← Statistics (STAT)

Hardest courses in Statistics

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.

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42 courses

Historical grades cover up to five years through the selected term. Only courses with at least 100 letter grades are ranked.

Introduction to statistical methods covering distributions, inference, regression, and experimental design.

Offering recorded · Fall 2026

Workload is described as manageable with straightforward exams, though success requires consistent effort and engagement with the material.

historical GPA · grades

Spring 2022–Spring 2026

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.

historical GPA · grades

Spring 2022–Spring 2026

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.

historical GPA · grades

Spring 2022–Spring 2026

STAT 340 teaches data exploration, modeling, and analysis using R, covering probability, simulation, hypothesis testing, Bayesian inference, regression, and machine learning techniques.

Offering recorded · Fall 2026

Reviewers consistently report that exams are extremely difficult, often unrelated to lectures or homework, with low averages and insufficient time for paper-based tests.

historical GPA · grades

Spring 2022–Spring 2026

Introduces linear optimization problems, emphasizing formal proofs, the simplex method, duality theory, and theorems of the alternatives.

Offering recorded · Fall 2026

The course is considered very difficult, with hard assignments that require significant study time, though group work is permitted. Exams are reported as easier than homework, although one student found the final exam unexpectedly difficult despite its stated format.

historical GPA · grades

Spring 2022–Spring 2026

Introduction to the theory of probability covering axioms, distributions, and limit theorems

Offering recorded · Fall 2026

Historical reviews of Benedek Valko, Erik Bates: Workload varies by instructor; one review cites an extremely challenging final, while another notes fair homework and adjusted exam weights to reward improvement.

historical GPA · grades

Spring 2022–Spring 2026

Introduction to combinatorics covering enumeration, generating functions, graph theory, and matching problems.

Offering recorded · Fall 2026

The course is dense and challenging. Homework is feasible but requires reading the textbook, as lectures may not fully explain concepts. Exams often draw directly from the book and previous tests.

historical GPA · grades

Spring 2022–Spring 2026

Introduction to mathematical statistics covering probability, distributions, the central limit theorem, and estimation.

Offering recorded · Fall 2026

Historical reviews of Amy Beyler, Derek Bean, Yaoguo Xie: Workload and difficulty vary significantly by instructor. Some report high-quality materials and fair tests, while others describe difficult exams and a need for extensive self-study to gain solid knowledge.

historical GPA · grades

Spring 2022–Spring 2026

Introduction to time series analysis covering stationarity, AR/MA models, and forecasting applications.

Offering recorded · Fall 2026

Historical reviews of Panduan An: Reviewers describe the course as difficult, with homework questions being very hard and a high drop rate before the midterm. The lecture pace is often too fast to take notes, and prereqs do not adequately prepare students for the workload.

historical GPA · grades

Fall 2022–Fall 2025

Introduction to stochastic processes covering Markov chains, point processes, and renewal theory with applications to queueing and branching models.

Offering recorded · Fall 2026

Seppalainen assigns challenging homework that is harder than exams, while Valko uses a grading scheme based on multiple weighted components rather than heavy testing.

historical GPA · grades

Spring 2022–Spring 2026

Introduction to mathematical statistical inference, focusing on likelihood, estimation, and hypothesis testing.

Offering recorded · Fall 2026

The course is described as difficult and intense, with long homeworks, high-stakes exams, and a significant gap between lecture content and test material.

historical GPA · grades

Spring 2022–Spring 2026

Introduction to probability and mathematical statistics covering distributions, moments, and estimation.

Offering recorded · Fall 2026

The workload includes ten homeworks, one midterm, and one final, with exams described as easy if students engage with the provided materials.

historical GPA · grades

Spring 2022–Spring 2026

STAT 351 introduces distribution-free statistical methods, covering rank tests, permutation methods, and kernel estimation.

Offering recorded · Fall 2026

Historical reviews of Chunming Zhang: Assignments and projects are generally inline with lectures and comprehensive, but students note a lack of practice materials and exams that may not reflect course content.

historical GPA · grades

Spring 2022–Spring 2025

STAT 240 introduces reproducible data science modeling and statistical inference using R and R Markdown.

Offering recorded · Fall 2026

Students report that exams are significantly harder than lectures and homework, with unclear instructions and arbitrary difficulty. Self-study is often necessary due to the disconnect between taught material and assessments.

historical GPA · grades

Spring 2022–Spring 2026

STAT 421 teaches techniques for analyzing categorical response data, including contingency tables and regression modeling, using R.

Offering recorded · Fall 2026

The workload is relentless, consisting of approximately 66% homework with four to six hours of work per week across multiple large assignments.

historical GPA · grades

Fall 2022–Fall 2025

Covers theory and algorithms for nonlinear optimization, focusing on unconstrained methods like quasi-Newton and trust-region techniques.

Offering recorded · Fall 2026

Assignments are graded harshly, and the course is notoriously difficult, requiring significant preparation to keep up with the rigorous material.

historical GPA · grades

Spring 2022–Spring 2026

STAT 609 covers mathematical statistics, including probability theory, distribution theory, and limit theorems like the Central Limit Theorem.

Offering recorded · Fall 2026

One reviewer reports that homework and class content differ significantly from the test, requiring preparation for a difficult exam experience.

historical GPA · grades

Fall 2022–Fall 2025

Theory of Probability I introduces measure theoretic probability and stochastic processes.

Offering recorded · Fall 2026

Historical reviews of Hao Shen, Jun Yin: Jun Yin's materials were hard with a weird curve, while Hao Shen was very test-heavy and did not provide proper preparation for exams.

historical GPA · grades

Fall 2022–Fall 2025

STAT 615 develops mathematical theories and statistical concepts for understanding, predicting from, and criticizing data models.

Offering recorded · Fall 2026

historical GPA · grades

Spring 2022–Spring 2026

STAT 405 teaches tools for collecting, managing, and analyzing large data sets using Linux, R, and distributed computing.

Offering recorded · Fall 2026

The course involves heavy and hard homework assignments with tight and strict deadlines, even for students with flexible accommodation needs.

historical GPA · grades

Spring 2023–Spring 2026

STAT 424 introduces statistical experimental design, covering randomization, blocking, factorial designs, and response surface methodology with engineering applications.

Offering recorded · Fall 2026

Workload involves writing R code for homework, while exams are often predictable and similar to practice problems, though self-study via the textbook is recommended.

historical GPA · grades

Spring 2022–Spring 2026

STAT 610 introduces statistical inference, covering distribution theory, limit theorems, parameter estimation, hypothesis testing, Bayesian inference, and nonparametric estimation.

Offering recorded · Fall 2026

Historical reviews of Wei-Yin Loh: Assignments and exams are described as too tricky, with a useless textbook even for open-book exams, potentially causing students to quit before mid-term.

historical GPA · grades

Spring 2022–Fall 2025

STAT 436 teaches data visualization techniques within data science workflows, covering data preparation, exploratory analysis, and model interpretability.

Offering recorded · Fall 2026

The course uses a flipped format with pre-lecture readings, a large group project, and exams. Some students find it easy to succeed with minimal effort, while others note the material requires deep engagement.

historical GPA · grades

Spring 2023–Spring 2026

STATISTICAL METHODS FOR BIOSCIENCE I covers statistical inference, ANOVA, regression, and diagnostics for bioscience applications.

Offering recorded · Fall 2026

Historical reviews of Bret Larget, Debraj Das: Exams can be difficult and heavily weighted, with one reviewer noting they covered unseen material, while another found them fair and focused on required knowledge.

historical GPA · grades

Fall 2022–Fall 2025

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.

historical GPA · grades

Spring 2022–Spring 2026

Covers statistical methods for clinical trial design, survival analysis, and sequential monitoring.

Offering recorded · Fall 2026

historical GPA · grades

Fall 2022–Fall 2025

STAT 601 provides a thorough grounding in modern statistical methods, covering data collection, exploration, probability, and analysis.

Offering recorded · Fall 2026

Historical reviews of Vivak Patel: Reviewers describe a heavy workload with difficult projects and expect students to do significant independent work, noting that graduate-level rigor requires hard work.

historical GPA · grades

Spring 2022–Fall 2025

Applied Regression Analysis covers linear regression for prediction and interpretation, including feature selection, assumption checking, and extensions like mixed and generalized linear models using R.

Offering recorded · Fall 2026

Weekly homework is described as long and unrelated to lectures, while the final project is considered very important and strictly graded. Students report dealing with shifting deadlines and vague test questions that rely on the professor's interpretation.

historical GPA · grades

Spring 2022–Spring 2026

STAT 461 teaches advanced statistical methodologies for modern finance, including discrete and continuous stochastic models.

Offering recorded · Fall 2026

Historical reviews of Yazhen Wang: Reviewers found homeworks difficult and exams tricky, noting that the lack of a textbook and study aids made preparation challenging.

historical GPA · grades

Fall 2023–Fall 2025

STAT 679 covers special topics in statistics at the master's level, with subject matter varying by offering.

Offering recorded · Fall 2026

historical GPA · grades

Spring 2022–Spring 2026