No course-specific feedback yet.
Fall 2026
Special Topics in Statistics
STAT 479 covers special topics in statistics of interest to undergraduate students.
Summary
1 / 6
Historical reviews of John Gillett, Karl Rohe, Kris Sankaran: Workload varies by instructor; Gillett’s course required independent Linux learning and debugging, while Sankaran’s recent term involved graduate-level assignments. Sankaran’s earlier terms featured spread-out homeworks, and Rohe provided few guidelines.
Grade history
↗ average GPA
letter grades
letter grades
A
AB
B
BC
C
D
F
All recorded terms · compare terms & instructors
Prerequisites
Course mapNone
- None
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- None
Professors
Fall 2026No course-specific feedback yet.
Historical instructors & teaching patterns
Historical reviews of Kris Sankaran: Kris Sankaran's current course features cutting-edge content but receives criticism for abysmal teaching style and materials, with graduate-level exams and assignments requiring significant outside time. One reviewer notes the difficulty and lack of consideration for undergraduates.
JOHN GILLETT is recorded teaching in Spring 2022. Recorded history may be incomplete and does not establish a future schedule.
KARL ROHE is recorded teaching in Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2020, Spring 2022, Fall 2023, Fall 2024. Recorded history may be incomplete and does not establish a future schedule.
KRIS SANKARAN is recorded teaching in Spring 2022, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
SEBASTIAN RASCHKA is recorded teaching in Fall 2018, Spring 2019, Fall 2019. Recorded history may be incomplete and does not establish a future schedule.
YAZHEN WANG is recorded teaching in Fall 2013, Fall 2014. 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 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 003 | Classroom Instruction | 38 / 66 | 0 |
| LEC 004 | Classroom Instruction | 0 / 2 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Historical reviews of John Gillett, Karl Rohe, Kris Sankaran: Reviewers describe Kris Sankaran as a passionate professor covering R and machine learning, though one review criticizes his materials and graduate-level assignments. Past experiences with instructors like Gillett and Rohe were mixed, ranging from highly responsible to disorganized.
Recent recorded grades — Spring 2025: 3.91 GPA, 94.7% A/AB (n=38 letter grades); Fall 2025: 3.97 GPA, 98.2% A/AB (n=110 letter grades); Spring 2026: 3.49 GPA, 68.2% A/AB (n=88 letter grades).
difficulty & workload
Historical reviews of John Gillett, Karl Rohe, Kris Sankaran: Workload varies by instructor; Gillett’s course required independent Linux learning and debugging, while Sankaran’s recent term involved graduate-level assignments. Sankaran’s earlier terms featured spread-out homeworks, and Rohe provided few guidelines.
Historical reviews of John Gillett, Karl Rohe, Kris Sankaran, Sebastian Raschka, Yazhen Wang: Reviewers found Sankaran helpful and passionate, though one criticized his materials. Gillett was praised for patience but criticized for fast lectures. Raschka received praise for his lectures, while Wang and Rohe were described as disorganized.
Grades
Fall 2026 · Projected
Before grades are released– average GPA
Approximate 80% prediction interval
About this estimate
The course’s semester-average GPA, not an individual student’s grade. The center uses 3 same-season terms, weighted toward recent results.
The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.
5 earlier forecasts · 0.15 GPA average error.
Grades over time
Through Fall 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.
Selected offering source records
Special Topics in Statistics
Recorded 2026-09-07Sports Analytics
Recorded 2026-09-07Bayesian Stats&Machine Lrning
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:932:023894",
"course_id": "STAT 479",
"course_uid": "course_1282dc8dbc1e7c5749f7cc41",
"term_id": "1272",
"source_course_id": "023894",
"source_subject_id": "932",
"title": "Special Topics in Statistics",
"credits_min": 1,
"credits_max": 3,
"typically_offered": "Occasionally"
},
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:932:023894.25",
"course_id": "STAT 479",
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"term_id": "1272",
"source_course_id": "023894.25",
"source_subject_id": "932",
"title": "Sports Analytics",
"credits_min": 1,
"credits_max": 3,
"typically_offered": "Occasionally"
},
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:932:023894.30",
"course_id": "STAT 479",
"course_uid": "course_1282dc8dbc1e7c5749f7cc41",
"term_id": "1272",
"source_course_id": "023894.30",
"source_subject_id": "932",
"title": "Bayesian Stats&Machine Lrning",
"credits_min": 1,
"credits_max": 3,
"typically_offered": "Occasionally"
}
]Rate My Professors
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Madgrades
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "16c9ebb9fd0657f5999f15786eec5746ecf0a8a01dccae66d4a4c1c06b09ff16",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}