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:

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

[codingcharlatan]

Immutability slide with Ninja Turtles/Vanilla Ice GIF was well done

[ArpadHorvathSzfvar]

It's wonderful. Fortunately accumulate, iterate, take, drop, tail is defined in the toolz library, but it is worth understanding how could I implement them.

[alexbond]

absolutely amazing. Thank you for this wonderful presentation.

[mamahuhu_one]

It would be interesting to see how to solve dynamic programming problems in python in a functional programming

[worstyearever]

can someone point me to a how to do get the gif to work?

[RAHUDAS]

Good, learn something meaningful ful in first 15 minutes