234 cards
This deck introduces the core vocabulary and concepts that form the foundation of data science. It covers the basics of what data science is, the typical workflow that practitioners follow, and well-known methodologies like CRISP-DM. You'll also find cards on key building blocks such as datasets, features, and target variables, along with an overview of the main branches of machine learning — including supervised, unsupervised, reinforcement, and semi-supervised learning. Finally, it walks through common task types like regression, classification, clustering, dimensionality reduction, and feature engineering, giving you a well-rounded starting point for the field.
It's a great fit if you're new to data science, preparing for a course or interview, or simply want a refresher on the terminology used in textbooks, courses, and conversations with data teams. The questions are phrased as straightforward definitions, so even if you have no technical background yet, you'll be able to build a solid mental map of how the pieces fit together before diving into code or more advanced topics.
Because many of these terms are closely related — for example, regression and classification both fall under supervised learning — it helps to study the deck across several short sessions rather than cramming everything at once. Try reviewing a small batch of cards each day, and when you come across a concept you've already seen, take a moment to connect it back to the bigger picture. This kind of spaced repetition will make the vocabulary feel intuitive and easier to recall when you encounter it in articles, courses, or on the job.