STAT 303 teaches using the R language to manipulate data and perform exploratory data analysis.
Offering recorded · Fall 2026
The course involves multiple quizzes, homework assignments, and an exam, with reviewers noting the workload is substantial despite the straightforward nature of the tasks.
Introduction to statistics for science and engineering covering descriptive statistics, probability, hypothesis testing, linear regression, and ANOVA using R.
Offering recorded · Fall 2026
Homework is time-consuming, taking five to six hours per set, and exams are challenging with a time crunch. Students must spend significant time understanding homework to prepare for tests that may differ from practice problems.
STAT 371 introduces applied statistical practice for the life sciences using R, covering EDA, inference, ANOVA, and regression.
Offering recorded · Fall 2026
Historical reviews indicate varied workloads. John Davis reported short homework, whereas Cecile Ane assigned a heavy group project and required extra practice. Vivak Patel's course was noted as highly difficult.
Covers pattern classification, regression, clustering, and dimensionality reduction with a focus on statistical evaluation and Python implementation.
Offering recorded · Fall 2026
Historical reviews of John Gillett, Sebastian Raschka: Workload is generally manageable with recorded lectures, though Gillett's exams can be difficult and require careful review, while one review cites unorganized materials and confusing group policies.
STAT 479 covers special topics in statistics of interest to undergraduate students.
Offering recorded · Fall 2026
Historical reviews of John Gillett, Karl Rohe, Kris Sankaran: Workload varies by instructor; Gillett’s course required independent Linux learning and debugging, while Sankaran’s recent term involved graduate-level assignments. Sankaran’s earlier terms featured spread-out homeworks, and Rohe provided few guidelines.