Preface to the Second Edition Preface to the First Edition 1. Introduction The Ascendance of Data What Is Data Science? Motivating Hypothetical: DataSciencester Finding Key Connectors Data Scientists You May Know Salaries and Experience Paid Accounts Topics of Interest Onward 2. A Crash Course in Python The Zen of Python Getting Python Virtual Environments Whitespace Formatting Modules Functions Strings Exceptions Lists Tuples Dictionaries defauhdict Counters Sets Control Flow Truthiness Sorting List Comprehensions Automated Testing and assert Object-Oriented Programming Iterables and Generators Randomness Regular Expressions Functional Programming zip and Argument Unpacking args and kwargs Type Annotations How to Write Type Annotations Welcome to DataSciencester! For Further Exploration 3. Visualizing Data matploflib Bar Charts Line Charts Scatterplots For Further Exploration 4. Linear Algebra
Vectors Matrices For Further Exploration 5. Statistks Describing a Single Set of Data Central Tendencies Dispersion Correlation Simpson's Paradox Some Other Correlational Caveats Correlation and Causation For Further Exploration …… 6. Probability 7. Hypothesis and Inference 8. Gradient Descent 9. Getting Data 10. Working with Data 11. Machine Learning 12. k-Nearest Neighbors 13. Naive Bayes 14. Simple Linear Regression 15. Multiple Regression 16. Logistic Regression 17. Decision Trees 18. Neural Networks 19. DeepLearning 20. Clustering 21. Natural Language Processing 22. Network Analysis 23. Recommender Systems 24. Databases and SQL 25. MapReduce 26. Data Ethics 27. Go Forth and Do Data Science Index
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