Think Stats
by Allen B. Downey · first published 2011
64% recommend33% neutral2% critical
Why HN recommends it
Teaches statistics and probability to programmers by implementing methods in Python and working with real data. Free to read, clearer than some assigned textbooks, and a good first step before Think Bayes.
Summarised by Claude from recommending comments
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…Python : http://www.greenteapress.com/thinkpython/thinkpython.html Think Bayes : http://www.greenteapress.com/thinkbayes/index.html Think Complexity : http://www.greenteapress.com/complexity/index.html Think Stats : http://www.greenteapress.com/thinkstats/index.html
on “The Little Book of Semaphores” ↗…vision skills. Really glad to see I'm not the only one. I don't have any recommendations for linear algebra, but for stats and probability (which I always found intimidating in the past), Allen Downey's "Think Stats" and "Think Bayes" did the trick.
on “A Software Engineer’s Adventures in Learning Mathematics” ↗How does it compare/contrast with: - Wasserman's All of Statistics? (which I haven't read/worked on) - Downey's Think Stats and Think Bayes? (which I've read in bits and pieces and have found amazing) - Any other well known Stats or Probability text?
on “Stats without Tears (2016)” ↗Allen B. Downey is doing a great work concerning free text books for Computer Science (Think [Stats|OS|Bayes|Python|Complexity]). Consider a donation maybe.
on “Think OS: A Brief Introduction to Operating Systems” ↗…too. Definitely not an introduction, though – it assumes you already know how regression works and preferably have already used it quite a bit in practice. For an introduction, I might recommend Downey's Think Stats [1] for a gentle introduction, Kutner's Applied Linear Statistical Models for a more traditional, mathy introduction or Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical [2]…
on “Guide to Linear Regression (2015)” ↗…the fact that it demonstrates ways to teach math through programming and algorithms. Another book which also does a good job at using programming to demonstrate more concrete math is Allen Downey's "Think Stats" book. All through the book, you learn the mathematical concepts of statistics through hands on programming activities. There definitely is a chicken and egg problem in areas like machine learning because…
on “A Course in Machine Learning” ↗Think Stats by Allen Downey (http://www.greenteapress.com/thinkstats/index.html)
on “Ask HN: Recommendations for Statistics books?” ↗…which also includes a high school math refresher for people who need it (I did). For probability, "Probability Demystified" is a good basic intro. For statistics, I would really recommend Allen Downey's Think Stats (http://greenteapress.com/thinkstats2/index.html), especially if you're coming from a programming background. Most introductions to statistics focus heavily on the mathematics needed to enable certain…
on “Complete Course on Machine Learning” ↗…understanding of math doesn't suddenly make them any more central to a conceptual understanding of statistics than they are. Two good examples of good stats intros that are light on the math: Allen Downey's Think Stats http://greenteapress.com/thinkstats2/ or the more traditional/frequentist http://onlinestatbook.com/.
on “The Math I Learned After I Thought Had Already Learned Math” ↗For people who are interested in learning more about simulation, permutation tests and bootstrapping, the three techniques that the slide deck discusses, check out Allen Downey's Think Stats: http://greenteapress.com/thinkstats2/. It really is a pity that introductions to statistics spend so much time on analytic approximations and so little on the underlying concepts.
on “Statistics for Hackers” ↗…and Differential Forms by Hubbard. It gives an amazingly clear viewpoint on the higher level analysis and algebra topics, both numerically and abstractly. For statistics, you might try something like Think Stats by Downey which emphasizes explorations with Python, real data, and Bayesian statistics. As a faithful companion in your journey, use something like GeoGebra or Desmos to really explore the visual side…
on “Ask HN: How to self-learn math?” ↗…hardcopies but I think all of them are also just freely available. Here's a few: Think Complexity https://github.com/AllenDowney/ThinkComplexity2 Think DSP https://github.com/AllenDowney/ThinkDSP Think Stats https://github.com/AllenDowney/ThinkStats/ Think Bayes https://github.com/AllenDowney/ThinkBayes2/
on “Think Linear Algebra” ↗Seconded, not only is Downey quite generous, his books are every bit as good, if not better, than expensive counterparts. Think Stats bailed my ass out of failing a stats class because it was so much clearer than the assigned book.
on “Think Linear Algebra” ↗I recommend Think Stats by Allen Downey http://greenteapress.com/thinkstats/
on “Introduction to Statistics using NumPy” ↗…Programmer ("Think OCaml") - http://greenteapress.com/thinkocaml/index.html Think Operating Sytems - http://greenteapress.com/thinkos/index.html Think Bayes - http://greenteapress.com/wp/think-bayes/ Think Stats - http://greenteapress.com/thinkstats2/index.html Also important is his section about how to help free books: http://greenteapress.com/easy.html
on “The Little Book of Semaphores [pdf]” ↗