This course provides students with a foundational understanding of classical statistics within the broader context of data science. Topics include exploratory analysis and descriptive statistics, probability theory and the foundations of statistical modeling, estimators, hypothesis testing, and classical linear regression. Causal inference and reproducibility issues are treated briefly. Students will learn to apply the most common statistical procedures correctly, checking assumptions and responding appropriately when they appear violated; to evaluate the design of a study and how the variables being measured relate to research questions; and to analyze real-world data using the open-source language R.

Requisites

• Master of Information and Data Science students only. Intermediate competency in calculus is required. A college-level linear algebra course is recommended.

Repeat Rules

Course is not repeatable for credit.