A practical Introduction to Data Science 2026

Data science brings together tools, methods, and workflows from fields such as statistics, computer science, signal processing, machine learning, and more, combined into a practical toolkit for extracting insight from data. Its impact spans a wide range of domains: in the life sciences, it helps interpret complex measurements like single-cell sequencing; in physics, it supports the discovery of rare signals hidden in noisy experiments; and in public policy, it enables the visualization and forecasting of large-scale phenomena such as migration and mobility.

A Practical Introduction to Data Science is designed as a first course for a broad audience and focuses on hands-on work in Python. It expects basic programming experience, preferably in Python. The course introduces exploratory analysis, principled visualization, statistical and probabilistic foundations, causal thinking, and core machine learning ideas—supported throughout by concrete, computational examples.