Last time, I posted Part 2 of a blog trilogy on data programming with Python. That article revolved on showcasing …

Last week I posted the first of a three-part series on basic data programming with Python. For that article, I resurrected scripts written 10 years ago that deployed core Python data structures and functions to assemble a Python list for…

I had an interesting discussion with one of my son's friends at a neighborhood gathering over the holidays. He's just reached the halfway point of a Chicago-area Masters in Analytics program and wanted to pick my brain on the state of the discipline.

Of the four major program foci of business, data, computation, and algorithms, he acknowledged…

Like most Chicago football fans, I was pretty distraught after the Bears lost last Sunday's playoff game courtesy of a missed field goal at the end -- a kick that first hit the goalpost and then the crossbar before ultimately failing miserably. While most local fans were grief-stricken like me, some were irrationally inconsolable, demanding the…

After posting my last blog, I decided next to do a 2-part series on …

A So-So Second Date with Julia

A few months ago, I wrote a quite positive blog on the Julia analytics language,…

I started a series on causal inference for data science a few weeks back. I think CI methodologies offer great potential for the DS discipline, given that much of our data is observational i.e. outside…

For those who follow the stock market, October's been a pretty rough month, with overall market levels, as measured by major indexes such as the Russell 3000 and the Wilshire 5000, now down into correction territory of 10 percent declines. The falls, unfortunately, closely follow a …

I've spent time over the last few months attempting to enhance my skills in the statistical sub-field of causal inference.

Overly simplified, causal inference comprises a series of methodologies and techniques to assist analysts in making the jump from association or correlation to cause and effect. How…

A poll released recently showed Python increasing its lead over R as the language of choice for analytics professionals. Setting aside questions of the…

This is the third and (I promise) last of the series "A Little College Sports Analysis", wherein I attempt to use data from the Learfield Directors' Cup to evaluate the prowess of…

Last time, I wrote on wrangling data from a pdf file to assemble a data set of D1 college athletic performance in the Learfield Directors' Cup competition. In this blog, I…

I'm a big college sports fan, especially active in debates about which D1 conference is best. Five years ago, I came across the Learfield Directors' Cup, an annual evaluation/ranking program of college sports…

After my last blog on the use of relational databases PostgreSQL and MonetDB to help compensate for R's RAM limitations, I received an email from a reader who…

In PostgreSQL, MonetDB, and Too-Big-for-Memory Data in R -- Part I, I began to discuss how data that was too big for RAM is handled in R, a memory-constrained statistical platform. I…

In a blog from three months ago, I wrote on "kind of" big data in R. The "kind of" was meant as a caveat that data size in R is limited by RAM. I also mentioned the potential of working with relational data stores for…

My daughter just started a business analytics Master's program. For the probability sequence of the core statistics course, one of her assignments is to calculate the probability of single 5…

I've got a big digital mouth. Last time, I wrote on frequencies using R, noting cavalierly that I'd done similar development in Python/Pandas. I…