I pulled out a dusty copy of Thinking Stats by Allen Downey the other day. I highly recommend this terrific little read that teaches statistics with easily understood examples using Python. When I purchased the book eight years ago, the Python code proved invaluable as…

In my many years as a data scientist, I've spent more time doing forecast work than any other type of predictive modeling. Often as not, the challenges have involved forecasting demand for an organization's many products/lines of business a year or more out based on five or more years of actual data, generally of daily…

A little less than a year ago, I posted a blog on generating multivariate frequencies with the Python Pandas data management library, at the same time showcasing Python/R graphics interoperability. For my…

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 can one progress from noting a correlation between factors…

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

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 college athletic conferences. The …

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 embellish that data, calculating individual school ranks from scores…

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 performance based on hard numbers. In separate rankings, Division I,II, and…

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 asked if I'd ever used the R …

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 attempted to demonstrate the potential of working…