Spring 2027

Data Science & Engineering

Hands-on intro to Data Science & Engineering using Python for describing, analyzing, and predicting from real-world data, covering visualization and ethics.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Grigoris Chrysos

Homework and exams contained LeetCode-style problems that were often unrelated to lecture material, leading students to describe the assignments as impossible or way out of scope.

What you’ll be able to do

  • Write working code in Python to import, manipulate, analyze, visualize, and otherwise interact with datasets of various types.

  • Perform descriptive analyses to extract, summarize, and interpret salient features from datasets.

  • Perform predictive analyses to model trends and make predictions from datasets.

  • Apply techniques to identify and clean data that contains missing entries, outliers, or other forms of noise or uncertainty.

  • Recognize and evaluate potential issues pertaining to bias, fairness, privacy, and ethics in applying data science techniques.

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

MATH 112, 114, 171, or member of Engineering Guest Students

ECE 204

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

GRIGORIS CHRYSOS is recorded teaching in Spring 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 2027

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records
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Student experience

the class

Past student reviews · Grigoris Chrysos

Students indicated the course was better suited for those with prior Python experience, with one noting the data science lessons were interesting while another found it manageable for a senior.

Recent recorded grades — Fall 2024: 3.70 GPA, 90.1% A/AB (n=71 letter grades); Spring 2025: 3.60 GPA, 91.5% A/AB (n=82 letter grades); Fall 2025: 3.65 GPA, 85.7% A/AB (n=70 letter grades).

difficulty & workload

Past student reviews · Grigoris Chrysos

Homework and exams contained LeetCode-style problems that were often unrelated to lecture material, leading students to describe the assignments as impossible or way out of scope.

Past student reviews · Grigoris Chrysos

Students reported that the instructor was unresponsive or rude on Piazza, refusing to empathize with complaints about the difficulty or scope of assignments.

Topics

  • Data-centric and computational thinking.
  • Data importing, manipulation, summarization, and visualization.
  • Bias, fairness, and ethics in data science.

Skills

  • Python programming for importing, manipulating, summarizing, and visualizing data.
  • Describing, analyzing, and making predictions using real-world data.
  • Understanding bias, fairness, and ethics in data science.

Grades

Latest available · Fall 2025— 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 Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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

No offering records for the selected term.

Raw records 
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-29

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "b8b2036bc36b1c7fb73de06510785e5d54005710b690db0cb340d93dab840476",
  "requirements_status": "needs_review",
  "dataset_revision": "c9cd06eaa45f9ddf7e69faa38b89ff3d3dd995dd",
  "observed_at": "2026-10-29 21:30:40.248904+00:00"
}