First of all, I would like to point out that the skill of building MVP and microservices for a data scientist is extremely useful! When you can build a prototype and test it in a working environment it just feels so much better and allows you... Read more

Statistical methods are inarguably the hottest approach to evaluating datasets at scale right now. They’re not without their weaknesses though – they’re ultimately heuristic, and some methods like neural networks require tremendous amounts of data to create a well-fitted model. That’s where semantics come in. If... Read more

I like to call linear regression the data scientist’s “workhorse.” It may not be sexy, but it’s a tried and proven technique that can be very useful. When the problem you’re trying to solve requires the prediction of a numeric response variable using multiple continuous (numeric)... Read more

Joint probability, conditional probability, and marginal probability… These are three central terms when learning about probability, and they show up in Bayesian statistics as well. However… I never really could remember what they were, especially since we were usually taught them using formulas, rather than pictures.... Read more

How do you operate a data-driven application before you have any data? This is known as the cold start problem. We faced this problem all the time when I designed clinical trials at MD Anderson Cancer Center. We uses Bayesian methods to design adaptive clinical trial designs, such... Read more

Symmetric Gaussian matrices The previous post looked at the distribution of eigenvalues for very general random matrices. In this post we will look at the eigenvalues of matrices with more structure. Fill an n by n matrix A with values drawn from a standard normal distribution and let Mbe the average of A and its transpose, i.e. M =... Read more