Spring 2027

Data Science Programming I

Introduction to Data Science programming using Python with no prior experience required, emphasizing analysis of real datasets and visual communication.

offering recorded4 credits
Recorded instructors · Spring 2027 Blerina Gkotse3.7/5Michael Doescher3.5/5

Summary

1 / 6

Past student reviews · Michael Doescher

Projects were extremely time-consuming and often repetitive, but students appreciated that the grade was largely based on their completion rather than just exams.

What you’ll be able to do

  • 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

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Satisfied Quantitative Reasoning (QR) A or declared in the Professional Capstone Program in Computer Sciences. Not open to students with credit for COMP SCI 301.

      • Satisfied Quantitative Reasoning (QR) A
      • declared in the Professional Capstone Program in Computer Sciences
      take one
      • credit for COMP SCI 301
      not eligible with
    take all
COMPSCI 220 used by

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

Prerequisite text tree
  • All of
    • Any of
      • Satisfied Quantitative Reasoning (QR) A
      • declared in the Professional Capstone Program in Computer Sciences
    • Not eligible with
      • credit for COMP SCI 301

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

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.

Adjusted rating
RMP difficulty
captured reviews
About this rating

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.

For this course: 4.2/5 raw quality · 3.1/5 difficulty · 49 reviews

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

Past student reviews · Michael Doescher

Michael Doescher taught a project-heavy course in which projects were time-consuming, and one student also found them repetitive. He live-coded during lectures, which some found engaging, though another student said lectures did not help because he spent time on unrelated content.

Recent recorded grades — Spring 2025: 3.27 GPA, 57.4% A/AB (n=749 letter grades); Fall 2025: 3.36 GPA, 63.0% A/AB (n=915 letter grades); Spring 2026: 3.43 GPA, 70.5% A/AB (n=755 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Past student reviews · Meenakshi Syamkumar, Andrew Kuemmel

A student reported that Meenakshi Syamkumar moved quickly through lectures, sent over 14 emails weekly, and enforced harsh grading where a 95% was required for an A. Another student found Andrew Kuemmel's course poorly designed with overlapping deadlines, noting that grading varied by TA and that Kuemmel was strict about minor details.

ANDREW KUEMMEL is recorded teaching in Fall 2021, Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

GURMAIL SINGH is recorded teaching in Fall 2022, Spring 2023, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

LOUIS OLIPHANT is recorded teaching in Spring 2024, Fall 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

MEENAKSHI SYAMKUMAR is recorded teaching in Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

MICHAEL DOESCHER is recorded teaching in Spring 2020, 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.

PEYMAN MORTEZA is recorded teaching in Fall 2021. 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 002Classroom Instruction0 / 2350
LEC 001Classroom Instruction0 / 2350
LEC 003Classroom Instruction0 / 2350
LEC 004Classroom Instruction0 / 2350
LAB 311Classroom Instruction0 / 750
LAB 312Classroom Instruction0 / 750
LAB 313Classroom Instruction0 / 750
LAB 321Classroom Instruction0 / 750
LAB 322Classroom Instruction0 / 750
LAB 323Classroom Instruction0 / 750
LAB 331Classroom Instruction0 / 750
LAB 343Classroom Instruction0 / 750
LAB 341Classroom Instruction0 / 750
LAB 342Classroom Instruction0 / 750
LAB 332Classroom Instruction0 / 750
LAB 333Classroom Instruction0 / 750

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Michael Doescher

Michael Doescher was an extremely engaging lecturer with his live coding, making the class enjoyable even for students with no prior coding experience.

Recent recorded grades — Spring 2025: 3.27 GPA, 57.4% A/AB (n=749 letter grades); Fall 2025: 3.36 GPA, 63.0% A/AB (n=915 letter grades); Spring 2026: 3.43 GPA, 70.5% A/AB (n=755 letter grades).

difficulty & workload

Past student reviews · Michael Doescher

Projects were extremely time-consuming and often repetitive, but students appreciated that the grade was largely based on their completion rather than just exams.

Past student reviews · Michael Doescher

Attending office hours was crucial to succeed in the class.

Topics

  • Data Science programming with Python
  • Analysis of real datasets
  • Visual communication

Skills

  • Data Science programming in Python
  • Analyzing real datasets
  • Visual communication

Grades

Historical instructor

Spring 2027 · 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 5 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 · 0.16 GPA average error.

Grades over time

Through Spring 2027

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.

Download part 1
Selected offering source records
COMPSCI 220 · Spring 2027

Data Science Programming I

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": "1274:266:025498",
    "course_id": "COMPSCI 220",
    "course_uid": "course_834bc468ca49d8090d0f211f",
    "term_id": "1274",
    "source_course_id": "025498",
    "source_subject_id": "266",
    "title": "Data Science Programming I",
    "credits_min": 4,
    "credits_max": 4,
    "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 1Download part 2Download part 3
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "173c720d954d528c687c14b157e1107e648039077f852375bd438e96aedb5c92",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}