APMA 3150
From Data to Knowledge
Course Description
Prerequisites
Engineering Undergraduate and APMA 3100 or APMA 3110
This course uses a Case-Study approach to teach statistical techniques with R: confidence intervals, hypotheses tests, regression, and anova. Also, it covers major statistical learning techniques for both supervised and unsupervised learning. Supervised learning topics cover regression and classification, and unsupervised learning topics cover clustering & principal component analysis. Prior basic statistic skills are needed. Prerequisite: Engineering Undergraduate and APMA 3100 or APMA 3110.
Instructors
Heze Chen
Fall 2026
mowefr 11:00am - 11:50amwe 7:00pm - 9:00pmwe 7:00pm - 9:00pm
5.0
Rating
3.0
Difficulty
3.81
GPA
Meiqin Li
Fall 2026
mowefr 9:00am - 9:50amwe 7:00pm - 9:00pmwe 7:00pm - 9:00pm
3.8
Rating
3.0
Difficulty
3.74
GPA
Diana Morris
Summer 2024
Sa 1:00pm - 3:15pmMoTuWeThFr 1:00pm - 3:15pm
—
Rating
—
Difficulty
—
GPA
Gianluca Guadagni
Fall 2022
MoWe 3:30pm - 4:45pm
4.0
Rating
3.0
Difficulty
3.65
GPA
Kamwoo Lee
Fall 2019
—
Rating
—
Difficulty
3.76
GPA