I recently came across an interesting account by a practical data scientist on how to munge 25 TB of data. What caught my eye at first was the article's title: "Using AWK and R to parse 25tb". I'm a big R user now and made a living with AWK 30 years ago as a budding data analyst. I also empathized with the author's recountings of…

Despite the consuming controversy surrounding his presidency, POTUS 45 has been able to secure solid ratings on the performance of the economy over his so-far 30-month administration. And he certainly isn't bashful about taking credit for the successes, opining loudly and often that his tax cuts and de-regulation initiatives…

Last time I wrote on using Python/Pandas as an adjunct to loading PostgreSQL tables. In this sequel, I demo how R can be used to collaborate with the database in…

I enjoy data prep munging for analyses with computational platforms such as R, Python-Pandas, Julia, Apache Spark, and even relational databases. The wrangling cycle provides the opportunity to get a feel for and preliminarily explore data that are to be later analyzed/modeled.

A critical task I prefer handling in computation over database is…

After posting my most recent blog using …

I recently downloaded a 5 year Public Use Microsample (PUMS) from the latest release of the American Community Survey (ACS) census data. The data contain a wealth of demographic information on both American households and…

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…