Joel Grus: Learning Data Science Using Functional Python
PyData Seattle 2015
Everyone has an opinion on the best way to learn data science. Some people start with statistics or machine learning theory, some use R, and some use libraries like scikit-learn. I'll use several examples to contrast these with a simpler approach using functional programming techniques in Python. In addition, I'll show how even advanced data scientists can benefit from thinking more functionally.
Materials available here:
Github: https://github.com/joelgrus/stupid-it...
Slides: https://docs.google.com/presentation/...
0:00 - Introduction
0:45 - What is functional programming?
2:57 - Iterators
3:55 - Generators/Generator comprehensions
6:10 - itertools
12:30 - Fibonacci numbers example
15:56 - Prime numbers example
16:57 - K-means clustering
26:10 - Aside: Matplotlib animation for K-means
27:40 - Gradient Descent
35:17 - Linear Regression for Stochastic Gradient Descent
37:30 - End of lecture and Questions
S/o to https://github.com/stobinaator for the video timestamps!
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