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Contents

  1. Textbook Information

  2. I. Main Body

    1. 1. 1. What is Data Science? — Computational and Inferential Thinking

    2. 2. 1.1. Chapter 1: Introduction — Computational and Inferential Thinking

    3. 3. 1.1.1. Computational Tools — Computational and Inferential Thinking

    4. 4. 1.1.2. Statistical Techniques — Computational and Inferential Thinking

    5. 5. 1.2. Why Data Science? — Computational and Inferential Thinking

    6. 6. 1.3. Plotting the classics — Computational and Inferential Thinking

    7. 7. 1.3.1. Literary Characters — Computational and Inferential Thinking

    8. 8. 1.3.2. Another Kind of Character — Computational and Inferential Thinking

    9. 9. 2. Causality and Experiments — Computational and Inferential Thinking

    10. 10. 2.1. Observation and Visualization: John Snow and the Broad Street Pump — Computational and Inferential Thinking

    11. 11. 2.2. Snow’s “Grand Experiment” — Computational and Inferential Thinking

    12. 12. 2.3. Establishing Causality — Computational and Inferential Thinking

    13. 13. 2.4. Randomization — Computational and Inferential Thinking

    14. 14. 2.5. Terminology and Further Reading — Computational and Inferential Thinking

    15. 15. 4. Data Types — Computational and Inferential Thinking

    16. 16. 4.1. Numbers — Computational and Inferential Thinking

    17. 17. 4.2. Strings — Computational and Inferential Thinking

    18. 18. 4.2.1. String Methods — Computational and Inferential Thinking

    19. 19. 4.3. Comparisons — Computational and Inferential Thinking

    20. 20. 6. Tables — Computational and Inferential Thinking

    21. 21. 6.1. Sorting Rows — Computational and Inferential Thinking

    22. 22. 6.2. Selecting Rows — Computational and Inferential Thinking

    23. 23. 6.3. Example: Population Trends — Computational and Inferential Thinking

    24. 24. 6.4. Example: Sex Ratios — Computational and Inferential Thinking

    25. 25. 7. Visualization — Computational and Inferential Thinking

    26. 26. 7.1. Visualizing Categorical Distributions — Computational and Inferential Thinking

    27. 27. 7.2. Visualizing Numerical Distributions — Computational and Inferential Thinking

    28. 28. 7.3. Overlaid Graphs — Computational and Inferential Thinking

  3. Appendix

Computational and Inferential Thinking: The Foundations of Data Science

Computational and Inferential Thinking: The Foundations of Data Science#
2nd Edition by Ani Adhikari, John DeNero, David Wagner.This text was originally developed for the UC Berkeley course Data 8: Foundations of Data Science.

You can view this text online or view the source.

The contents of this book are licensed for free consumption under the following license:
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

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Icon for the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Computational and Inferential Thinking: The Foundations of Data Science Copyright © by Ani Adhikari; John DeNero; and David Wagner is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, except where otherwise noted.

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