- 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: 14 customer reviews
- Amazon Best Sellers Rank: #1,117,779 in Books (See Top 100 in Books)
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Think Stats 1st Edition
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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.
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Top customer reviews
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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.
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.
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!
Finally, when an author provides a great book for free I think it is the responsibility of us readers to support the author by purchasing the texts. It is an extraordinary effort to write a good book and develop solid, working code -- let alone a great book.
Thank you Prof. Downey !! Please also consider writing a book using python for more advanced analytics (i.e. PCA, discrimination, classifications, clustering, etc ).