Introductory Data Science Programming I for Research course covering Python, programming basics, web scraping, database queries, and tabular analysis.
Recent recorded grades — Spring 2025: 3.86 GPA, 96.6% A/AB (n=29 letter grades); Fall 2025: 3.77 GPA, 100.0% A/AB (n=13 letter grades); Spring 2026: 3.92 GPA, 100.0% A/AB (n=6 letter grades).
integrate foundational concepts and tools from mathematics, computer science and statistics to solve data science problems
demonstrate competencies with tools and processes necessary for data management and reproducibility
produce meaning from data employing modeling strategies
demonstrate critical thinking related to data science concepts and methods
demonstrate oral, written and visual communication skills related to data science
manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information
Source: UW Guide
All recorded terms · compare terms & instructors
Graduate/professional standing
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Raw average: 3.74/5 from 23 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.
RMP profile ↗ · All captured review dates; profile matched by name.
No course-specific feedback yet.
Raw average: 3.48/5 from 89 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.
RMP profile ↗ · All captured review dates; profile matched by name.
Recent recorded grades — Spring 2025: 3.86 GPA, 96.6% A/AB (n=29 letter grades); Fall 2025: 3.77 GPA, 100.0% A/AB (n=13 letter grades); Spring 2026: 3.92 GPA, 100.0% A/AB (n=6 letter grades). Includes jointly taught sections.
MICHAEL DOESCHER is recorded teaching in Fall 2020, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 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.
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| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 10 | 0 |
| LEC 002 | Classroom Instruction | 0 / 10 | 0 |
| LEC 003 | Classroom Instruction | 0 / 10 | 0 |
| LEC 004 | Classroom Instruction | 0 / 10 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Recent recorded grades — Spring 2025: 3.86 GPA, 96.6% A/AB (n=29 letter grades); Fall 2025: 3.77 GPA, 100.0% A/AB (n=13 letter grades); Spring 2026: 3.92 GPA, 100.0% A/AB (n=6 letter grades).
No workload feedback recorded.
Latest available · Spring 2026— not enough history to project Spring 2027.
Through Spring 2026
Not enough comparable courses for Spring 2027 in UW–Madison.
Descriptions, prerequisites, and recorded course offerings.
Observations at scan time; dates do not imply when a catalog change took effect.
Data Science Programming I for Research
Recorded 2026-10-14[
{
"run_id": "20261014T203208-0851c9a1",
"semester": "1274",
"observed_at": "2026-10-14 17:50:09.728220+00:00",
"offering_id": "1274:266:025710",
"course_id": "COMPSCI 319",
"course_uid": "course_933b36d4952c8b40f181b993",
"term_id": "1274",
"source_course_id": "025710",
"source_subject_id": "266",
"title": "Data Science Programming I for Research",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Not Applicable"
}
]Original comments behind the course and instructor summaries.
No records available.
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Recorded model configuration, reasoning, and tool conversations.
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "61879aec9b7d2052d43c2faa9e3fe2a8af91cefd030fea5863f42f48d09a1e70",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}