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Think Stats 1st Edition

4 out of 5 stars 14 customer reviews
ISBN-13: 978-1449307110
ISBN-10: 1449307116
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Editorial Reviews

Book Description

Probability and Statistics for Programmers

From the Author

I wrote this book for a class I developed at Olin College.  The goal of the class is to teach students to use statistical tools to explore real datasets and answer interesting questions.  The webpage for the class is here: sites.google.com/site/thinkstats2011a --- it includes my lecture notes, in-class exercises, homeworks, etc.
The examples in this book are in Python, but it is a simple subset of Python.  If you have read the first 14 chapters of Think Python, you are ready to go.
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Product Details

  • Paperback: 138 pages
  • Publisher: O'Reilly Media; 1 edition (July 22, 2011)
  • Language: English
  • ISBN-10: 1449307116
  • ISBN-13: 978-1449307110
  • Product Dimensions: 7 x 0.5 x 9.2 inches
  • Shipping Weight: 8.8 ounces
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (14 customer reviews)
  • Amazon Best Sellers Rank: #257,719 in Books (See Top 100 in Books)

More About the Author

Allen Downey is a Professor of Computer Science at Olin College and author of Think Python, Think Stats, Think Bayes, Think Complexity, and several other computer science books. The idea behind these books is that if you know how to program, you can use that skill to learn other things.

Allen is an avid runner, gardener and cook. He ran the Boston Marathon for the first time in 2011, finishing in 3:45. Allen lives in Needham, MA with his wife, two daughters, and two cats.

Customer Reviews

Top Customer Reviews

Format: Paperback
If your grasp of Programming exceeds your understanding of Basic Statistics, this book IS for you. As a University Statistics professor, I am constantly looking for reading materials that I can use to integrate Practical Statistics with programming. I am generally faced with the problem of having to mine Programming texts for Stats lessons, all too often I am faced with books that attempt to teach a programming language with examples from Freshman Statistics as an afterthought. (Too much of one, not enough of the other)

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.

Caution:
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.
1 Comment 75 of 76 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Format: Paperback
Bayesian statistics and Bayesian thinking has taken the world by storm. If you read Kahneman's popular
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!
Comment 45 of 48 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Format: Kindle Edition
This is a good book to teach programmers [python especially] how to use mathematical statistics in their programs. The only real shame about the Kindle version of this book is it is available for free under the creative commons from the publisher, Greenteapress, for free but it's being sold here for a 10 spot.
2 Comments 26 of 27 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Format: Paperback Verified Purchase
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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Format: Paperback
When I first looked at the ToC, I was glad, because the book promised interesting topics. When I saw the definition of Variance in the beginning - I was even happier, because I thought that those interesting topics will be explained thoroughly.
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.
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