Syllabus

STAT 432 - Statistical Learning with R and Python

Instructor and Logistics

• Instructor: Ruoqing Zhu
• Email: add contact email
• Lecture time/location: add schedule
• Office hours: add office hour schedule and Zoom details

Course Objectives

After this course, students should be able to:

• Understand core supervised and unsupervised statistical learning methods.
• Implement and evaluate models in both R and Python.
• Compare model complexity, generalization, and interpretability.
• Produce reproducible reports using Quarto.

Prerequisites

• Introductory probability and statistics.
• Linear algebra fundamentals.
• Basic coding experience in either R or Python.

Software

• R and RStudio (or VS Code)
• Python 3.10+ and common scientific packages
• Quarto CLI for rendering reports and pages

Assessment (Starter)

Component	Weight
Homework	60%
Quizzes	10%
Final Project	30%

Homework and Late Policy

• Regular deadline: Thursday 11:59 PM CT
• Late window: add exact late policy details
• Submission system: Gradescope (PDF reports)

Using AI Tools

See Using AI Tools in This Course for guidance on different AI tools, how to use them effectively as learning assistants, and course policies around AI usage.

Academic Integrity

University academic integrity policies apply to all course work. Discussion is encouraged, but each student must submit original work.

Changes

The course schedule and policies may be adjusted as needed and announced on this site.