ] in canada ) ; and many other issues of data privacy, security, and integrity. some regulations apply only to certain types of data or data related to certain industries. for example, in the united states, the health insurance portability and accou... | openstax_principles-of-data-science-web | [
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to be undisclosable from other datasets, especially in the medical or financial domain. ultimately, adhering to applicable regulatory provisions throughout the data science cycle ensures that all relevant information and data are collected and stored in a way that meets its specific needs without violating any laws or ... | openstax_principles-of-data-science-web | [
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organization ’ s regulatory compliance officer is responsible for ensuring that the data science project follows all relevant regulatory needs. a regulatory compliance officer ( rco ) is a trained individual responsible for confirming that a company or organization follows the laws, regulations, and policies that rule ... | openstax_principles-of-data-science-web | [
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appropriate policies and procedures. solution the correct choice is d. gadget galaxy must halt the project and seek guidance from a regulatory compliance expert to develop appropriate policies and procedures. the project team should undergo training on regulatory compliance to ensure team members understand and observe... | openstax_principles-of-data-science-web | [
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8. 1 • ethics in data collection 385 informed consent consists of disclosure, or providing full and clear information about the activity, study, survey, or data collection process ; understanding, or ensuring that the participant is able to comprehend the information ; voluntariness, or absence of any coercion, pressur... | openstax_principles-of-data-science-web | [
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certain sites depending on your own preferences. does the use of cookies by online websites violate any of the principles of informed consent? what about websites that will not load unless the user agrees to their cookie policy? confidentiality refers to the safeguarding of privacy and security of data by controlling a... | openstax_principles-of-data-science-web | [
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of data scientists is contracted by gadget galaxy to conduct a study on consumer buying patterns. they begin by collecting private information from respective participants. the team has implemented an informed consent process to ensure ethical and transparent data collection. this process includes a consent form outlin... | openstax_principles-of-data-science-web | [
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openstax. org / r / oversight1 ). this breach exposed the personal information of over 145 million americans ( near half the total u. s. population ) along with over 15 million british citizens and a smaller number of people from other countries, making it one of the largest and most severe data breaches in history. th... | openstax_principles-of-data-science-web | [
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8. 1 • ethics in data collection 387 exploring further cryptocurrencies bitcoin and other cryptocurrencies use encryption to secure transactions and maintain the integrity of the blockchain, the decentralized ledger that records all transactions. encryption methods, such as public - private key cryptography, ensure tha... | openstax_principles-of-data-science-web | [
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' s medical information or financial information, this would still pose a data security risk. organizations should always ensure that any data they make publicly available is secure and protected by their data security policies and protocols. if data is accidentally breached during the project development process ( the... | openstax_principles-of-data-science-web | [
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388 8 • ethics throughout the data science cycle access for free at openstax. org example 8. 3 problem consider a data science team working on a revolutionary data science project that would have a huge impact on the health care industry. the project involves gathering and analyzing sensitive medical information from p... | openstax_principles-of-data-science-web | [
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data pertains to confidential medical information of patients, it is important to obtain consent from the data owner before granting access to a third party. this can be done through a formal process where the data owner signs off on granting access to the researcher. 5. use secure methods for data transfer : if access... | openstax_principles-of-data-science-web | [
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8. 1 • ethics in data collection 389 example 8. 4 problem a data scientist is leading a project for a construction company that involves extensive data collection. the team carefully assesses the data requirements and implements measures to encrypt the stored data during transmission to safeguard against potential brea... | openstax_principles-of-data-science-web | [
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compromised in the breach. this notification should include details of the breach, the type of data exposed, and any steps the individual can take to protect themselves. 5. secure affected accounts : if the breach involves compromised user accounts, those accounts must be secured immediately. this may involve resetting... | openstax_principles-of-data-science-web | [
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of rules, policies, and procedures that enable precise control over data access while ensuring that it is safeguarded. for data sharing to be successful, organizations and individuals are required to establish strong infrastructure management and protocols. it is crucial to consider all perspectives of the project part... | openstax_principles-of-data-science-web | [
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, researchers, and the project ’ s eventual audience, should be granted access to the data in accordance with the intended purpose. if appropriate, and with no privacy - related implications, the data may be disseminated to the broader public through platforms like kaggle ( https : / / openstax. org / r / kaggle ), whi... | openstax_principles-of-data-science-web | [
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8. 1 • ethics in data collection 391 8. external partners or clients. in some cases, access to the data may be necessary for external partners or clients who are involved in the project or have a business need for the data. it is important to have measures in place to control and monitor data access, such as role - bas... | openstax_principles-of-data-science-web | [
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is most appropriate. permissions should be granted to various team members based on their need to know. | openstax_principles-of-data-science-web | [
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8. 2 ethics in data analysis and modeling learning outcomes by the end of this section, you should be able to : • 8. 2. 1 define bias and fairness in the context of data science and machine learning. • 8. 2. 2 identify sensitive data elements and implement data anonymization techniques. • 8. 2. 3 apply data validation ... | openstax_principles-of-data-science-web | [
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bias. ) bias, whether intentional or unintentional, causes unethical outcomes and may even lead to legal concerns. it is important to proactively address any potential concerns before concluding the project and posting the results. data science teams must plan and build models addressing all possible outcomes with fair... | openstax_principles-of-data-science-web | [
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when data is biased, the resulting models and algorithms may lead to misinformed decisions, inaccurate predictions, or unfair outcomes. a real - world example of how bias in data science methods or data collection can ruin an analysis is seen in the case of the compas algorithm used in the u. s. criminal justice system... | openstax_principles-of-data-science-web | [
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the modeling process are based on improving the model, not | openstax_principles-of-data-science-web | [
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8. 2 • ethics in data analysis and modeling 393 manipulating the model to achieve a desired outcome. the use of available training datasets ( see decision - making using machine learning basics ) is crucial in detecting discrepancies or inequalities that could lead to unfair results. models and algorithms should be tes... | openstax_principles-of-data-science-web | [
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the outcome, such as gender or race are not used for prediction. 3. interpretability. understanding how outcomes are related to the features of the data can help determine if bias is present and lead to corrections. this may be difficult if the algorithm is very complex, especially when the model involves neural networ... | openstax_principles-of-data-science-web | [
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be dataset 3 ( c ), as it includes a broadly representative range of individuals. while dataset 2 ( b ) may also represent a diverse range of individuals, the dataset is likely too small to be of much use. dataset 1 ( a ) is not appropriate, as the system would then be biased to perform well only on college graduates t... | openstax_principles-of-data-science-web | [
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. moreover, there is a responsibility to continuously monitor and reassess the ethical implications of data analysis and modeling. as technology and data practices evolve, so do ethical concerns. it is important to regularly review and adapt ethical standards to ensure the protection of individuals'rights and promote r... | openstax_principles-of-data-science-web | [
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8. 2 • ethics in data analysis and modeling 395 is the process of transforming data into a fixed - length value or string ( called a hash ), typically using an algorithm called a hash function. the key property of hashing is that it produces a unique output for each unique input ( within practical constraints ) while m... | openstax_principles-of-data-science-web | [
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use protected techniques to ensure that data can ’ t be accessed or operated in an unauthorized manner. example 8. 7 problem mike is a data privacy officer for his local school district. the district is looking to collect data from parents to gain insight into their opinions on the district's curriculum. however, mike ... | openstax_principles-of-data-science-web | [
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who share the same characteristics, thus achieving the k - anonymization. 396 8 • ethics throughout the data science cycle access for free at openstax. org data that is under intellectual property rights, such as copyright, can also be anonymized as long as the intellectual property rights holder has given permission t... | openstax_principles-of-data-science-web | [
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data validation data validation ( as introduced in collecting and preparing data ) is the process of checking, verifying, and validating the accuracy and reliability of data before it is used in decision - making. when both data validation and ethical rules are applied, the models and analyses that use the data are mor... | openstax_principles-of-data-science-web | [
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8. 2 • ethics in data analysis and modeling 397 ethical guidelines for data validation cover a broad range of protocol and policy intended to inform when and how such tools as cross - validation and outlier detection and handling should be used. there are overlaps and interactions between these three key areas of data ... | openstax_principles-of-data-science-web | [
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is estimated to account for between 1 and 1. 5 % of all global electricity demand ( https : / / openstax. org / r / iea ), and greenhouse gas emissions from these sources are expected to exceed 14 % of global emissions of greenhouse gasses by 2040 ( nordgren, 2023 ). the environmental effects of data 398 8 • ethics thr... | openstax_principles-of-data-science-web | [
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8. 3 ethics in visualization and reporting learning outcomes by the end of this section, you should be able to : • 8. 3. 1 recognize the importance of visualizing data in a way that accurately reflects the underlying information. • 8. 3. 2 define data source attribution and its significance in data science and research... | openstax_principles-of-data-science-web | [
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, scales, and legends. 5. present all data in a complete picture, avoiding masking or omitting portions of graphs. 6. ensure that the scales on all axes in a chart are consistent and proportionate. 7. exercise caution when implying causality between connected data points, providing supporting evidence if needed. 8. uti... | openstax_principles-of-data-science-web | [
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8. 3 • ethics in visualization and reporting 399 3. avoiding bias and manipulation. data scientists must avoid manipulating or cherry - picking data to support a specific narrative or agenda. this can lead to biased results and misinterpretations, which can have serious consequences. 4. fact - checking and verifying da... | openstax_principles-of-data-science-web | [
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ethics throughout the data science cycle access for free at openstax. org figure 8. 6 second data scientist ’ s presentation a. the second data scientist chooses to remove the low sales outliers from the graph. this approach can be useful in certain cases, such as when the data is extremely skewed and the outliers are ... | openstax_principles-of-data-science-web | [
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holistic and unbiased depiction of the sales trend. this enables a comprehensive understanding of the sales performance and any discernible patterns. on the contrary, the second data scientist's presentation may give the illusion of a positive trend in sales, but it fails to accurately portray the complete dataset. omi... | openstax_principles-of-data-science-web | [
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8. 3 • ethics in visualization and reporting 401 than manipulating the data to support a desired narrative. additionally, the removal of data points without disclosure in the graph violates ethical principles in data analysis. example 8. 9 problem two expert data analysts have constructed bar graphs shown in figure 8. ... | openstax_principles-of-data-science-web | [
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making it more difficult to observe small differences in percentages. moreover, graph b fails to label the horizontal or vertical axes in equivalent | openstax_principles-of-data-science-web | [
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8. 3 • ethics in visualization and reporting 403 detail, which may cause undue confusion, even if the intended labels may be clear from context. example 8. 10 problem a data scientist at a health care research institution is analyzing the effects of a new medication on a specific disease. after collecting data from mul... | openstax_principles-of-data-science-web | [
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clearly identifying and acknowledging the sources employed in the visualizations and reporting of data. it is the data scientist's responsibility to uphold these principles and present data in a manner that is both transparent and ethical. data source attribution is demanded for several reasons : 1. accuracy and credib... | openstax_principles-of-data-science-web | [
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instructors allow their students to use resources such as chatgpt to assist them in their writing and research. similarly to citing textual references, these instructors might require their students to include a statement on the use of ai, including what prompts they used and exactly what the ai or chatbot responded. t... | openstax_principles-of-data-science-web | [
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8. 3 • ethics in visualization and reporting 405 questions of academic integrity abound, and similar ethical issues must also be considered when planning and conducting a data science project. accessibility and inclusivity one of the ethical responsibilities of data scientists and researchers is to ensure that the data... | openstax_principles-of-data-science-web | [
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such as geographic location, age, education level, and income level contribute to the digital divide, which can lead to data inequality, where certain groups are underrepresented or excluded from data - driven decision - making. addressing the digital divide through investments in infrastructure ( e. g., developing rel... | openstax_principles-of-data-science-web | [
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access for free at openstax. org increasing demand for data science skills in the workforce, encouraging broader adoption in varied educational settings. | openstax_principles-of-data-science-web | [
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8. 3 • ethics in visualization and reporting 407 key terms anonymization act of removing personal identifying information from datasets and other forms of data to make sensitive information usable for analysis without the risk of exposing personal information anonymous data data that has been stripped of personally ide... | openstax_principles-of-data-science-web | [
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from unauthorized access or interception ethics in data science responsible collection, analysis, use, and dissemination of data explainable ai ( xai ) set of processes, methodologies, and techniques designed to make artificial intelligence ( ai ) models, particularly complex ones like deep learning models, more unders... | openstax_principles-of-data-science-web | [
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of age, ability, or disability group project project a : analysis of one - year temperatures the world wildlife fund ( wwf ), the largest privately supported international conservation organization, recently advertised for a data scientist to investigate temperature variations. after receiving applications from ten hig... | openstax_principles-of-data-science-web | [
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and suggestions regarding the quality of food services provided in the cafeteria. the gathered information will be used to enhance the food quality at the school, thereby potentially improving the academic performance of students. group 1 will be responsible for creating a questionnaire survey on the food services qual... | openstax_principles-of-data-science-web | [
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charts, and pie charts. • the second team will analyze the visual object from the first team based on ethical principles. ethical considerations : the second team will carefully analyze each visual object in the report, taking into account ethical principles such as presentation accuracy, data source attribution, acces... | openstax_principles-of-data-science-web | [
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organizations from attack? research one to two specific examples of an organizational target of a ransomware attack to support your case. 410 8 • group project access for free at openstax. org chapter review 1. which option best explains the significance of informed consent during the data collection phase? a. to ensur... | openstax_principles-of-data-science-web | [
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based data only d. quantitative, qualitative, tabular, text - based, and audio - based data 8. who should be given access to the data in a data science project? a. anyone who is directly involved in the project b. shareholders and customers only c. anyone who is directly involved in the project and has a legitimate nee... | openstax_principles-of-data-science-web | [
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potential bias? a. to ensure models are not overfitting b. to maintain the accuracy of the model c. to uphold fairness and equity in the decision - making process d. to avoid legal consequences 14. the admissions team of a large university would like to conduct research on which factors contribute the most to student s... | openstax_principles-of-data-science-web | [
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what is the term used for properly crediting and acknowledging the sources of data in data science and research? a. data attribution b. data manipulation c. data accuracy d. data extraction 19. what is one ethical responsibility of data scientists and researchers when presenting data? a. maximizing profits b. manipulat... | openstax_principles-of-data-science-web | [
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, heidari, h., jabbari, s., kearns, m., & roth, a. ( 2021 ). fairness in criminal justice risk assessments : the state of the art. sociological methods & research, 50 ( 1 ), 3 - 44. https : / / doi. org / 10. 1177 / 0049124118782533 noaa national centers for environmental information. ( 2021 ). state of the climate : g... | openstax_principles-of-data-science-web | [
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9. 5 multivariate and network data visualization using python introduction data visualization serves as an effective strategy for detecting patterns, trends, and relationships within complex datasets. by representing data graphically, analysts can deduce relationships, dependencies, and outlier behaviors that might oth... | openstax_principles-of-data-science-web | [
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output such as graphs, charts, and maps. in descriptive statistics : statistical measurements and probability distributions, we introduced graphs of probability distributions such as binomial and normal distributions. then, in inferential statistics and regression analysis, we introduced and worked with scatterplots fo... | openstax_principles-of-data-science-web | [
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9. 1 encoding univariate data learning outcomes by the end of this section, you should be able to : • 9. 1. 1 visualize data with properly labeled boxplots, histograms, and pareto charts. • 9. 1. 2 use python to generate various data visualizations for univariate data. we ’ ve seen that data may originate from surveys,... | openstax_principles-of-data-science-web | [
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##ots, histograms, and pareto charts. boxplots a boxplot or box - and - whisker plot is a graph used to display a distribution of a dataset based on the quartiles, minimum, and maximum of the data. this is called the five - number summary, and thus a boxplot displays the minimum, ( first quartile ), median, ( third qua... | openstax_principles-of-data-science-web | [
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the location of the minimum. also, draw a whisker to the right extending from the rectangle to the location of the maximum. 5. an optional step is to plot outliers on the boxplot either to the left of the left whisker or to the right of the right whisker. ( see descriptive statistics : statistical measurements and prob... | openstax_principles-of-data-science-web | [
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the distribution. recall from collecting and preparing data that skewness refers to the lack of symmetry in a distribution of data. if one whisker ( either the left or right whisker ) is longer than the other, it indicates skewness toward that side. for instance, if the left whisker is longer, the distribution may be l... | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 417 series graphs, boxplots, and scatterplots, respectively. we ’ ve already seen how this package is used to generate a variety of visualizations, including scatterplots ( in inferential statistics and regression analysis ), time series graphs ( in time and series forecasting, and clust... | openstax_principles-of-data-science-web | [
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number summary for this dataset and the corresponding boxplot showing the distribution of salaries at a company ( refer to descriptive statistics : statistical measurements and probability distributions for details on how to calculate the median and quartiles of a dataset ) : five - number summary for salaries at a com... | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 419 quartile, respectively. recall from measurements of position that the interquartile range ( iqr ) is calculated as the third quartile less the first quartile, and thus the iqr is represented by the width of the rectangle shown in the boxplot. in this example, the first quartile is $ ... | openstax_principles-of-data-science-web | [
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introduced in what are data and data science?. ) name team position height ( inches ) weight ( pounds ) age ( years ) paul _ mcanulty sd outfielder 70 220 26. 01 terrmel _ sledge sd outfielder 72 185 29. 95 jack _ cust sd outfielder 73 231 28. 12 jose _ cruz _ jr. sd outfielder 72 210 32. 87 russell _ branyan sd outfie... | openstax_principles-of-data-science-web | [
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25. 75 table 9. 1 baseball player dataset ( socr - small. csv ) source : http : / / wiki. stat. ucla. edu / socr / index. php / socr _ data _ mlb _ heightsweights solution here is the python code to generate a vertical boxplot for the ages of a sample of baseball players. ( note : in order to generate a boxplot using a... | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 421 example 9. 2 problem a real estate agent would like to compare the distribution of home prices in san francisco, california, versus san jose, california. the agent collects data for a sample of recently sold homes as follows ( housing prices are in u. s. $ 1000s ). san francisco hous... | openstax_principles-of-data-science-web | [
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home prices ( in thousands )') # define a function to format the ticks for the y - axis to include dollar signs def format _ ticks ( value, tick _ number ) : return f'$ { value :,. 0f }'python code 422 9 • visualizing data access for free at openstax. org # apply the custom formatter to the y - axis ax. yaxis. set _ ma... | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 423 figure 9. 2 histogram displaying the distribution of heights ( in inches to the nearest half - inch ) of 100 male semiprofessional soccer players histograms are widely used in exploratory data analysis and descriptive statistics to gain insights into the distribution of numerical dat... | openstax_principles-of-data-science-web | [
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distribution of the data, including information about central tendency, spread, skewness, and presence of outliers. the shape of a histogram can provide characteristics of the underlying data distribution. if the histogram shows the heights of bars where the tallest bar is in the center of the histogram and the bars de... | openstax_principles-of-data-science-web | [
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, including examples of skewed distributions. a histogram with multiple peaks is indicative of a bimodal or multimodal distribution — which indicates the possibility of subpopulations within the dataset. a histogram showing bars that are all at the same approximate height is indicative of a uniform distribution, where ... | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 425 to create a histogram, follow these steps : 1. decide on the number of bins. 2. calculate the width of each bin. 3. set up a frequency distribution table with two columns : the first column shows the interval for each bin and the second column is the frequency for that interval, whic... | openstax_principles-of-data-science-web | [
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specified as well as the number of bins. import matplotlib. pyplot as plt import matplotlib. ticker as ticker # define a function to format the ticks with commas as thousands separators def format _ ticks ( value, tick _ number ) : return f'{ value :,. 0f }'# dataset of sales amounts sales = [ 4969, 4092, 2277, 4381, 3... | openstax_principles-of-data-science-web | [
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from the left side of the chart to the right side of the chart. a pareto chart is unique in that it combines both bar and line graphs to represent data in descending order of frequency or importance, along with the cumulative percentage of the total. the chart is named after vilfredo pareto, an italian economist who id... | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 427 when creating a pareto chart, two vertical scales are typically employed. the left vertical scale is based on frequency, whereas the right vertical scale is based on cumulative percentage. the left vertical scale corresponds to the bar graph and the bars are then oriented in descendi... | openstax_principles-of-data-science-web | [
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twinx function is a method in matplotlib that allows the two y - axis scales to share the same x - axis. import pandas as pd import matplotlib. pyplot as plt # manufacturing data results for one - month time period python code 428 9 • visualizing data access for free at openstax. org data = {'failure _ category': ['cra... | openstax_principles-of-data-science-web | [
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' failure _ category'], rotation = 45, ha ='right') # title plt. title ('pareto chart of smartphone manufacturing defects') # show the plot plt. show ( ) the resulting output will look like this : | openstax_principles-of-data-science-web | [
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9. 1 • encoding univariate data 429 notice in the pareto output chart that the leftmost three bars account for approximately 75 % of the overall defect level, so a quality engineer can quickly determine that the three categories of “ scratches, ” “ does not charge, ” and “ cracked screen ” account for the majority of t... | openstax_principles-of-data-science-web | [
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9. 2 encoding data that change over time learning outcomes by the end of this section, you should be able to : • 9. 2. 1 create and interpret labeled graphs to visually identify trends in data over time. • 9. 2. 2 use python to generate various data visualizations for time series data. researchers are frequently intere... | openstax_principles-of-data-science-web | [
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, etc. by doing this, we make each point on the graph correspond to a point in time and a measured quantity. the points on the graph are typically connected by straight lines in the order in which they occur. once the chart is created, a researcher can identify trends or patterns in the data to help guide decision - ma... | openstax_principles-of-data-science-web | [
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9. 2 • encoding data that change over time 431 order in which they occur ( see the python output from example 9. 6 ). using python for times series data visualization data visualization is a very important part of data science, and python has many built - in graphing capabilities through a package called matplotlib. to... | openstax_principles-of-data-science-web | [
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def format _ ticks ( value, tick _ number ) : return f'{ value :,. 0f }'python code 432 9 • visualizing data access for free at openstax. org # apply the custom formatter to the y - axis plt. gca ( ). yaxis. set _ major _ formatter ( ticker. funcformatter ( format _ ticks ) ) # add labels to the x and y axes by using x... | openstax_principles-of-data-science-web | [
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9. 2 • encoding data that change over time 433 intentionally adjust the scale to produce a desired effect in the graph ). the use of colors to differentiate data can be an important part of graphs and displays. overall, the use of color can make the visualization more appealing and engaging for the viewer. however, the... | openstax_principles-of-data-science-web | [
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in your pie chart ; if you have more than about five or six categories, you may want to consider a different type of chart. consider combining categories where appropriate for simplicity. ( sometimes an “ other ” category can be used to combine categories with very few counts. ) if the graph is intended for a more tech... | openstax_principles-of-data-science-web | [
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4 offers some suggestions for picking the most appropriate graphing method based on your goals and type of data. goal of visualization type of data visualization methods to consider show a comparison of different groups or categories qualitative or quantitative • bar charts are useful for comparing different groups of ... | openstax_principles-of-data-science-web | [
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9. 3 graphing probability distributions learning outcomes by the end of this section, you should be able to : • 9. 3. 1 create graphs to visualize the shape of various types of probability distributions. • 9. 3. 2 interpret probabilities as areas under probability distributions. • 9. 3. 3 use python to generate various... | openstax_principles-of-data-science-web | [
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9. 3 • graphing probability distributions 435 • there are only two possible outcomes for each trial, which can be labeled as “ success ” or “ failure. ” • the probability of success remains the same for each trial of the experiment. • the random variable x counts the number of successes in the experiment. notice that s... | openstax_principles-of-data-science-web | [
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to the value of the probability of success can affect the shape of the distribution, as shown in example 9. 7. example 9. 7 problem use the python function binom to graph the binomial distribution for and three different values of, namely, and, and comment on the resulting shapes of the distributions. solution to creat... | openstax_principles-of-data-science-web | [
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python code | openstax_principles-of-data-science-web | [
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9. 3 • graphing probability distributions 437 here is the graph for and. here is the graph for and. 438 9 • visualizing data access for free at openstax. org notice the impact of p on the shape of the graph : for, the graph is right skewed, for, the graph resembles a bell - shaped distribution, and for, the graph is le... | openstax_principles-of-data-science-web | [
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