Fall 2026

Data Science & Engineering

Introduction to Data Science using Python for data analysis and prediction.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Grigoris Chrysos: Students report that homework assignments take hours and are often impossible, requiring serious independent study. Exams are described as incredibly tough and unrelated to lecture material.

Grade history

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

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

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Grigoris Chrysos: Grigoris Chrysos taught ECE 204 in Spring 2024. Reviewers report that he treated the introductory course as advanced, assigning out-of-scope homework and exams featuring difficult LeetCode-style problems. Students criticized his poor empathy, belittling Piazza responses, and refusal to adjust expectations for novices.

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

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

Meeting source records

No records available.

Student experience

the class

Historical reviews of Grigoris Chrysos: Reviewers describe Grigoris Chrysos’s ECE 204 as unexpectedly difficult, with exams and homework containing out-of-scope material and LeetCode-style problems. Many students recommend avoiding the course or having prior Python experience.

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

Historical reviews of Grigoris Chrysos: Students report that homework assignments take hours and are often impossible, requiring serious independent study. Exams are described as incredibly tough and unrelated to lecture material.

Historical reviews of Grigoris Chrysos: Reviewers criticize Chrysos for being rude on Piazza and ignoring student concerns about the course difficulty. One student found the data science lessons interesting, but others found the experience frustrating.

Topics

  • Data-centric and computational thinking
  • Bias, fairness, and ethics in data science

Skills

  • Programming in Python
  • Importing, manipulating, summarizing, and visualizing data
  • Describe, analyze, and make predictions using data
  • Notions of bias, fairness, and ethics in data science

Grades

Latest available · Fall 2025— not enough history to project Fall 2026.

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

1320 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

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
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
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 1
Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "fddaf3237854089af308b7fea1ffe55eb0a3dce02db0f1cc413ae302af488aa1",
  "requirements_status": "valid",
  "dataset_revision": "b68f68efcd3387a01fee00416f57beb6d43f3bcb",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}