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Showing 1-6 of 6 reviews(Verified Purchases). See all 14 reviews
on September 6, 2011
What I like about Think Stats is that it is direct and to the point. It includes a case study that runs through the book and works on data available online. It provides a great starting point for exploring once you see how the given examples work. Each chapter has a handful of exercises that can get you started if you aren't sure what to do next. Downey has an easy style of writing and finds the fine line between enough information and too many details. That said, this book might be a bit thin if you don't have any experience with statistics or have access to a mentor.

Keeping in mind the that the book is a focused overview, it certainly supports the programmer who is looking for hands-on examples but I believe it also is useful for the non-programmer that needs a quick understanding of the core concepts. They may not be able to do the calculations but they will be able to participate in a conversation.

As it's concise and has active examples, the book would be a great supporting text for a course that requires assumes some statistics experience but doesn't need the overhead of a full-blown stats book. As I have mentioned in other reviews, this book is a good addition to the O'Reilly collection of books on data mining - Segaran's Programming Collective Intelligence: Building Smart Web 2.0 Applications, Russell's Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, and Janert's Data Analysis with Open Source Tools.
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on June 28, 2014
I was disappointed by this book. It purports to present stats in the Python environment, as a starter for people who are light in stats and heavy in Python. It's a thin book, but that would be fine if it delivered on its premise. The data he uses is not obtainable by a quoted link and the code in the book amounts, over many pages, to under 100 lines of Python. The statistics are bare-bones definitions that are quite well covered, better covered, in other books.

I was especially interested in the book because he promised to make some of the arduous formulae more understandable in the prospect of code. No code for those parts though.
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on November 28, 2012
I love this book because of the way it clarifies Bayesian statistics.

Allen is an excellent teacher but I only give 4 stars because Think Stats is more of a guide book to the material on his websites rather than a self contained teaching volume.

Professor Downey is very clear that without knowing Python you will struggle with the examples.

You need to know Python or at least be able to read it or the examples will not make sense.
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on August 19, 2014
Excellent intro to statistics. Well written, easy to follow. Similar to 'Think Bayes'. The same author, the same high quality!
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on December 10, 2014
Worked Perfectly
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on April 1, 2014
I should have stayed with the free version. Not much help as it is more a lay person's description of stats rather than a wealth of python thrown at stats.
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