Examples from the book and a few other hacks inspired by Joel Grus' Data Science from Scratch.

• Joel's github repo

I had a lot of fun with this book. Data science lends itself to the hacker's approach of diving in and getting your hands dirty with a breadth of topics.

That said, the book gives a fly-over view of some fairly deep subjects, leaving the reader with a good lay of the land but also an intimidating sense of how much there is left to learn.

I had little problem using Python 3 to work through the book even though it's done in Python 2. You'll have to add some parentheses here and there and be aware that map is a generator in Python 3. The 2nd Edition is fully Python 3 and in the works now.

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. Clustering
20. Natural Language Processing
21. Network Analysis
22. Recommender Systems
23. Databases and SQL
24. MapReduce

• Joel's Jupyter-con talk I don't like notebooks and slides.

• Building a deep learning library

• 100+ Interesting Data Sets for Statistics by Robb Seaton

I divided things up by chapter, which makes imports difficult. You'll have to do some ridiculous thing like this:

export PYTHONPATH=./chapter_01:./chapter_03:./chapter_04:./chapter_05:./chapter_06:./chapter_07

or this:

import os.path
import sys
book_dir = '/Users/CBare/Documents/projects/data-science-from-scratch'
sys.path.extend(os.path.join(book_dir, 'chapter_{:02d}'.format(i)) for i in [3,4,5,6,7])