COMPSCI 220 introduces data science programming with Python, focusing on analyzing real datasets and visual communication, with no prior experience required.
Weekly projects constitute nearly half the grade and require significant time investment, while exams are multiple-choice and carry less weight.
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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.
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
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Michael Doescher is described as caring, knowledgeable, and accommodating, with clear lectures that help beginners understand material. Reviewers appreciate his transparency and desire for student success, noting that his teaching style supports those with little prior coding experience.
Some students criticize his live coding sessions as inefficient and note that lectures can move too fast for note-taking. Others find his humor inappropriate or distracting, and one reviewer compares the current class's organization favorably to previous courses they took.
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 reviews of Meenakshi Syamkumar: Meenakshi Syamkumar is criticized for moving too fast in lectures and having harsh grading standards, with one student noting a 94.6% resulted in a B- grade. Another review advises avoiding her class if you expect meaningful instructor responses or clear content delivery.
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.
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| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 299 / 301 | 0 |
| LEC 002 | Classroom Instruction | 199 / 200 | 0 |
| LAB 311 | Classroom Instruction | 75 / 75 | 0 |
| LAB 321 | Classroom Instruction | 55 / 55 | 0 |
| LEC 003 | Classroom Instruction | 201 / 203 | 0 |
| LAB 333 | Classroom Instruction | 68 / 67 | 0 |
| LEC 004 | Classroom Instruction | 300 / 302 | 0 |
| LAB 341 | Classroom Instruction | 75 / 75 | 0 |
| LAB 312 | Classroom Instruction | 75 / 75 | 0 |
| LAB 313 | Classroom Instruction | 75 / 75 | 0 |
| LAB 314 | Classroom Instruction | 74 / 75 | 0 |
| LAB 322 | Classroom Instruction | 72 / 73 | 0 |
| LAB 323 | Classroom Instruction | 72 / 73 | 0 |
| LAB 331 | Classroom Instruction | 67 / 67 | 0 |
| LAB 332 | Classroom Instruction | 66 / 67 | 0 |
| LAB 342 | Classroom Instruction | 75 / 75 | 0 |
| LAB 343 | Classroom Instruction | 75 / 75 | 0 |
| LAB 344 | Classroom Instruction | 75 / 75 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Michael Doescher teaches a project-heavy course that reviewers find thorough and helpful for learning, though the class is considered difficult for beginners 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).
Weekly projects constitute nearly half the grade and require significant time investment, while exams are multiple-choice and carry less weight.
Students report mixed experiences with lectures, citing either ineffective live coding sessions or fast pacing that makes note-taking difficult, alongside complaints about inappropriate jokes.
– average GPA
Approximate 80% prediction interval
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.
4 earlier forecasts · 0.10 GPA average error.
Through Fall 2026
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.
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.
Descriptions, prerequisites, and recorded course offerings.
Observations at scan time; dates do not imply when a catalog change took effect.
Data Science Programming I
Recorded 2026-09-07[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:266:025498",
"course_id": "COMPSCI 220",
"course_uid": "course_834bc468ca49d8090d0f211f",
"term_id": "1272",
"source_course_id": "025498",
"source_subject_id": "266",
"title": "Data Science Programming I",
"credits_min": 4,
"credits_max": 4,
"typically_offered": "Not Applicable"
}
]Original comments behind the course and instructor summaries.
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded model configuration, reasoning, and tool conversations.
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "0d2001e434bcf880b3c929f18247c233eeb9bc6c08ae4abedbc026f7b01362d3",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}