Spring 2027

Data Science Programming II

Data Science Programming II covers intermediate Python data science, including graph data structures, reproducibility tools, A/B testing, and basic ML techniques like clustering and classification.

offering recorded4 credits
Recorded instructors · Spring 2027 Gurmail Singh3.1/5

Summary

1 / 6

Past student reviews · Gurmail Singh

Grading was based on six biweekly projects, two midterms, and one final. Projects took a long time, and homework was described as heavy, ranging from easy to challenging.

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

  • conduct data science activities aware of and according to policy, privacy, security and ethical considerations

  • demonstrate oral, written and visual communication skills related to data science

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

COMP SCI 220, 300, 319, graduate/professional standing, or declared in the Computer Sciences for Professionals Capstone Certificate

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

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.96/5 from 72 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: 3.3/5 raw quality · 2.8/5 difficulty · 51 reviews

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

Past student reviews · Gurmail Singh

Gurmail Singh was seen by some as caring, reachable, and generous with extra credit, and one student described grading as six biweekly projects, two midterms, and a final, with online exams. Other students found his lectures boring, his notes inconsistent, or his explanations unclear, and one reported heavy homework and emotional strain.

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

Past student reviews · Yiyin Shen

Past students had mixed experiences with Yiyin Shen; some found her lectures clear and manageable, while others felt she assumed prior knowledge without detailed explanations. Projects took 2–6 hours each, with the final project described as fun. One student noted the class was fairly easy, though attendance was not strictly mandatory if quizzes were completed remotely.

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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 2500
LEC 002Classroom Instruction0 / 2500
LAB 311Classroom Instruction0 / 840
LAB 321Classroom Instruction0 / 840
LAB 312Classroom Instruction0 / 840
LAB 313Classroom Instruction0 / 840
LAB 322Classroom Instruction0 / 840
LAB 323Classroom Instruction0 / 840

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

Meeting source records
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Student experience

the class

Past student reviews · Gurmail Singh

Gurmail Singh is described as a caring, reachable professor who offers generous extra credit. Students noted he is clear with announcements and wants students to succeed, though opinions on his teaching style vary widely.

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

Past student reviews · Gurmail Singh

Grading was based on six biweekly projects, two midterms, and one final. Projects took a long time, and homework was described as heavy, ranging from easy to challenging.

Past student reviews · Gurmail Singh

Lectures were frequently cited as boring, disorganized, or hard to follow due to accent and volume issues. Some students found the TAs helpful, while others felt the lectures were unnecessarily long and off-topic.

Topics

  • Data structures for datasets, specifically graphs
  • Reproducibility, version control, and Python virtual environments
  • Dataset generation via tracing and A/B testing
  • Classification, clustering, optimization, and simulation
  • Plotting and visual communication

Skills

  • Implementing data structures like graphs for efficient dataset representation
  • Using software-engineering tools for reproducibility, including version control and virtual environments
  • Generating meaningful datasets using tracing and A/B testing
  • Applying basic classification, clustering, optimization, and simulation techniques
  • Creating plots and communicating visually

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 · 0.19 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 320 · Spring 2027

Data Science Programming II

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": "1274:266:025499",
    "course_id": "COMPSCI 320",
    "course_uid": "course_1dae8ec78980ea57dc44f5c9",
    "term_id": "1274",
    "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 2026-10-14

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 3Download part 4
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "9f22d1a8b9a1e999b993c3f932ac3aa46b5d39b1f1cde376929cfbd294f2e12f",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}