INTRODUCTION TO DATA SCIENCE
Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.
Prerequisites & course information
Antirequisite(s): The former Integrated Science 2002B. Prerequisite(s): 1.0 course from Mathematics, Calculus, Numerical and Mathematical Methods, or Applied Mathematics (numbered 1000 and higher) with a minimum mark of 60%. Data Science 1000A/B (with a minimum grade of 60%) can be used to meet 0.5 of the 1.0 mathematics course requirements Extra Information: 2 lecture hours/week, 2 laboratory hours/week. Course Weight: 0.50
Sources
- Course catalogue ↗2026-2027