- Preface to the Second Edition
- Preface to the First Edition
- 1. Introduction
- 2. A Crash Course in Python
- The Zen of Python
- Getting Python
- Virtual Environments
- Whitespace Formatting
- Modules
- Functions
- Strings
- Exceptions
- Lists
- Tuples
- Dictionaries
- 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
- Welcome to DataSciencester!
- For Further Exploration
- 3. Visualizing Data
- 4. Linear Algebra
- 5. Statistics
- 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. Deep Learning
- 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
About the Author
Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com and tweets all day long at @joelgrus.
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