Fall 2026

Data Science Programming II

COMPSCI 320 teaches intermediate data science programming in Python, covering data structures, reproducibility, and machine learning techniques.

offering recorded4 credits
Recorded instructors · Fall 2026 Gurmail Singh3.1/5

Summary

1 / 7

The workload consists of biweekly projects, midterms, and a final, with exams being online and attendance effectively optional due to full credit policies.

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

COMP SCI 220(or COMP SCI 301 prior to Spring 2020),COMP SCI 300, 319, graduate/professional standing, or declared in the Computer Sciences for Professionals Capstone Certificate

    • COMPSCI 220
    • COMP SCI 301 prior to Spring 2020
    • COMPSCI 300
    • COMPSCI 319
    • graduate/professional standing
    • declared in the Computer Sciences for Professionals Capstone Certificate
    take all
COMPSCI 320 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
    • COMPSCI 220
    • COMP SCI 301 prior to Spring 2020
    • COMPSCI 300
    • COMPSCI 319
    • graduate/professional standing
    • declared in the Computer Sciences for Professionals Capstone Certificate

Professors

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 2.99/5 from 71 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: 3.4/5 raw quality · 2.7/5 difficulty · 50 reviews

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

Gurmail Singh is described as caring, reachable, and easy to talk to, with students recommending him highly. Reviewers note his grading is fair and simple, often facilitated by generous extra credit opportunities on exams and assignments.

Critics report that Singh rarely explains material well, with lectures described as confusing, inconsistent, or poorly spoken. Some students found the lectures boring or disorganized, relying instead on recorded materials or TAs for understanding the content.

Recent recorded grades — Spring 2025: 3.83 GPA, 91.9% A/AB (n=455 letter grades); Fall 2025: 3.80 GPA, 91.1% A/AB (n=460 letter grades); Spring 2026: 3.77 GPA, 90.1% A/AB (n=365 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews for Peyman Morteza describe him as patient and helpful during office hours.

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

MEENAKSHI SYAMKUMAR is recorded teaching in Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

YIYIN SHEN is recorded teaching in Fall 2022, Fall 2023, Spring 2024. Recorded history may be incomplete and does not establish a future schedule.

YOUNG WU is recorded teaching in Fall 2023, 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 Instruction197 / 00
LAB 311Classroom Instruction51 / 500
LAB 312Classroom Instruction48 / 500
LAB 313Classroom Instruction48 / 500
LAB 314Classroom Instruction50 / 500
LEC 002Classroom Instruction195 / 00
LAB 321Classroom Instruction50 / 500
LAB 322Classroom Instruction50 / 500
LAB 323Classroom Instruction49 / 500
LAB 324Classroom Instruction46 / 500

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

Gurmail Singh is praised by some students as an easy, caring instructor who offers generous extra credit and simple grading, making the course accessible for those who follow project directions.

Recent recorded grades — Spring 2025: 3.83 GPA, 91.9% A/AB (n=455 letter grades); Fall 2025: 3.80 GPA, 91.1% A/AB (n=460 letter grades); Spring 2026: 3.75 GPA, 88.9% A/AB (n=488 letter grades).

difficulty & workload

The workload consists of biweekly projects, midterms, and a final, with exams being online and attendance effectively optional due to full credit policies.

Many reviewers criticize Singh's lectures as confusing, inconsistent, and poorly explained, noting that students often rely on recorded lectures from previous instructors or office hours with helpful TAs.

Topics

  • Data structures (graphs)
  • Software engineering tools (version control, virtual environments)
  • Tracing and A/B testing
  • Classification, clustering, optimization, simulation

Skills

  • Reproducible analysis with version control
  • Tracing and A/B testing
  • Classification, clustering, optimization, and simulation
  • Plotting and visual communication

Grades

Historical instructor

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 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 · 0.25 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.

Download part 1
Selected offering source records
COMPSCI 320 · Fall 2026

Data Science Programming II

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": "1272:266:025499",
    "course_id": "COMPSCI 320",
    "course_uid": "course_1dae8ec78980ea57dc44f5c9",
    "term_id": "1272",
    "source_course_id": "025499",
    "source_subject_id": "266",
    "title": "Data Science Programming II",
    "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 2026-09-07

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 1Download part 2
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "38b4feb19acf6de7f2d2882ecc7e787853bea3050d3022d5bd081aa05c1e9657",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}