STAT-41033 credit hours

Statistical Learning

Statistics · 3 hrs Lecture

Offered: Fall Or Winter

STAT-4103 prerequisites

8 courses appear in this chain, up to 3 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

a minimum grade of C+ in MATH-0041

permission of the Department Chair

Add your completed courses to see which of these you've already done and what you could take next. They stay on this device.

Where STAT-4103 counts

Required by 2 programs:

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

Counts toward 1 more as one option among several, not as a course you must take:

STAT 3-YEAR BA/BSc IN STATISTICS

Read from the departments' own calendar pages, where 71 of 141 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.