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# Think Stats

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If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the book to help you learn the entire data analysis process—fr ...more

You'll work with a case study throughout the book to help you learn the entire data analysis process—fr ...more

Paperback, 138 pages

Published
July 22nd 2011
by O'Reilly Media
(first published January 1st 2011)

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For starters, it doesn't actually list what a student should know ahead ...more

One annoyance. I think I'm maybe the perfect audience for this book: someone who took stats long ago, has worked with data ever since in some capacity, but has moved further and further away from the first principles/fundamentals. Someone who speaks Python and wants to port all of her Stata skillz onto pandas (the Python lib ...more

You need to know Python to get the most out of this book, which really wasn't a problem for me. All code is available online and well-commented. Maybe not the best coding style, but hey he's a professor. What do you expect?

There is a bit of calculus, but it's mainly using the notation to get a concept across. Nothing to sweat over. (Do the ...more

Much of my frustration with this book can be summed by an example glossary entry: "chi-squared test: A test that uses the chi ...more

A lot of the actual python code has been abstracted by the author and put in classes and functions, making the examples easy to replicat ...more

The nice thing about it is that you go through the same prolems/datasets from one chapter to another. And you build on top of what you learned in a very cohe ...more

Also somewhat unconventional selection & sequencing of topics, as well as some atypical emphases (e.g. Cumulative Distribution Functions) for a Stats 101 program. This makes it a nice complement to traditional materials. ...more

Nevertheless, the code and syntax has little in the way of introduction and is thus interesting to read and understand, but much harder to apply from scratch (perhaps that is the intention) - perhaps by actually going through numpy, pandas and modelling packages things would be easier to apply eventually.

If you like this style of learning and like to solve interesting problems with some math and lots of coding experiments, I highly recommend Peter Norvig's Jupyter Notebooks:

http://norvig.com/ipython/README.html .

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