Spring 2027

Political Data Science

Political Data Science covers basics of applying data science to politics, focusing on computing/programming tools for managing and analyzing political data and conceptual foundations of analysis methods.

offering recorded3–4 credits
Recorded instructors · Spring 2027 Soeren Henn

Summary

1 / 6

Past student reviews · Jack Edelson

Reviewers felt the material was too difficult for an elementary level, with R concepts rushed and a final project requiring application of all taught material.

What you’ll be able to do

  • Summarize and visualize data from political science

  • Wrangle messy political science data into tidy forms

  • Evaluate claims about causality in political science research

  • Be able to use linear regression to analyze relationships in political science

  • Understand uncertainty in analysis of political data and how to quantify it

  • Understand election polling and forecasting

  • Analyze political data using professional tools such as R

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

  • Satisfied Quantitative Reasoning (QR) A
POLISCI 270 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
  • Satisfied Quantitative Reasoning (QR) A

Professors

Spring 2027

Recent recorded grades — Fall 2024: 3.53 GPA, 80.9% A/AB (n=47 letter grades); Spring 2026: 3.67 GPA, 84.4% A/AB (n=90 letter grades).

Historical instructors & teaching patterns

JACK EDELSON is recorded teaching in Fall 2017. Recorded history may be incomplete and does not establish a future schedule.

SOEREN HENN is recorded teaching in Fall 2024, 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 / 1020
DIS 302Classroom Instruction0 / 170
DIS 303Classroom Instruction0 / 170
DIS 304Classroom Instruction0 / 170
DIS 305Classroom Instruction0 / 170
DIS 306Classroom Instruction0 / 170
DIS 307Classroom Instruction0 / 170

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 · Jack Edelson

Students described the course as a low-level class that expected difficult coding without adequate support.

Recent recorded grades — Fall 2022: 3.82 GPA, 95.2% A/AB (n=42 letter grades); Fall 2024: 3.53 GPA, 80.9% A/AB (n=47 letter grades); Spring 2026: 3.67 GPA, 84.4% A/AB (n=90 letter grades).

difficulty & workload

Past student reviews · Jack Edelson

Reviewers felt the material was too difficult for an elementary level, with R concepts rushed and a final project requiring application of all taught material.

Past student reviews · Jack Edelson

Past students found lectures disorganized and unhelpful, leading to unclear concepts and a frustrating final project experience.

Topics

  • Elections, surveys, and experiments in political science.
  • Election polling and forecasting.
  • Causality in political science research.

Skills

  • Using computing and programming tools to manage and analyze political science data.
  • Wrangling messy political science data into tidy forms.
  • Analyzing political data using professional tools such as R.
  • Using linear regression to analyze relationships in political science.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 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

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.

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Selected offering source records
POLISCI 270 · Spring 2027

Political Data Science

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": "1274:778:015875",
    "course_id": "POLISCI 270",
    "course_uid": "course_99345b3492738e258d4be6a0",
    "term_id": "1274",
    "source_course_id": "015875",
    "source_subject_id": "778",
    "title": "Political Data Science",
    "credits_min": 3,
    "credits_max": 4,
    "typically_offered": "Fall, Spring"
  }
]
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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "9ef3f14a0e908af8b104765f2166b19b5103e097ef1d537326846accd350aaad",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}