Spring 2027

Statistical Methods for Data Science

Statistical Methods for Data Science covers modern statistical methods, data analysis, linear regression, diagnostic tools, prediction, model selection, and experimental design.

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

Summary

1 / 4

Recent recorded grades — Fall 2023: 3.83 GPA, 100.0% A/AB (n=20 letter grades); Fall 2024: 3.75 GPA, 100.0% A/AB (n=32 letter grades); Fall 2025: 3.53 GPA, 87.5% A/AB (n=32 letter grades).

What you’ll be able to do

  • Strategize and select a regression equation, examine residuals, transform data, recognize biases due to excluded variables and measurement error

  • Conduct general linear modeling for exponential family data and specifically models for binary, count, and categorical data, perform model fitting and inference

  • Develop the concepts and relevant methodology and ability to design and analyze experiments

  • Use and interpret computer package for regression programs

  • Present clear data structure and analysis in the context of data drawn from real-world applications

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

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 613

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: 3.83 GPA, 100.0% A/AB (n=20 letter grades); Fall 2024: 3.75 GPA, 100.0% A/AB (n=32 letter grades); Fall 2025: 3.53 GPA, 87.5% A/AB (n=32 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Data description, linear regression models, diagnostic tools, prediction, model selection, and experimental design
  • General linear modeling for exponential family data, including binary, count, and categorical data

Skills

  • Selecting regression equations, examining residuals, transforming data, and recognizing biases
  • Conducting general linear modeling for exponential family data and performing model fitting and inference
  • Designing and analyzing experiments
  • Using and interpreting computer packages for regression programs
  • Presenting clear data structure and analysis for real-world applications

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.

Read original reviews

No records available.

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": "4a740513df0927817e43137285d40ac8b0d19731fcb67b6cf12117f41f0bcb66",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}