Plan ahead for courses where students have earned lower grades.
Lowest historical GPA first. At least 100 letter grades over the five years through the selected term. Grades reflect past outcomes, not workload or a guaranteed result.
38 courses
Ranked courses have at least 100 recorded letter grades.
An 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 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 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 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 2026Linear 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 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 2026Introduction 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 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 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 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 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 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 2026Statistical Learning covers complexity theory, Hilbert spaces, Gaussian processes, Variational Analysis, and concentration inequalities for prediction and decision making.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Spring 2023–Spring 2026Computing in Data Science and Statistics surveys popular tools and frameworks for data scientists, covering Python, machine learning, data visualization, web scraping, and MapReduce.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Spring 2023–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 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 2026Introduction to statistical inference covering distribution theory, limit theorems, parameter estimation, hypothesis testing, and Bayesian methods.
Offering recorded · Spring 2027 · Sources
Past student reviews · Wei-Yin Loh
Homework and exams were described as too tricky, and the textbook was considered useless, even for open-book exams.
historical GPA · grades
Fall 2022–Fall 2025Introduction to Machine Learning and Statistical Pattern Classification covering classification, regression, clustering, dimensionality reduction, and model evaluation using Python.
Offering recorded · Spring 2027 · Sources
Past student reviews · John Gillett
The workload was described as manageable compared to programming courses, though exam problems required careful review and some critical thinking.
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 2026Applied Multivariate Analysis covers dimension reduction, clustering, and classification using R, emphasizing conceptual underpinnings and core models for complex datasets.
Offering recorded · Spring 2027 · Sources
Past student reviews · Wei-Yin Loh
Workload consisted of a one-semester project and a series of small homework assignments, rated as moderate difficulty.
historical GPA · grades
Spring 2023–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 2026Covers statistical data visualization techniques in data science, including data prep, EDA, spatial/graph data, dimensionality reduction, and interactive models.
Offering recorded · Spring 2027 · Sources
Past student reviews · Kris Sankaran
The workload included a significant group project, homework portfolio, and exams, though some students found the assessment structure manageable.
historical GPA · grades
Spring 2023–Spring 2026Statistical Methods I provides a thorough grounding in modern statistical methods, including probability concepts, statistical inference, and data analysis using modern models, methods, and software.
Offering recorded · Spring 2027 · Sources
Past student reviews · Vivak Patel
Students reported having to do all the heavy lifting themselves, noting that the teaching style was unhelpful and office hours were not a viable resource for getting assistance.
historical GPA · grades
Fall 2022–Fall 2025Introduction to algorithms and applications of classification and regression trees, covering recursive partitioning, pruning, cross-validation, and various regression tree models.
Offering recorded · Spring 2027 · Sources
Past student reviews · Wei-Yin Loh
The course was considered extremely difficult by some, and the final project accounted for 60% of the total grade.
historical GPA · grades
Fall 2022–Spring 2026STAT 424 covers the design and analysis of experiments, including randomization, blocking, factorial designs, and response surfaces, with a focus on engineering applications and A/B testing.
Offering recorded · Spring 2027 · Sources
Past student reviews · Wei-Yin Loh
One student reported that the course required reading through limited lecture slides, with a midterm weighted at 40% and a final at 50% of the total grade.
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 2026Special topics in statistics at the master's level, with subject matter varying by offering.
Offering recorded · Spring 2027 · Sources
historical GPA · grades
Fall 2022–Spring 2026Theory 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 2026STAT 998 is a statistical consulting course structured as a consulting apprenticeship for students with graduate or professional standing.
Offering recorded · Spring 2027 · Sources
Past student reviews · Sunduz Keles
The course required students to undergo full training in the steps necessary to execute a complete consulting project with non-statistical clients.
historical GPA · grades
Fall 2022–Spring 2026Covers deep neural networks and generative models with statistical connections, emphasizing practical implementation via open-source computing for supervised learning and dataset synthesis.
Offering recorded · Spring 2027 · Sources
Past student reviews · Yiqiao Zhong
One student noted the semester had little workload and was manageable if concepts were understood, while another described exams as easy. Difficulty ratings varied, ranging from 2 to 5.
historical GPA · grades
Spring 2023–Spring 2026