IBM
IBM Data Science Professional Certificate
Twelve courses covering the applied data science workflow end to end. Methodology first, then Python and SQL, analysis and visualisation, machine learning with Scikit-learn, and a generative-AI module, closing on a capstone that has to be built rather than answered. It is the broadest of these credentials and the one the statistics in my day-to-day work grew out of.
What it covers
The domains the credential examines, taken from the issuer’s own published outline, not a self-assessment.
Data Science MethodologyPython for Data ScienceSQL & DatabasesPandas & NumPyData VisualizationMachine Learning with Scikit-learnGenerative AIApplied Capstone Project
Where it shows up
A credential is a floor, not a portfolio. The case studies are where this work is actually visible: the architecture, retrieval, and pipeline decisions behind them, with the measurements that back each one.