Improving data quality in data analytics & machine learning (Udemy.com)

This is a short, mostly conceptual course on data quality, taught by Mike X Cohen, a former neuroscience professor. It spends its first hour and a half on why quality matters and how to plan for it, then moves into assessing data and cleaning it. The Python code is optional and appears in the lectures that use pandas, numpy, seaborn and matplotlib. It suits analysts, students and the managers who work with them.

One reviewer calls the explanations easy to follow and describes the course as a nice browse through data quality and data preparation topics. Another praises the clear explanation of statistical themes. Of 1,166 ratings, 644 are five stars and 405 are four, so about nine in ten are positive. The complaints are small. Ninety-seven people gave three stars, and the one lukewarm review in our sample wanted Portuguese audio. The syllabus lists four quizzes and no coding exercises, so the code is something to watch more than practice.

At 5.4 hours across 45 lectures, it is quick to finish, and the last update was in August 2026, so it is current. The longest sections, on assessing data and on transformations, run about 90 minutes each. The syllabus and that 'browse' review point to a tour more than a workshop: it points to methods and shows them, but it will not turn a beginner into a cleaning expert. Worth it as a first pass at the topic, less so for anyone who wants heavy hands-on wrangling.