Spring 2027

Computational Statistics

Computational Statistics covers statistical inference via computation, including data reduction, parameter estimation, hypothesis testing, prediction, modeling, and Bayesian/non-Bayesian inference.

offering recorded4 credits
Recorded instructors · Spring 2027 Vivak Patel2.7/5

Summary

1 / 6

Past student reviews · Vivak Patel

The course included optional problem sets, and the final exam consisted of a few questions from these sets.

What you’ll be able to do

  • Explain, evaluate, and avoid the ill-effects of finite-precision arithmetic in statistical calculations

  • Construct summary statistics from data using multiple algorithms, including best-practice approaches

  • Identify problems for which Monte Carlo simulation is appropriate, and deploy schemes to simulate random variables

  • Recognize statistical model fitting situations where tools from numerical linear algebra are required, and deploy various matrix decompositions for these situations

  • Demonstrate knowledge of basic convex analysis and how this is used to solve statistical optimization problems

  • Describe and deploy algorithmic strategies for both nonlinear and constrained optimization problems that arise in fitting statistical models

  • Construct confidence sets for a parameter, standard errors for an estimator, a p-value for a hypothesis test, or a prediction accuracy estimate for a predictor in both standard and non-standard statistical settings

  • Use graph-based approaches to deploy Bayesian posterior analysis

  • Design, deploy, and justify various Markov-chain Monte Carlo algorithms for statistical inference

  • Outline the structure of specialized optimization tools used in statistics

  • Deploy computations on a variety of data structures using R programming tools

Source: UW Guide

Grade history

↗
average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor

    • Statistics MS
    • Statistics PhD
    • Biomedical Data Science PhD
    • Biomedical Data Science MS
    • Business PhD
    • Statistics Doctoral Minor
    take one
STAT 771

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Any of
    • Statistics MS
    • Statistics PhD
    • Biomedical Data Science PhD
    • Biomedical Data Science MS
    • Business PhD
    • Statistics Doctoral Minor

Professors

Spring 2027

Vivak Patel

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 1.97/5 from 29 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 2.3/5 raw quality · 4.3/5 difficulty · 3 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Vivak Patel

Vivak Patel's teaching received mixed feedback. Some students reported poor communication and unhelpful responses, while others found the course well-organized and accessible. The class centered on applying methods to a research project of your choice, featuring easy-to-follow lectures and optional problem sets that appeared on the final exam.

Recent recorded grades — Fall 2018: 3.46 GPA, 85.7% A/AB (n=14 letter grades); Fall 2019: 3.67 GPA, 90.0% A/AB (n=20 letter grades); Fall 2020: 3.00 GPA, 0.0% A/AB (n=6 letter grades).

Historical instructors & teaching patterns

VIVAK PATEL is recorded teaching in Fall 2018, Fall 2019, Fall 2020, Fall 2021. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 300
DIS 311Classroom Instruction0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Vivak Patel

Vivak Patel taught a well-organized course centered on applying methods to a research project of your choice, with easy-to-follow lectures and an accessible approach to student support.

Recent recorded grades — Fall 2023: 3.66 GPA, 80.6% A/AB (n=31 letter grades); Fall 2024: 3.70 GPA, 82.6% A/AB (n=23 letter grades); Spring 2026: 3.65 GPA, 76.9% A/AB (n=13 letter grades).

difficulty & workload

Past student reviews · Vivak Patel

The course included optional problem sets, and the final exam consisted of a few questions from these sets.

Past student reviews · Vivak Patel

One student described the professor as excellent and very accessible, noting he reminded them to reach out if struggling and that the lectures were easy to follow.

Topics

  • Model fitting and optimization
  • Bayesian posterior analysis
  • Convex analysis
  • Data management systems

Skills

  • Monte Carlo simulation
  • Markov-chain Monte Carlo algorithms
  • Numerical linear algebra and matrix decompositions
  • Nonlinear and constrained optimization
  • R programming for statistical computation

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
STAT 771 · Spring 2027

Computational Statistics

Recorded 2026-10-14
Raw records 
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  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:018507",
    "course_id": "STAT 771",
    "course_uid": "course_c7c2e62609a7d096ae23e826",
    "term_id": "1274",
    "source_course_id": "018507",
    "source_subject_id": "932",
    "title": "Computational Statistics",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Occasionally"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "f96f1f0f99a0f11dcfee8686a7018d53d485726086812a2ae26dbcdb84f4c0df",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}