STAT 391

Quantitative Introductory Statistics for Data Science
4.0 credits

Course Description

The basic concepts of statistics, machine learning and data science, as well as their computational aspects. Statistical models, likelihood, maximum likelihood and Bayesian estimation, regression, classification, clustering, principal component analysis, model validation, statistical testing. Practical implementation and visualization in data analysis. Assumes knowledge of basic probability, mathematical maturity, and ability to program. Prerequisite: either CSE 312 or MATH 394/STAT 394. Offered: W.

Current Time Schedule

Winter, 2025

SLN/Section Time Location Instructor
20744 A
Open

T Th 12:30PM - 2:20PM

T Th 12:30PM - 2:20PM

T Th 12:30PM - 2:20PM

ARC 160

ARC 160

ARC 160

JAMES BUENFIL

Kristine Yun-Yun Chan

Marina Meila

ACMS MAJORS ONLY FOR PERIOD 1. NO ENROLLMENT RESTRICTIONS FOR PERIOD 2 & 3. --- PLEASE SUBSCRIBE TO UW NOTIFY IF YOU WOULD LIKE TO GET SEAT ALERTS FROM THE UNIVERSITY. PLEASE CONTINUE TO USE UW NOTIFY BEFORE AND AFTER QUARTER STARTS.

 

 

20745 B
Open

T Th 12:30PM - 2:20PM

T Th 12:30PM - 2:20PM

ARC 160

ARC 160

JAMES BUENFIL

Marina Meila

NO ENROLLMENT RESTRICTIONS FOR PERIOD 1, 2 & 3. --- PLEASE SUBSCRIBE TO UW NOTIFY IF YOU WOULD LIKE TO GET SEAT ALERTS FROM THE UNIVERSITY. PLEASE CONTINUE TO USE UW NOTIFY BEFORE AND AFTER QUARTER STARTS.