Browse all Statistics courses →
Red: department · Gray: UW–Madison
GPA and percentages are weighted by recorded letter grades. UW comparisons use the same terms. Cross-listed courses count once within this department and once across UW; department totals should not be added together. Grade counts are not unique students. Offering counts reflect captured records, not a complete historical schedule.
Grades have not been recorded for this term. Historical results remain in the overview above.
No released grades for this term.
54 courses
Introduction to Clinical Trials I covers design and analysis of clinical trials, including hypothesis definition, sample size, randomization, and statistical analysis for biomedical researchers.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Spring 2023–Spring 2026 · limited sampleSurvey of statistical methods in human genetics including linkage analysis, GWAS, rare variants, heritability, and risk prediction.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Spring 2025–Spring 2026 · limited sampleSurvival Analysis Theory and Methods covers analytic methods for censored survival data, including nonparametric and parametric methods, proportional hazards regression, and current topics.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Spring 2023–Spring 2026 · limited sampleTheoretical Foundations of Machine Learning covers advanced mathematical theory including statistical learning theory, Vapnik-Chevronenkis Theory, model selection, high-dimensional models, nonparametric methods, probabilistic analysis, and.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2022–Fall 2025 · limited sampleLinear Optimization covers problems with linear inequality constraints, emphasizing formal proofs, the simplex method's theory, duality, and theorems of the alternatives.
Offering recorded · Spring 2027 · Sources
Past student reviews · Carla Michini
Homeworks were extremely hard at times, though the total volume was limited to eight assignments. The workload is demanding due to the difficulty of the problems rather than the quantity.
historical GPA · grades
Fall 2022–Spring 2026Nonlinear Optimization I covers theory and algorithms for nonlinear optimization, focusing on unconstrained optimization techniques.
Offering recorded · Spring 2027 · Sources
Past student reviews · Michael Ferris
Assignments often required more than 14 hours to complete, with no penalty-free late days or assignment drops available.
historical GPA · grades
Fall 2022–Spring 2026Undergraduate Cooperative Education offering full-time work experience combining classroom theory with practical knowledge in computer science, data science, statistics, or information science.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2025–Fall 2025 · limited sampleIntroduction to combinatorics covering enumeration, inclusion-exclusion, generating functions, graph coloring, finite designs, and matching problems with applications in science and puzzles.
Offering recorded · Spring 2027 · Sources
Past student reviews · Mitch Keller
The grading system was unique and demanding: students needed full marks on all assignments and tests or extra credit to achieve an A; otherwise, the highest possible grade was an AB.
historical GPA · grades
Fall 2022–Spring 2026Internship course enabling students to earn academic credit for work experience related to Computer Sciences or Data Science programs.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2022–Spring 2026 · limited sampleAdvanced topics in the mathematical foundations of data science.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2022–Spring 2026Advanced Bayesian statistics course covering theory, methods, and computational procedures for data analysis, including MCMC and variational inference.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2022–Spring 2026Statistical Methods for Bioscience II covers polynomial and multiple regression, two-way ANOVA, split-plot design, subsampling, ANCOVA, elementary sampling, and bioassay.
Offering recorded · Spring 2027 · Sources
Past student reviews · Brian Powers
The course received a moderate difficulty rating from the reviewer.
historical GPA · grades
Spring 2023–Spring 2026Covers discrete-time and continuous-time Markov chains, Poisson point processes, and renewal processes, with applications to queueing, branching, and models in science, engineering, and business.
Offering recorded · Spring 2027 · Sources
Past student reviews · Timo Seppalainen
Homework assignments were challenging and time-consuming, but students noted there were only about ten. The homework was significantly harder than the exams, meaning thorough homework completion often reduced the need for additional exam study.
historical GPA · grades
Fall 2022–Spring 2026Introduction to Probability and Mathematical Statistics I covers probability theory, combinatorial methods, discrete and continuous distributions, moments, and estimation.
Offering recorded · Spring 2027 · Sources
Past student reviews · Garvesh Raskutti
One student found exams crazy difficult and felt the instructor assumed students had taken analysis. Another said open-book exams required high mathematical skills.
historical GPA · grades
Fall 2022–Spring 2026Covers likelihood-based statistical inference, estimation, hypothesis testing, and theoretical properties using mathematical statistics and computers.
Offering recorded · Spring 2027 · Sources
Past student reviews · Cecile Ane
Homework assignments can be challenging and may rely heavily on statistical software like R, which some students found difficult without prior experience. The course material itself is generally not considered overly difficult conceptually.
historical GPA · grades
Fall 2022–Spring 2026Introduction to the Theory of Probability covering axioms, random variables, distributions, expectation, conditional probability, multivariate distributions, inequalities, and limit theorems.
Offering recorded · Spring 2027 · Sources
Past student reviews · Benedek Valko
The course required weekly homework and quizzes, with homework described as lengthy and exams as tough but fair. Lectures were dense, covering topics from the textbook, though students found them manageable if they practiced problems consistently.
historical GPA · grades
Fall 2022–Spring 2026Advanced mathematical statistics covering estimation, hypothesis testing, confidence regions, decision theory, and likelihood ratio methods with current research topics.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Spring 2023–Spring 2026 · limited sampleTheory of Probability II covers possible topics including martingales, weak convergence of measures, and an introduction to Brownian motion.
Offering recorded · Spring 2027 · Sources
Past student reviews · Benedek Valko
Homework was described as too easy and infrequent, with one student expecting weekly assignments of greater difficulty.
historical GPA · grades
Spring 2023–Spring 2026Data Science Modeling I introduces reproducible data management, modeling, analysis, and statistical inference using R through hands-on case studies.
Offering recorded · Spring 2027 · Sources
Past student reviews · Bi Cheng Wu
Graded work was take-home and open-note, but it took a long time; students needed to stay on top of content and spend significant effort on homework.
historical GPA · grades
Fall 2022–Spring 2026An introductory course covering distributions, measures of central tendency, hypothesis testing, confidence intervals, regression, correlation, and experimental design.
Offering recorded · Spring 2027 · Sources
Past student reviews · Alexander Covington
Alexander Covington assigned long weekly homework sets that carried heavy weight in the final grade, while expecting deeper knowledge of content barely covered in lectures.
historical GPA · grades
Fall 2022–Spring 2026R For Statistics I covers the statistical language R, focusing on manipulating data and performing exploratory data analysis.
Offering recorded · Spring 2027 · Sources
Past student reviews · Alexander Covington
Past students reported extremely high workloads, noting that homework required significant self-learning because lecture videos were basic. Exams covered extensive details from self-study materials, and solutions were not provided.
historical GPA · grades
Fall 2022–Spring 2026R For Statistics II covers the statistical language R, focusing on writing conditional expressions, loops, and functions, manipulating data structures, extracting text data, and creating visualizations.
Offering recorded · Spring 2027 · Sources
Past student reviews · Bo Yang
One student reported earning an A despite the instructor's lack of support, citing prior high-pace work experience that covered the course content.
historical GPA · grades
Fall 2022–Spring 2026R FOR STATISTICS III provides an understanding of the statistical language R and teaches combining it with high performance computing tools for scientific computing.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2022–Spring 2026Introduction to mathematical statistics covering probability, discrete distributions, acceptance sampling, normal distribution, central limit theorem, and estimation methods.
Offering recorded · Spring 2027 · Sources
Past student reviews · Amy Beyler, Derek Bean
Workload was generally manageable, with some students finding homework tougher than exams. Exams were sometimes described as difficult or tricky, but often aligned with practice materials or were curved.
historical GPA · grades
Fall 2022–Spring 2026Introduction to Theory and Methods of Mathematical Statistics II covers unbiased estimation, MLE, confidence intervals, hypothesis tests, Neyman-Pearson lemma, LRT, regression, and ANOVA.
Offering recorded · Spring 2027 · Sources
Past student reviews · Bo Yang
The grade was based on about five homework assignments and three exams. One student found the material not difficult but noted it was taught extremely slowly.
historical GPA · grades
Fall 2022–Spring 2026Introduction to Statistics for Science and Engineering covers descriptive stats, probability, hypothesis testing, linear regression, ANOVA, and experimental design using R.
Offering recorded · Spring 2027 · Sources
Past student reviews · Michael Culbertson
Despite considerable use of R in the course, students were taught only the bare minimum required for assignments.
historical GPA · grades
Fall 2022–Spring 2026Applied Regression Analysis covers linear regression for prediction, feature selection, and interpretation, extending to mixed and generalized linear models using R.
Offering recorded · Spring 2027 · Sources
Past student reviews · Karl Rohe
Karl Rohe’s homework was due weekly, long, and unrelated to lectures, while exam questions were vague.
historical GPA · grades
Fall 2022–Spring 2026Data Science Modeling II teaches exploring, modeling, and analyzing data using R, covering probability, hypothesis testing, regression, ANOVA, and machine learning methods like random forests.
Offering recorded · Spring 2027 · Sources
Past student reviews · Brian Powers, Bi Cheng Wu
Some students found the tests very difficult, with one noting they were harder than the midterms. Another described the instructor as super strict, warning students to be careful about making mistakes.
historical GPA · grades
Fall 2022–Spring 2026Introductory applied statistics for life sciences using R, covering probability, inference, ANOVA, linear regression, and experimental design.
Offering recorded · Spring 2027 · Sources
Past student reviews · Cecile Ane, Mitchell Paukner
Cecile Ane’s group project made the class feel like 4 credits, and her homework was not enough for preparation. Mitchell Paukner’s tests accurately reflected lecture content, though students found the lectures exhausting.
historical GPA · grades
Fall 2022–Spring 2026Data Science Computing Project focuses on tools for collecting, managing, and analyzing large data sets using Linux, R, and distributed computing, with team-based projects involving research, writing, and presentations.
Offering recorded · Spring 2027 · Sources
Past student reviews · John Gillett
Students reported heavy and hard homework with tight deadlines, while another noted that exams covered too much content.
historical GPA · grades
Spring 2023–Spring 2026