Think Stats
 Used Book in Good Condition
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 realworld data
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Learning Statistics using Programming,
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, realworld data examples, and a stepbystep 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.
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100 pages and couple of hours to get a good flavor of Bayesian Stats,
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!
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Good Book – Free from Authors Site,
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