36-400 Overview of Statistical Learning and Modeling

9 units, Carnegie Mellon University

This course is a high-level introduction both to fundamental concepts of probability and statistics and to the ways by which statisticians go about approaching and analyzing data. The course will cover data processing, exploratory data analysis, parameter estimation and hypothesis testing, clustering, and common regression and classification models. Students will carry out work using the R and Python programming languages. This course is open only to students not majoring in Stat & DS who have taken the prerequisite courses.

Prerequisites: 36-200 and (36-309 or 36-290 or 36-202)

Offered: Fall 2026

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