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- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.07%3A_Group_ProjectThis page outlines three projects aimed at enhancing data science skills for students and professionals. Project A focuses on finding and cleaning secondary data while analyzing datasets relevant to s...This page outlines three projects aimed at enhancing data science skills for students and professionals. Project A focuses on finding and cleaning secondary data while analyzing datasets relevant to specific policies. Project B involves downloading a dataset, formulating questions, and visualizing results using Python.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.08%3A_Chapter_ReviewThis page includes multiple-choice questions on data science. The first question addresses incorrect step and goal pairings in the data science cycle. The second contrasts local storage with cloud sys...This page includes multiple-choice questions on data science. The first question addresses incorrect step and goal pairings in the data science cycle. The second contrasts local storage with cloud systems in the evolution of data management. The third emphasizes the interdisciplinary nature of data science by asking for the best example among various fields, including history, mathematics, biology, and chemistry.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/03%3A_Descriptive_Statistics-_Statistical_Measurements_and_Probability_Distributions/3.04%3A_Probability_TheoryThis page explores key probability concepts essential for data science, covering terminology, relative and theoretical probability, and practical examples. It details dependent and independent events,...This page explores key probability concepts essential for data science, covering terminology, relative and theoretical probability, and practical examples. It details dependent and independent events, emphasizing their applications in marketing and medicine through relatable scenarios. The significance of conditional probability, illustrated by Bayes' Theorem, is highlighted, particularly in medical diagnostics.
- https://eng.libretexts.org/Bookshelves/Computer_Science/Programming_Languages/Python_Programming_(OpenStax)/15%3A_Data_Science/15.06%3A_Chapter_SummaryThis page provides an overview of data science fundamentals, highlighting its multidisciplinary nature and lifecycle, which involves data acquisition, exploration, analysis, and reporting. It introduc...This page provides an overview of data science fundamentals, highlighting its multidisciplinary nature and lifecycle, which involves data acquisition, exploration, analysis, and reporting. It introduces key Python libraries, including NumPy for numerical tasks and Pandas for data management. The importance of Exploratory Data Analysis (EDA) and data visualization techniques is emphasized, along with functions for data structure manipulation in Python.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/04%3A_Inferential_Statistics_and_Regression_Analysis/4.02%3A_Hypothesis_TestingThis page discusses hypothesis testing in statistics, focusing on one-sample and two-sample methods. Key concepts include defining null and alternative hypotheses, calculating test statistics and p-va...This page discusses hypothesis testing in statistics, focusing on one-sample and two-sample methods. Key concepts include defining null and alternative hypotheses, calculating test statistics and p-values, and making decisions about hypotheses based on significance levels. Real-world applications are illustrated, including claims about student AI usage and medication effectiveness, with examples showing how to analyze sample data, assess claims, and interpret results.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/08%3A_Ethics_Throughout_the_Data_Science_Cycle/8.02%3A_Ethics_in_Data_Analysis_and_ModelingThis page addresses ethical considerations in data science and machine learning, focusing on bias and fairness. It outlines the definition of bias, the importance of identifying sensitive data, and th...This page addresses ethical considerations in data science and machine learning, focusing on bias and fairness. It outlines the definition of bias, the importance of identifying sensitive data, and the application of data validation methods. Key topics include the risks associated with sensitive patient data, the necessity of anonymization, and the need for continuous ethical monitoring.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/02%3A_Collecting_and_Preparing_Data/2.01%3A_Overview_of_Data_Collection_MethodsThis page outlines the systematic process of data collection crucial for data science, emphasizing the need to understand project objectives. It discusses various data collection methods like surveys ...This page outlines the systematic process of data collection crucial for data science, emphasizing the need to understand project objectives. It discusses various data collection methods like surveys and experiments, and distinguishes between observational and transactional data, each providing different insights.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/01%3A_What_Are_Data_and_Data_Science/1.00%3A_IntroductionThis page provides an overview of key terms in data and data science, illustrating their relevance in different domains. It highlights technologies utilized by data scientists, particularly emphasizin...This page provides an overview of key terms in data and data science, illustrating their relevance in different domains. It highlights technologies utilized by data scientists, particularly emphasizing Python for data analysis, and aims to lay a technical groundwork for readers to grasp and implement more complex data science concepts in future chapters.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/10%3A_Reporting_Results/10.06%3A_Chapter_ReviewThis page outlines best practices for crafting technical data science reports for varied audiences, emphasizing concise communication, avoidance of jargon, and effective use of visual aids. It highlig...This page outlines best practices for crafting technical data science reports for varied audiences, emphasizing concise communication, avoidance of jargon, and effective use of visual aids. It highlights documenting assumptions, a layered approach for accessibility, and metrics beyond accuracy. An executive summary should clearly address the business problem, methodologies, and provide a strong conclusion.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/11%3A_Appendix/11.00%3A_Appendix_A-_Review_of_Excel_for_Data_ScienceThis page discusses Microsoft Excel as a data manipulation and analysis tool, emphasizing its features, menus, and functions for analyzing datasets. It includes a guide for creating bar charts and sca...This page discusses Microsoft Excel as a data manipulation and analysis tool, emphasizing its features, menus, and functions for analyzing datasets. It includes a guide for creating bar charts and scatterplots, modifying chart elements, and performing basic statistical calculations like average and standard deviation.
- https://eng.libretexts.org/Bookshelves/Data_Science/Principles_of_Data_Science_(OpenStax)/08%3A_Ethics_Throughout_the_Data_Science_Cycle/8.07%3A_Critical_ThinkingThis page emphasizes the importance of establishing infrastructure and protocols for data sharing to enhance security, efficiency, and regulatory compliance. It highlights the role of anonymizing data...This page emphasizes the importance of establishing infrastructure and protocols for data sharing to enhance security, efficiency, and regulatory compliance. It highlights the role of anonymizing data to protect privacy and reduce breach risks. Additionally, it underscores data validation as vital for ethical usage, ensuring the accuracy, consistency, and reliability of data to prevent misinformation and maintain integrity in analyses and outcomes.
