Terran Melconian and I recently published a piece on KDNuggets that clarifies some important distinctions between machine learning and data science and discusses implications for job seekers. As two experienced data science leaders, we wrote this article to provide a underrepresented perspective on the widespread misunderstanding that machine learning is the best education for early…

I was recently interviewed by entrepreneurial B2B digital marketing strategist Greg Allbright for the third episode of his newest podcast, After Hours Marketing. The episode, titled “The Subtle Differences Between Reporting and Analytics,” follows this recent post on the subject. We also touched on the CRAPOLA design principles for quantitative data. If you’d like to…

It is a story as old as data science: company has valuable data asset, company hires data scientist, data scientist finds insights, company is thrilled, company can’t figure out what to do next. The months after a company’s data first goes under the microscope can be an exciting time, but as initial goodwill fades the…

Big Mark, a new manager at Statistical Misconception Corporation, recently hired his first ever direct report, Little Mark. Little Mark turned out to be a star contributor. The company’s CEO, Biggest Mark, noticed one of Little Mark’s landmark accomplishments, and quickly earmarked another hire for Big Mark. However, he remarked, “You’ve really lucked out with…

Early in my career, I produced many data visualizations for a senior executive. Let’s call him Gordon. Gordon is unabashedly a man of strong convictions. One of his most strongly and repeatedly voiced was that, in any data visualization, the color green had to represent good and the color red had to represent bad. And…