DATASCI 2000A
0.50 credit · Main
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.
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
The former Integrated Science 2002B.
2 lecture hours/week, 2 laboratory hours/week.
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