Spring 2027

Graphs and Networks in Data Science

Covers mathematical foundations of networks for data science, focusing on algorithmic graph theory, linear algebra, network statistics, and PageRank.

offering recorded3 credits
Recorded instructors · Spring 2027 Sebastien Roch3.1/5

Summary

1 / 6

Past student reviews · Hanbaek Lyu

Deadlines were flexible with frequent homework extensions, while tests were challenging but manageable with consistent study.

What you’ll be able to do

  • State formal definitions in graph theory and networks.

  • Describe essential network search algorithms (e.g., shortest path and minimum spanning tree) and prove their correctness and computational complexity.

  • Understand how community detection algorithms using spectral analysis works; implement and/or use community detection algorithms and apply them to real-world networks using Python.

  • Simulate various random graph models (Erdos-Renyi, Watts-Strogats, Stochastic Block Model, Configuration model, Preferential Attachment).

  • Use a programming language to load network data and apply deterministic and randomized algorithms for network data analysis; fit various random graph models to the given real-world network and infer its statistical properties.

  • Identify applications of course content in areas of modern research.

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 320, 340, 341, 345, or 375) and (COMP SCI 200, 220, 300, 310, or placement into COMP SCI 300), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)

MATH 444

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

Prerequisite text tree

Professors

Spring 2027

Sebastien Roch

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.23/5 from 13 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.

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

No course-specific feedback yet.

Historical instructors & teaching patterns

HANBAEK LYU is recorded teaching in Fall 2023, Spring 2025. 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 / 750

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 · Hanbaek Lyu

Professor Han was described as caring and supportive, creating a learning environment where students felt looked after.

Recent recorded grades — Fall 2023: 3.75 GPA, 80.0% A/AB (n=20 letter grades); Spring 2025: 3.52 GPA, 57.1% A/AB (n=63 letter grades); Spring 2026: 2.97 GPA, 40.6% A/AB (n=69 letter grades).

difficulty & workload

Past student reviews · Hanbaek Lyu

Deadlines were flexible with frequent homework extensions, while tests were challenging but manageable with consistent study.

Past student reviews · Hanbaek Lyu

Students found the tests insightful and noted that success depended on working throughout the course rather than last-minute cramming.

Topics

  • Basics of graph theory and network statistics
  • Graph traversal algorithms and matrix methods
  • Community detection and PageRank
  • Simulation of random graph models

Skills

  • Stating formal definitions in graph theory and networks
  • Describing and proving correctness of network search algorithms
  • Simulating random graph models
  • Applying algorithms for network data analysis and fitting random graph models

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
MATH 444 · Spring 2027

Graphs and Networks in 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": "1274:600:026526",
    "course_id": "MATH 444",
    "course_uid": "course_8b31dcae72f6377cdea586b3",
    "term_id": "1274",
    "source_course_id": "026526",
    "source_subject_id": "600",
    "title": "Graphs and Networks in Data Science",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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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
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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": "f770767c9c522dde1a3b88f67731067b58c1e0ca83ecb1c130d169a974170bf2",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}