Spring 2027

Statistical Inference for Data Science

Covers statistical inference methods including parameter estimation, Bayesian approaches, hypothesis testing, ANOVA, and linear regression for data science applications.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.45 GPA, 63.2% A/AB (n=19 letter grades); Spring 2025: 3.45 GPA, 62.1% A/AB (n=29 letter grades); Spring 2026: 3.58 GPA, 71.0% A/AB (n=31 letter grades).

What you’ll be able to do

  • Construct point estimators including maximum likelihood estimators, understand the theoretical properties of point estimation methods, evaluate their performance using mathematical derivations and simulation-based techniques, and identify optimal point estimators

  • Describe the Bayesian approach to point estimation and contrast it with the frequentist approach

  • Construct and evaluate hypothesis tests (such as likelihood ratio tests) using mathematical derivations or simulation-based techniques, interpret their results, understand theoretical properties of hypothesis testing methods, and identify optimal hypothesis tests

  • Construct and evaluate interval estimators using mathematical and simulation-based techniques, understand the theoretical properties of interval estimation methods, and interpret their results

  • Identify and describe the assumptions underlying methods of statistical inference and explain their importance

  • Fit models and carry out statistical inference in classical situations: namely: the comparison of two or more samples (ANOVA), the analysis of categorical data, linear regression, generalized linear models, and random and mixed effects models

Source: UW Guide

Grade history

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

Prerequisites

Course map

STAT 611

STAT 612 used by

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

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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
LEC 001Classroom Instruction0 / 450

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

Recent recorded grades — Spring 2024: 3.45 GPA, 63.2% A/AB (n=19 letter grades); Spring 2025: 3.45 GPA, 62.1% A/AB (n=29 letter grades); Spring 2026: 3.58 GPA, 71.0% A/AB (n=31 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Parameter estimation, Bayesian inference, hypothesis testing, confidence sets, ANOVA, categorical data, and linear regression
  • Generalized linear models and random/mixed effects models

Skills

  • Parameter estimation, Bayesian inference, hypothesis testing, ANOVA, and linear regression
  • Fitting models for ANOVA, categorical data, linear regression, generalized linear models, and mixed effects

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

Where this course fits relative to

Latest available grades · Spring 2026 · 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

1280 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
STAT 612 · Spring 2027

Statistical Inference for Data Science

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:026233",
    "course_id": "STAT 612",
    "course_uid": "course_6aad5ab0349d79e4904f2259",
    "term_id": "1274",
    "source_course_id": "026233",
    "source_subject_id": "932",
    "title": "Statistical Inference for Data Science",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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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
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": "9ad675d22d5d64f8cf07d2a0f90cc0716e95ab011aa84e82ceab1af9fed3cf38",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}