Spring 2027

Data Science Computing Project

Data Science Computing Project focuses on tools for collecting, managing, and analyzing large data sets using Linux, R, and distributed computing, with team-based projects involving research, writing, and presentations.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · John Gillett

Students reported heavy and hard homework with tight deadlines, while another noted that exams covered too much content.

What you’ll be able to do

  • Collect and manage data and write programs and documentation viatools suited to large computations including an operating system, aneditor, and a version control system.

  • Run analyses too large for a laptop on cluster, grid, and/or cloudcomputing environments.

  • Work in teams to research, develop, write, and make presentations ona data analysis proposal, a draft data analysis, and a revised dataanalysis.

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

(STAT 240 or 303) and (COMP SCI 200, 220, 300, or placement into COMP SCI 300), or graduate/professional standing

STAT 405

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

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

JOHN GILLETT is recorded teaching in Spring 2023, Spring 2024, Spring 2025, Spring 2026. 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 / 520

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 · John Gillett

John Gillett was described as a favorite professor who was super kind and provided helpful office hours that left students feeling confident in the material.

Recent recorded grades — Spring 2024: 3.35 GPA, 68.9% A/AB (n=61 letter grades); Spring 2025: 3.65 GPA, 80.0% A/AB (n=55 letter grades); Spring 2026: 3.59 GPA, 80.4% A/AB (n=46 letter grades).

difficulty & workload

Past student reviews · John Gillett

Students reported heavy and hard homework with tight deadlines, while another noted that exams covered too much content.

Past student reviews · John Gillett

The instructor gave step-by-step instructions to guide students through processes, though one student noted he was initially unwilling to grant deadline extensions.

Topics

  • Large data set collection, management, and analysis
  • Linux, R, distributed computing, text editors, and git/github
  • Cluster, grid, and cloud computing environments
  • Team-based data analysis projects with research, writing, and presentations

Skills

  • Collecting, managing, and analyzing large data sets using operating systems, editors, and version control systems.
  • Using Linux, R, git/github, and running analyses on cluster, grid, or cloud computing environments.
  • Team-based research, development, writing, and presenting data analysis proposals and revisions.

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 405 · Spring 2027

Data Science Computing Project

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": "1274:932:026274",
    "course_id": "STAT 405",
    "course_uid": "course_a420e635de610c245d2c3002",
    "term_id": "1274",
    "source_course_id": "026274",
    "source_subject_id": "932",
    "title": "Data Science Computing Project",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
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": "7a855f63b497f351b17858d7144b2dc541e43d4dfbef1082bc398392d53ece3f",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}