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Think Stats

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3.62  ·  Rating details ·  385 ratings  ·  47 reviews
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—fr
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Paperback, 138 pages
Published July 22nd 2011 by O'Reilly Media (first published January 1st 2011)
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Average rating 3.62  · 
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Nathan Brodsky
The book is full of valuable insights and good, elaborate explanations. Well worth the read.
Jean-Luc
Aug 19, 2012 rated it did not like it
Most books about Statistics teach the subject w/ with pen and paper, and don't take advantage of the powerful CPUs sitting on most students' desks. Books about computing statistics assume the reader already knows the mathematical theory. This book tries to strike a happy medium: teaching students to understand data by writing programs to flesh out the computations for you. It's an ambitious book, but it doesn't entirely work.

For starters, it doesn't actually list what a student should know ahead
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Ali Izadi
Apr 09, 2020 rated it really liked it  ·  review of another edition
Practical stats with computationl approach. Good book to start using statistics in your data analysis problems.
Angela
May 04, 2016 marked it as did-not-finish
Shelves: hard-sciences, tech
It's a textbook. A good one. I didn't finish it. Wiping the slate clean! I saw Allen Downey give a talk on Bayesian stats, and it was fun and informative. I think he's great.

One annoyance. I think I'm maybe the perfect audience for this book: someone who took stats long ago, has worked with data ever since in some capacity, but has moved further and further away from the first principles/fundamentals. Someone who speaks Python and wants to port all of her Stata skillz onto pandas (the Python lib
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Sergey Shishkin
Jun 20, 2016 rated it really liked it
Very comprehensible introduction into computational statistics. Minus one star for code examples: Wrapping numpy, pandas and scikit into a class-oriented API made the examples rather harder to understand. I'd rather prefer the examples to re-implement library methods in plain Python first and then point to the library functions.
Nancy
Apr 23, 2015 rated it it was ok
While I'm only halfway through this book, it teaches neither statistics nor tips/tricks with Python libraries. The github source code that accompanies the book is probably more useful as a reference than the book. I recommend a book that focuses on one or the other. This is interesting to flip through.
Utsav Parashar
Jun 11, 2019 rated it really liked it  ·  review of another edition
Good Book to start with about stats.
basic knowledge of python will be useful.
André Hagenbruch
Dec 26, 2011 rated it really liked it
Although this is just a slim volume you will profit most from it if you have the time to do the exercises and follow the many pointers (often from Wikipedia) to the full explanations. After that you should have a pretty good grasp of topics like distributions, probabilities, and hypothesis testing...
Maged M.
Jan 06, 2018 rated it really liked it
Shelves: stats
thinking like a stats. I like the book structure. How Allen introduce several stats in the books through one problem.
Derek Bridge
May 04, 2019 rated it really liked it
My quest for a really helpful stats book goes on. Because this isn't it.

Now, that's a more severe judgment than I intend because there were parts of this book that were helpful and deepened my understanding.

In most stats books, I find it difficult to separate the material that explains the stats concepts from the material (if any - since this is always under-represented) that explains how to do stats (i.e. how to analyse a dataset or how to analyse the results of an experiment). This book is no
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Len MacRae
This book seems to have a very narrow use case. It's designed as a textbook for "an introduction to the practical tools of exploratory data analysis." Do not expect anything more. This is not the book for someone trying to learn statistics or trying to learn Python. I can see it having value within a course or as a supplement to other material but limited value elsewhere.
Much of my frustration with this book can be summed by an example glossary entry: "chi-squared test: A test that uses the chi
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Zach
Mar 20, 2018 rated it liked it  ·  review of another edition
Shelves: programming
This was a good look at some different prediction / modeling methods through simulation and re-sampling, but leaves many useful analytic methods of determining the same information for the last chapter. It would've been nice to have that presented alongside the initial information with simulations and re-sampling guiding an understanding of the analytic methods.

A lot of the actual python code has been abstracted by the author and put in classes and functions, making the examples easy to replicat
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Yahia El gamal
Jul 08, 2018 rated it liked it
Shelves: data-related
Very nice book. It's different from what you usually get in that area. I would describe as a modern introduction book of stats. Modern because it focuses on computational methods (e.g. starts with bootstrapping to calculate confidence intervals of the mean instead of analytical methods). It doesn't go very deep but it covers a lot of things.

The nice thing about it is that you go through the same prolems/datasets from one chapter to another. And you build on top of what you learned in a very cohe
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Michelle
Overall a clear, easy to follow intro to a variety of introductory topics in statistics with code snippets provided in Python.

My primary gripe is that the code snippets frequently use functions that are unexplained before they are used, or IMO unnecessarily introduce the use of OOP, which only makes following along more difficult.

Formatting-wise, I think the book would also benefit from adding syntax highlighting (unless that was just SafariBooks), PEP8 compliant function naming, and the flavor
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Mohit Aneja
Dec 17, 2019 rated it it was ok
Disclaimer: I didn't finish the book.

Although it is a good beginner level book for practical statistics, the author uses too many "thinkplot" libraries every now and then to explain the concepts. It made it a lot harder to interpret the actual real-life implementation of those functions since I have worked with Pandas, Numpy and Matplotlib libraries before. It'd have been better if the examples used raw Python code used in actual data science applications.
Kenta Suzuki
Jun 07, 2017 rated it really liked it
A good book for a programmer. This book teaches you stats in application, not theory or mathematical equation or proof which most of the textbooks present. If this book contained the instruction on how to do stats with numpy rather than pre-defined function by the author, this would be a five star book.
Ferhat Culfaz
Not much detail. Good simple explanations, but overall too simplistic and lacks depth. Plus a lot of the functions the author uses he wrote himself. It’s perhaps better to stick to the established libraries such as pandas and statsmodels to do similar work.

So overall, a bit too basic.
Yaroslav
May 21, 2019 rated it did not like it
Why you need to create a book, where you in each chapter gives the reader an opportunity to read this on wikipedia? Good book for professional statisticians who wants to revise the basics. It's not appropriate structure for the book, if the main goal to make some introduction for begginers.
Pritesh Shrivastava
Jul 15, 2019 rated it liked it
Had to skip some portions of the book.

One major disadvantage I found was that instead of using standard Python packages like Scipy, the examples include a lot of custom built functions and packages which make them less generalizable.
Máté Gulyás
Apr 04, 2020 rated it it was ok
Shelves: 2020
70% of the book is the description or API documentation of the author's library. I respect Allen B. Downey, I think he is good with explanations but this book would be much much better with the libraries we use in practice.
Eduardo Monteiro
Sep 14, 2017 rated it it was amazing
Outstandingly easy to read and learn basic statistics concepts with good and clean python code.
Maria Nicopolis
Apr 26, 2018 rated it liked it
I was looking for more important answers to some of the questions I had and this book was not the one because the answers I had were not mentioned like I would have thought they were.
Sweemeng Ng
Jan 20, 2019 rated it really liked it
Good book on statistical techniques to software developer
Aditya Mehta
Feb 02, 2020 rated it liked it
Quite summarized content, not comprehendable at times. One must be having deep-diving knowledge of statistatics before being able to code or think programmatically.
Mlv Prasad
Jan 19, 2020 rated it liked it
This review has been hidden because it contains spoilers. To view it, click here.
Daniel
Nov 18, 2017 rated it really liked it
Shelves: data-stats
Nice brief, concise, fairly intuitive intro to statistical fundamentals from the perspective of a programmer. Mathematical concepts & methods are illustrated & exemplified with python code as well as concrete examples, problems & traditional mathematical notation.

Also somewhat unconventional selection & sequencing of topics, as well as some atypical emphases (e.g. Cumulative Distribution Functions) for a Stats 101 program. This makes it a nice complement to traditional materials. Not sure how ef
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White Rose
Jul 14, 2019 rated it really liked it
This review has been hidden because it contains spoilers. To view it, click here.
Louis
Sep 20, 2011 rated it really liked it
Shelves: math-stats, computer
Statistics gets a little respect in Operations research, in part because it gets taught as a bunch of formulas and computer procedures. And the problem with the way that it is taught is that the formulas don't mean anything, and the student may know her way around menus, but that does not mean that she knows under what circumstances to use what method. And everything is learned in isolation, often without practice in getting her hands dirty. Think Stats gives students the chance to get their han ...more
Todd N
Aug 23, 2012 rated it really liked it
Shelves: big-data
Very good overview of statistics. It's really more of a self-study course than a book, which is why I'm going through it again with my computer's IDE fired up.

You need to know Python to get the most out of this book, which really wasn't a problem for me. All code is available online and well-commented. Maybe not the best coding style, but hey he's a professor. What do you expect?

There is a bit of calculus, but it's mainly using the notation to get a concept across. Nothing to sweat over. (Do the
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Allen Downey is a professor of Computer Science at Olin College and the author of a series of open-source textbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. in computer science from U.C. Be ...more

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