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Think Stats [Paperback]

Allen B. Downey
3.0 out of 5 stars  See all reviews (1 customer review)
List Price: CDN$ 34.99
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Book Description

July 25 2011 1449307116 978-1449307110 1

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—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

  • Develop your understanding of probability and statistics by writing and testing code
  • Run experiments to test statistical behavior, such as generating samples from several distributions
  • Use simulations to understand concepts that are hard to grasp mathematically
  • Learn topics not usually covered in an introductory course, such as Bayesian estimation
  • Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools
  • Use statistical inference to answer questions about real-world data

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Frequently Bought Together

Think Stats + Think Bayes + Think Complexity: Complexity Science and Computational Modeling
Price For All Three: CDN$ 99.69


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Product Details


Product Description

Book Description

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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1 of 1 people found the following review helpful
3.0 out of 5 stars Nice introduction but doesn't live up to claims Oct. 17 2012
Format:Paperback
An interesting and well written book that digs into a sample of statistical problems, providing just enough theory along the way. I would call it a good 'gateway' book to applied statistics but a far cry from the elaborate claims made on the back cover. The code solutions are enough to get by on but a cleanup and decent annotation would go along way to helping this book along.
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Amazon.com: 3.9 out of 5 stars  13 reviews
61 of 62 people found the following review helpful
5.0 out of 5 stars Learning Statistics using Programming Aug. 11 2011
By CromsFury - Published on Amazon.com
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.
41 of 44 people found the following review helpful
5.0 out of 5 stars 100 pages and couple of hours to get a good flavor of Bayesian Stats March 15 2012
By Ravi Aranke - Published on Amazon.com
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!
23 of 24 people found the following review helpful
4.0 out of 5 stars Good Book - Free from Authors Site Jan. 28 2012
By Devin R - Published on Amazon.com
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.
14 of 15 people found the following review helpful
5.0 out of 5 stars a great, focused treatment of basic statistics Sept. 6 2011
By H. Smith - Published on Amazon.com
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.
16 of 19 people found the following review helpful
3.0 out of 5 stars Suitable for people with statistics and Python knowledge Dec 18 2012
By Amazon Customer - Published on Amazon.com
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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