Allen Downey

Allen Downey

Author of Think Bayes & Think Python

Bio: I am a Professor of Computer Science at Olin College in Needham MA, and the author of Think Python, Think Bayes, Think Stats and several other books related to computer science and data science. Previously I taught at Wellesley College and Colby College, and in 2009 I was a Visiting Scientist at Google, Inc. I have a Ph.D. from U.C. Berkeley and B.S. and M.S. degrees from MIT. Here is my CV. I write a blog about Bayesian statistics and related topics called Probably Overthinking It. Several of my books are published by O’Reilly Media and all are available under free licenses from Green Tea Press.

Last batch of notebooks for Think Stats

Last batch of notebooks for Think Stats

Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  Each chapter has a notebook that shows the examples from the book along with some small exercises, with more substantial exercises at the end. If you are reading the book, you can get the notebooks by cloning this […]

Another batch of Think Stats notebooks

Another batch of Think Stats notebooks

Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  When I am done, each chapter will have a notebook that shows the examples from the book along with some small exercises, with more substantial exercises at the end. If you are reading the […]

Third batch of notebooks for Think Stats

Third batch of notebooks for Think Stats

As I mentioned in the previous post and the one before that, I am getting ready to teach Data Science in the spring, so I am going back through Think Stats and updating the Jupyter notebooks.  I am done with Chapters 1 through 9 now. If you are reading the book, you can get the notebooks by cloning this repository on […]

Python as a way of thinking

Python as a way of thinking

This article contains supporting material for this blog post at Scientific American.  The thesis of the post is that modern programming languages (like Python) are qualitatively different from the first generation (like FORTRAN and C), in ways that make them effective tools for teaching, learning, exploring, and thinking. I presented a longer version of this argument […]

More notebooks for Think Stats

More notebooks for Think Stats

More notebooks for Think Stats As I mentioned in the previous post, I am getting ready to teach Data Science in the spring, so I am going back through Think Stats and updating the Jupyter notebooks.  I am done with Chapters 1 through 6 now. If you are reading the book, you can get the notebooks by cloning this […]

New notebooks for Think Stats

New notebooks for Think Stats

Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  When I am done, each chapter will have a notebook that shows the examples from the book along with some small exercises, with more substantial exercises at the end. If you are reading the […]

Millennials are Less Likely to Divorce

Millennials are Less Likely to Divorce

Millennials are getting married later than previous generations, as I wrote about here.  But the ones who get married are no more likely to divorce during the first 10 years, and after that they might be substantially less likely to get divorced. The following figure shows estimates for the fraction of people who have not […]

Millennials Are Still Not Getting Married

Millennials Are Still Not Getting Married

Last year I presented a paper called “Will Millennials Ever Get Married?” at SciPy 2015.  You can see video of the talk and download the paper here. I used data from the National Survey of Family Growth (NSFG) to estimate the age at first marriage for women in the U.S., broken down by decade of […]

It’s a Small World, Scale-free Network After All

It’s a Small World, Scale-free Network After All

Real social networks generally have the properties of small world graphs (high clustering and low path lengths) and the characteristics of scale free networks (a heavy-tailed degree distribution). The Watts-Strogatz (WS) network model has small world characteristics, but the degree distribution is roughly normal, very different from observed distributions. The Barabasi-Albert (BA) model has low […]