Spring 2027

Statistical Models for Data Science

Covers probability, random variables, distributions, moments, generating functions, transformations, sampling theory, and limit theorems within statistical models for data science.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2023: 3.61 GPA, 73.7% A/AB (n=19 letter grades); Fall 2024: 3.70 GPA, 86.7% A/AB (n=30 letter grades); Fall 2025: 3.36 GPA, 62.5% A/AB (n=32 letter grades).

What you’ll be able to do

  • Describe the foundations of probability theory, including: the axioms of probability, the concepts of sample space and probability measure, events and their probabilities, and the notions of conditional probabilityand independence

  • Explain the concepts of random variables and probability distributions, and calculate or otherwise utilize key mathematical objects and results related to them, including: random number generation, expected values and moments, moment-based probabilistic inequalities, moment generating functions, joint probability distributions of multiple random variables

  • Identify the most important probability distributions used in Statistics, cite their properties, create computer visualizations of them, and simulate random variates from these distributions. This includes the normal distribution, the binomial distribution, the exponential distribution, the Poisson distribution, and more

  • Apply, and explain the significance of, key results related to the limits of sequences of random variables, including laws of large numbers, the Central Limit Theorem, and the delta method

  • Grasp important elements of sampling theory, including: random samples and how to generate them; statistics, such as the sample mean and sample variance, and how to derive or simulate their sampling distributions; and the distributions that arise from a normal random sample, namely the chi-square,T, and F distributions.

Source: UW Guide

Grade history

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

Prerequisites

Course map

Declared in Data Science MS or Data Engineering MS

  • Declared in Data Science MS or Data Engineering MS
STAT 611 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Declared in Data Science MS or Data Engineering MS

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records
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Student experience

the class

Recent recorded grades — Fall 2023: 3.61 GPA, 73.7% A/AB (n=19 letter grades); Fall 2024: 3.70 GPA, 86.7% A/AB (n=30 letter grades); Fall 2025: 3.36 GPA, 62.5% A/AB (n=32 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Foundations of probability theory, axioms, sample spaces, probability measures, events, conditional probability, and independence.
  • Random variables, probability distributions, expected values, moments, probabilistic inequalities, moment generating functions, and joint distributions.
  • Limits of sequences of random variables, laws of large numbers, Central Limit Theorem, and the delta method.

Skills

  • Probability theory, random variables, distributions, moments, generating functions, transformations, sampling theory, and limit theorems.
  • Identifying key probability distributions, creating computer visualizations, and simulating random variates.
  • Deriving or simulating sampling distributions of statistics like sample mean and variance, and understanding chi-square, T, and F distributions.

Grades

Latest available · Fall 2025— 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 Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1316 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

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.

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Selected offering source records

No offering records for the selected term.

Raw records 
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

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

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "8c66505fe91dffde2918069469dee7a58c9668381539784b66522548a35015dd",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}