Sunday, July 03, 2016

thinkstats-examples - my answers to Think Stats exercises

Josh Wills famously described a data scientist as someone better at statistics than a software engineer and better at software engineering than a statistician. My background is in software engineering, so I am always looking for ways to get better at statistics. Recently I was watching some PyCon videos on Youtube, and came across Prof. Allen B Downey's Bayesian Statistics Made Simple talk at PyCon 2015.

I found the approach quite unique - instead of proving theorems, he creates programs that simulate the setup using random data, and then uses the results to provide an intuition about the behavior the theorem describes. The talk was about Bayesian statistics, which he covers in detail in his book Think Bayes. He also mentioned one of his other books Think Stats, which is aimed at someone who is more programmer and less statistician. Unfortunately, even with the computational approach, I didn't quite fully understand his talk. So I decided to fix that by working my way through the two books. This post describes some notebooks I created as a result of working through the Think Stats book.

The notebooks have been uploaded to this Github repository and contains the following Jupyter (aka IPython) notebooks.

The examples in the book build up, chapter by chapter, a library of functions written in pure Python. Later functions call earlier functions, and their usage is almost like a Domain Specific Language (DSL). Since I have been using the Scientific Python stack (numpy, scipy, matplotlib, pandas, etc) for a while now, I decided to skip the DSL and use the libraries from the Scientific Python stack instead. Although there were times I wished I hadn't done so, I think overall it was the right choice for me, since it allows me to apply the concepts directly to my own projects without having to go through the DSL. Of course, YMMV.

One other thing that this mini-project has helped me with is becoming really good at writing LaTeX in Markdown :-). I started using the online LaTeX equation editor and copy-pasting the LaTeX into my notebook, but somewhere around Chapter 4, I developed the ability to just write the equations directly into the notebook. I think writing the equations this way helps make them much more readable, so acquiring this skill was a nice side effect.

The one caveat is that at least some of the answers are very likely to be incorrect. While I have tried to ensure that they are correct to the best of my ability, I am not an expert by any stretch of the imagination, and there were quite a few times when I found the material in the book pretty hard to go through. If you do find an error, please create an issue and tell me why I am wrong and preferably provide a correct answer, I will update the example and give you credit.

Thats all I have for today, hope you find the examples useful. At some point in the (hopefully near) future, I plan on doing something similar for the Think Bayes book as well. For those of you in the US, have a great 4th of July!

2 comments (moderated to prevent spam):

Zuriati Wenger said...

I was wondering how do you recommend I approach this book, as someone who has limited programming experience in Python. I like the style and the way he's trying to teach the stats with a real data. However, I cant get pass the first 2 chapters, knowing that I cannot figure out his Python code. What do you suggest?
Or is there an easier coding book with the same style as Allen B.Downey? Or I should just bite the bullet and learn the codes?

Thanks for your inputs!

Sujit Pal said...

Hi Zuriati, I am probably biased but I would recommend running the code as you read. Python is an easy language to learn, and since you don't care about using numpy+scipy+pandas+matplotlib as I was, you can follow his book more closely, so use the code provided verbatim and put in print statements to see what is happening as it runs.