Think Stats Paperback – Jul 25 2011
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Probability and Statistics for Programmers
About the Author
Allen Downey is an Associate Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master’s and Bachelor’s degrees from MIT.
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This book comes at the problem from the other side. Given that you already have a healthy grasp on programming and are trying to learn Statistics, each topic is presented with helpful, real-world data examples, and a step-by-step explanation of how to code the solutions. That makes this book excellent supplementary material for a Statistics class, or at the very least, a wonderful refresher for those returning to Statistics, with programming in mind.
This book is NOT for you if you do NOT have a basic understanding of Programming. This book will NOT teach you to program using statistics. It is meant to teach you statistics using programming.
Thinking, Fast and Slow, you are advised to think in Bayesian terms viz. to adjust your prior beliefs in light of new evidence.
However, there is a big gulf between knowing what you should do and actually being able to do Bayesian statistics in a mathematically correct way. The language of probability and ability to manipulate the algebra of probability statements is a prerequisite and that has some steep learning curve.
Fortunately, thanks to Allen Downey, you are in luck if you know some python programming. (If not, just pick up a copy of Think Python: An Introduction to Software Design by the same author). The best part of this book is that is thin - running at just over 100 pages, you can work through it over a weekend. Better still, you can watch the author delivering an interactive seminar and just follow along. Search for 'Bayesian statistics made (as) simple (as possible)' on youtube.
When he says that it is Bayesian Statistics made as simple as possible, that is no exaggeration.
As some of the reviewers have mentioned, Allen Downey has kindly made this book, as well as few other books, freely available on his site. Hats off to you, Sir!
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.
It gave me even greater joy to see Python examples, because it is the language I love and use daily.
But later on I was disappointed by the content.
First - the author probably comes from C++/Java/C# world - his Python code shows a clear OOP structure. It's not really accepted in Python world and the code is tough to read (Even considering my heavy coding experience)
Second problem - author jumps from completely basic level to some advanced assumptions. For example page 26 - I don't know what author meant by unbiasing function and how to do it. Even the sample code did not reveal me author's intentions.
I give a rating 3 because this book is a good place to start from (But you need to have prior knowledge and be ready to search/study stuff on your own), but not enough to cover the topics.