Fall 2026

Data Science Computing Project

STAT 405 teaches tools for collecting, managing, and analyzing large data sets using Linux, R, and distributed computing.

offering recorded3 credits
Recorded instructors · Fall 2026 John Gillett4.2/5Ming Pei

Summary

1 / 6

The course involves heavy and hard homework assignments with tight and strict deadlines, even for students with flexible accommodation needs.

Grade history

↗
average GPA
letter grades
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AB
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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

Fall 2026

John Gillett

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 4.39/5 from 67 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: 4.0/5 raw quality · 3.5/5 difficulty · 2 reviews

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

John Gillett is praised for clear, step-by-step instruction and helpful office hours, with one student calling him their favorite. However, he is noted for assigning heavy, hard homework with strict deadlines, and he was initially unwilling to grant extensions despite accommodations.

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). Includes jointly taught sections.

Recent recorded 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). Includes jointly taught sections.

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.

MING PEI is recorded teaching in 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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction48 / 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

John Gillett is praised as an excellent lecturer who provides clear guidance and helpful office hours, though he is also noted for assigning heavy homework with strict deadlines.

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

The course involves heavy and hard homework assignments with tight and strict deadlines, even for students with flexible accommodation needs.

Students find Gillett's office hours super helpful, leaving them feeling more confident in the material, and appreciate his kindness and special accommodations.

Topics

  • Linux, R, distributed computing, git/github
  • Data analysis projects

Skills

  • Collecting, managing, and analyzing large data sets
  • Team-based research, development, writing, and presentation

Grades

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

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

1283 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 · Fall 2026

Data Science Computing Project

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:932:026274",
    "course_id": "STAT 405",
    "course_uid": "course_a420e635de610c245d2c3002",
    "term_id": "1272",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "01312eabbd909112788028713c1d03ff54941ace69eb40db4fe46406b96c0128",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}