STAT-41033 credit hours

Statistical Learning

Statistics · 3 hrs Lecture

Offered: Fall Or Winter

STAT-4103 prerequisites

9 courses appear in this chain, up to 4 levels deep. 2 are needed whichever route you take: MATH-1201 → STAT-3103

Show the chain

STAT-3103Applied Regression Analysis

all of

any one of

STAT-1302Statistical Analysis II

any one of

STAT-1301Statistical Analysis I

any one of

Pre-Calculus Math 40S

Applied Math 40S

STAT-1401Statistics I for Business and Economics

any one of

Pre-Calculus Mathematics 40S

Applied Mathematics 40S

STAT-1501Elementary Biological Statistics I

any one of

Pre-Calculus Mathematics 40S

Applied Mathematics 40S

STAT-2001Elementary Biological Statistics II

any one of

STAT-1301Statistical Analysis I

Its prerequisites are shown above.

STAT-1401Statistics I for Business and Economics

Its prerequisites are shown above.

STAT-1501Elementary Biological Statistics I

Its prerequisites are shown above.

MATH-1201Linear Algebra I

any one of

Pre-Calculus Mathematics 40S

Applied Mathematics 40S

MATH-0042Mathematics Access II

any one of

A minimum grade of 65% in Pre-Calculus 30S

MATH-0041Mathematics Access Imin C+

permission of the Department Chair

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Where STAT-4103 counts

Required by 1 program:

STAT 4-YEAR BA/BSc IN STATISTICS (DATA SCIENCE STREAM)

Counts toward 2 more as one option among several, not as a course you must take:

STAT 3-YEAR BA/BSc IN STATISTICS
STAT 4-YEAR BA/BSc IN STATISTICS

Read from the departments' own calendar pages, where 50 of 208 programs are only partly machine-readable, so treat these as a minimum rather than the full list. Confirm with an academic advisor.

Description

This course deals with a variety of topics in statistical learning and their implementation in R. Topics include introduction to statistical learning methods; review of linear regression; use of LASSO and ridge regression techniques to identify useful explanatory variables; understanding the practical difference between predictive outcomes from parametric and non parametric methods; implementation of several ensemble learning methods; clustering methods and dimension reduction; employing reasonable programming practices with basic R syntax and functions; report writing for projects using standard software. Students who major in Data Science are encouraged to take ACS-4953 prior taking this course.

Requisite courses

STAT-3103 [prerequisite(s)].

Lecture

From the 2026-27 undergraduate calendar. Always verify details on WebAdvisor or with an academic advisor before registering.