11: Appendix

Principles of Data Science · LibreTexts

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    From LibreTexts, under CC BY 4.0, as the source states it; its equations are written in TeX, which the PDF shows typeset. This page at LibreTexts.

    • 11.0: Appendix A- Review of Excel for Data Science
      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.
    • 11.1: Appendix B- Review of R Studio for Data Science
      This page discusses R, an open-source statistical tool favored in data science for data exploration and visualization. It covers basic commands for creating visualizations like scatter plots and performing statistical analyses such as correlation and regression, using examples related to S&P 500 stock returns and other datasets.
    • 11.2: Appendix C- Review of Python Algorithms
      This page provides a comprehensive summary of Python algorithms from the textbook, offering a cross-reference for students. It covers algorithms related to data handling, visualizations, statistical analysis, machine learning, and neural networks, detailing their applications and relevant text sections. The summary highlights various functionalities, including data scraping and modeling techniques.
    • 11.3: Appendix D- Review of Python Functions
      This page provides an overview of Python functions and machine learning methods. It includes Python functions for data handling, statistical analysis, and visualization, with examples for library usage. Additionally, it categorizes machine learning techniques, covering algorithms for forecasting, classification, regression, and neural networks, along with tools for model training, evaluation, and data preprocessing.

    This page titled 11: Appendix was last modified on Tue, 22 Apr 2025 07:30:16 GMT and is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by OpenStax via source content that was edited to the style and standards of the LibreTexts platform.