data-science-from-scratch

A collection of notebooks based on "Data Science From Scratch" 2nd Edition (O'Reilly, Joel Grus)

Code and examples from the second edition of the book Data Science from Scratch. They require at least Python 3.6. To be continually updated as I progressively read the book.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In the book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

Book Chapters

1. Introduction
2. A Crash Course in Python
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. Databases and SQL
21. MapReduce