How to Play Fantasy Sports Strategically (and Win)
Daily Fantasy Sports is a multibillion-dollar industry with millions of annual users. The Imperial College Business School’s Martin Haugh created a framework to best those users by modeling what they’ll do and constructing a team based on it. Haugh presented his research on how to play Fantasy sports strategically... Read more
Thomas Wiecki of Quantopian on ‘Minding the Gap’ Between Statistics and Machine Learning at ODSC Europe 2018
Key Takeaways: It’s important for data scientists to understand the so-called “gap” between statistics and machine learning, and how there actually is a lot of commonality between the two; it’s just a matter of how you look at things. PyMC3 is a very useful probabilistic programming framework for Python.... Read more
Exploring the Central Limit Theorem in R
The Central Limit Theorem (CLT) is arguably the most important theorem in statistics. It’s certainly a concept that every data scientist should fully understand. In this article, we’ll go over some basic theory of the CLT, explain why it’s important for data scientists, and present some R code that... Read more
Mine Like Amazon with Market Basket Analysis
Pattern mining is an incredibly simple but powerful technique for discovering cooccurrences in large datasets. The most common approach to find those patterns is Market Basket Analysis, which is frequently pointed out as the method Amazon leverages for their “users also purchased” feature. Of course, that’s a dramatic oversimplification.... Read more
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... Read more
Statistics, Simians, the Scottish, and Sizing up Soothsayers
A predictive model can be a parametrized mathematical formula, or a complex deep learning network, but it can also be a talkative cab driver or a slides-wielding consultant. From a mathematical point of view, they are all trying to do the same thing, to predict what’s going to happen,... Read more
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... Read more
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... Read more