Tag Archives: Big data

PMean: My work on a CTSA grant

I’m on a Clincal and Translational Science Award (CTSA) research grant (5UL1TR000001-05, formerly 1U54RR031295-01A1), which is pretty cool. My name is even mentioned a few times in the grant. I thought that as I plan what I would do for this grant, I would see what the grant promised and write down what, exactly, that those promises mean. As I talk with various people (especially Russ Waitman, who is supervising my work on this grant), I will revise and update my plans. Still, I thought it would be valuable to put some thoughts down now, both to help me focus on what I should be doing and to offer an early draft of those ideas to the various people that I will end up interacting with. Continue reading

Recommended: The Origins of ‘Big Data’

I’m not a big fan of the term “big data” but I’ve been applying for a couple of jobs that ask for expertise in big data instead of expertise in Statistics. So in one of the cover letters, I wrote that I was doing big data analysis before the term was even coined. That forced me to do a quick fact check, and it looks like the term first came into wide use in the late 1990s. Here’s an article on the person who first coined the term “big data.” Continue reading

Recommended: Can A.I. be taught to explain itself

This is a nice article in the popular press that talks about some of the problems with “black box” models (in particular deep neural nets) used extensively in many big data projects. It is a bit shy on technical details, which is understandable for a paper like the New York Times. Even so, the stories are quite intriguing. This is a wake up call for those people who fail to recognize the serious problems with many big data models. Continue reading

Recommended: beanumber repository

This is the github repository of Ben Baumer. He is one of the co-authors of “Modern Data Science with R” and the data and code from that book is available here. He also provides code and data for OpenWAR, an open source method for calculating a baseball statistic, Wins Above Replacement. Finally, there is an R library for extracting, transforming, and loading “medium” sized datasets into SQL. Medium here means multi-gigabyte sized files. Related to this are a couple of “medium” sized data sets from the Internet Movie Database and from the NYC CitiBike dataset. Continue reading

Recommended: ROSE: A package for binary imbalanced learning

Logistic regression and other statistical methods for predicting a binary outcome run into problems when the outcome being tested is very rare, even in data sets big enough to insure that the rare outcome occurs hundreds or thousands of times. The problem is that attempts to optimize the model across all of the data will end up looking predominantly at optimizing the negative cases, and could easily ignore and misclassify all or almost all of the positive cases since they consistute such a small percentage of the data. The ROSE package generates artificial balanced samples to allow for better estimation and better evaluation of the accuracy of the model. Continue reading