Data Science Practicum
Data Science Practicum turns statistics concepts into practice via realistic projects, translating vague descriptions and complex datasets into concise written and graphical analyses for decision makers.
Summary
Past student reviews · Hyunseung Kang
The class involved a significant amount of work, and some students felt that program scheduling conflicts made it difficult to allocate enough time to perform well in all assignments.
What you’ll be able to do
Frame real-world data problems into testable and solvable statistical problems.
Develop analysis/solutions that are fast, scalable, and robust
Communicate solutions concisely and clearly, creating understandable and accurate visual/tabular summaries of the data analysis, and responding to audience questions clearly
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapDeclared in Statistics: Statistics and Data Science MS
- Declared in Statistics: Statistics and Data Science MS
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Declared in Statistics: Statistics and Data Science MS
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
HYUNSEUNG KANG is recorded teaching in Spring 2018, Spring 2019, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2023, Fall 2024. 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 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Past student reviews · Hyunseung Kang
Students described the course as highly beneficial for data science careers, noting that Hyunseung Kang provided great feedback and that the content was useful for long-term professional development.
Recent recorded grades — Spring 2025: 3.73 GPA, 84.6% A/AB (n=13 letter grades); Fall 2025: 3.72 GPA, 88.9% A/AB (n=18 letter grades); Spring 2026: 3.88 GPA, 100.0% A/AB (n=21 letter grades).
difficulty & workload
Past student reviews · Hyunseung Kang
The class involved a significant amount of work, and some students felt that program scheduling conflicts made it difficult to allocate enough time to perform well in all assignments.
Past student reviews · Hyunseung Kang
Critics reported that grading criteria were unclear and heavily based on randomized group projects, with one student feeling they learned little because the instructor demanded strictly statistical answers rather than embracing machine learning approaches.
Topics
- Applying statistics concepts to realistic data science projects
- Translating vague project descriptions and complex datasets into summarized analyses
- Producing written and graphical interpretations for organizational decision makers
Skills
- Framing real-world data problems into testable and solvable statistical problems
- Developing analysis and solutions that are fast, scalable, and robust
- Communicating solutions concisely and clearly using visual and tabular summaries
Grades
Latest available · Spring 2026— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
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.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "a872391b3243d5d1e4fff6a15cbfe6bb1f84a6b0cb5de865a3b5d0fcba1461ba",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}