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Think Stats

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3.65  ·  Rating details ·  294 ratings  ·  31 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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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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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.
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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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.
Sefa
Jan 14, 2015 rated it really liked it
Nice book on introductory statistics for people with programming background. It fills a gap between traditional statistics books and application oriented books calling R functions without explaining what is going on under the hood. The book is freely available and all source is available on Github, under continuous improvement and correction. Exercises are in IPython notebooks, cool!
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.
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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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.
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.
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.
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Toavina
Jul 20, 2016 rated it liked it
Shelves: data-science
Interesting way of learning statistics and very helpful in reinforcing concepts.

Nevertheless, the code and syntax has little in the way of introduction and is thus interesting to read and understand, but much harder to apply from scratch (perhaps that is the intention) - perhaps by actually going through numpy, pandas and modelling packages things would be easier to apply eventually.

Franta
Mar 08, 2014 rated it really liked it
This is a computing book that teaches basic statistics concepts. Downey has a very peculiar way of explaining math and science concepts - it is purely example/experiment driven.

If you like this style of learning and like to solve interesting problems with some math and lots of coding experiments, I highly recommend Peter Norvig's Jupyter Notebooks:
http://norvig.com/ipython/README.html .

Tadas Talaikis
Mar 01, 2016 rated it it was amazing
Shelves: probabilities
Short, easy, all the basic functions you can try and see how it works. A lot of examples, definitions in few simple sentences, like it should be. Very practical. We need more such books, not just half thousand of pages of dry, mostly abstract and thus useless in real life formulas.
Mattias Lundell
Mar 19, 2012 rated it it was ok
Shelves: 2012
A little bit thin but decent as introduction.
Dgg32
Sep 04, 2013 rated it it was amazing
a little bit too short.
Peter
Nov 29, 2012 rated it liked it
I'm the wrong audience for this book. Need a more introductory text for stats then could pick this up.
Lucas
May 04, 2016 rated it liked it
It was ok. Not enough explanations on the intuition behind the different distributions and the mathematical concepts.
Cristofer
Nov 10, 2011 rated it liked it
More introdutory than I thought. First chapters can be tedious.
Tom
Feb 18, 2012 rated it liked it
Really good.
Dense clear style (reminiscent of the K&R C book) is a breath of fresh air when compared to most waffly, chatty programming books on the market.
Alex Ott
Oct 20, 2011 rated it really liked it
Shelves: math
ok for refreshment & easy reading. But it's better to make all examples in code for better understanding
Todd
rated it it was amazing
Jan 25, 2018
Hjörtur Jónasson
rated it liked it
Mar 23, 2017
Hexin
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Jun 01, 2014
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