Think Stats [Paperback]

Allen B. Downey (Author)

Book Description

Publication Date: July 22, 2011

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

Editorial Reviews

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.

About the Author

Allen Downey is a 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.

Product Details

• Paperback: 138 pages
• Publisher: O'Reilly Media (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 (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars  See all reviews (9 customer reviews)
• Amazon Best Sellers Rank: #65,911 in Books (See Top 100 in Books)
  • #36 in Books > Computers & Technology > Databases > Database Design
  • #43 in Books > Computers & Technology > Computer Science > Information Theory
  • #45 in Books > Computers & Technology > Computer Science > Modeling & Simulation

More About the Author

Allen Downey is a Professor of Computer Science at Olin College and a former Visiting Scientist at Google, Inc.

He is the author of Think Stats, Think Python, the How to Think Like a Computer Scientist series (in Java, C++, OCaml, and more), The Little Book of Semaphores, and other computer science books, all available free at greenteapress.com.

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.

Most Helpful Customer Reviews

41 of 42 people found the following review helpful

5.0 out of 5 stars Learning Statistics using Programming August 11, 2011

By CromsFury

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.

23 of 24 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

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!

14 of 15 people found the following review helpful

4.0 out of 5 stars Good Book - Free from Authors Site January 28, 2012

By Devin R

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.