Descriptive Statistics and Probability for Data Science
Course ID
DSCI 551
Campus
UBC Vancouver
Semester
Fall
Course Block
1
Course Description
Introduces students to the foundational probabilistic principles of statistical reasoning. The course develops core skills in probability and distributional thinking, covering discrete and continuous distributions, conditional and joint probabilities, independence, and the logic of frequentist statistical estimation through maximum likelihood. Students also gain practical experience with Monte Carlo simulation as a tool for understanding probabilistic systems and approximating distributions. Emphasis is placed on interpreting and applying these concepts in data science contexts, ensuring students are equipped to build intuition for uncertainty, variability, and model-based reasoning. By the end of the course, students establish the mathematical and computational foundation required for subsequent courses in statistical inference, regression, and causal modelling.