Spring 2027

Introduction to Big Data Systems

Introduction to Big Data Systems covers deploying distributed systems for storing and analyzing large datasets using unstructured and structured storage, query languages, streaming data, and machine learning in Python.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Tyler Caraza-harter

Assessment included two 30-minute quizzes and a long project requiring 15-20 hours for a small grade percentage.

What you’ll be able to do

  • Deploy distributed systems for data storage and analytics

  • Demonstrate competencies with tools and processes necessary for loading data into distributed storage systems

  • Write programs that use distributed platforms to efficiently analyze large datasets

  • Produce meaning from large datasets by training machine learning models in parallel or on distributed systems

  • Measure resource usage and overall cost of running distributed programs

  • Optimize distributed analytics programs to reduce resource consumption and program runtime

  • Demonstrate competencies with cloud services designed to store or analyze large datasets

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

COMP SCI 320, 400, or Graduate/Professional Standing

COMPSCI 544

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

MEENAKSHI SYAMKUMAR is recorded teaching in Spring 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

TYLER CARAZA-HARTER is recorded teaching in Fall 2023, Fall 2024, Spring 2025, Fall 2025, 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 / 1500
LEC 002Classroom Instruction0 / 1500

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 · Tyler Caraza-harter

The course covers practical software skills like Linux, Docker, and databases, which students found valuable for future jobs.

Recent recorded grades — Spring 2025: 3.31 GPA, 56.8% A/AB (n=380 letter grades); Fall 2025: 3.30 GPA, 56.3% A/AB (n=551 letter grades); Spring 2026: 3.25 GPA, 53.5% A/AB (n=353 letter grades).

difficulty & workload

Past student reviews · Tyler Caraza-harter

Assessment included two 30-minute quizzes and a long project requiring 15-20 hours for a small grade percentage.

Past student reviews · Meenakshi Syamkumar

Meenakshi Syamkumar's lectures were clear and to the point, with helpful resources like Anki flashcards and TAs available for support.

Topics

  • Unstructured and structured data storage approaches
  • Query languages, streaming data processing, and machine learning model training
  • Measuring resource usage and optimizing distributed analytics programs

Skills

  • Deploying distributed systems for data storage and analytics
  • Loading data into distributed storage systems
  • Training machine learning models in parallel or on distributed systems
  • Using cloud services to store or analyze large datasets

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
COMPSCI 544 · Spring 2027

Introduction to Big Data Systems

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": "1274:266:026531",
    "course_id": "COMPSCI 544",
    "course_uid": "course_4c46b17e9b3f9a4f2ed9a0e9",
    "term_id": "1274",
    "source_course_id": "026531",
    "source_subject_id": "266",
    "title": "Introduction to Big Data Systems",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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 2026-10-14

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 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2bbaaf57b5455c0e6e9025525fbab3042076bc7a6279961cd51c7a0c55a2701d",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}