Fall 2026

Big Data Systems

COMPSCI 744 covers the design and implementation of big data processing systems, including cluster architecture, execution frameworks, and applications like batch analytics and machine learning.

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

Summary

1 / 5

Historical reviews of Shivaram Venkataraman: The course is heavy, requiring two 15-20 page paper readings per week alongside projects, assignments, and exams, making it appropriate for graduate students but potentially difficult for undergraduates.

Grade history

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average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI 744 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
  • Graduate/professional standing

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Shivaram Venkataraman: Shivaram Venkataraman is consistently praised for his clarity, engaging lectures, and inclusive atmosphere. Reviewers highlight his ability to extract core concepts from papers and structure the course phenomenally. He is described as smart, down-to-earth, and one of the best CS professors.

SHIVARAM VENKATARAMAN is recorded teaching in Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Spring 2024, 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

Fall 2026

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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

No records available.

Student experience

the class

Historical reviews of Shivaram Venkataraman: Shivaram Venkataraman is highly recommended for his smart, clear, and engaging lectures, with students finding the course structure phenomenal and the open-ended exams fun.

Recent recorded grades — Fall 2022: 3.96 GPA, 98.8% A/AB (n=85 letter grades); Spring 2024: 3.85 GPA, 90.0% A/AB (n=90 letter grades); Spring 2025: 3.80 GPA, 91.5% A/AB (n=71 letter grades).

difficulty & workload

Historical reviews of Shivaram Venkataraman: The course is heavy, requiring two 15-20 page paper readings per week alongside projects, assignments, and exams, making it appropriate for graduate students but potentially difficult for undergraduates.

Historical reviews of Shivaram Venkataraman: Students appreciate the inclusive atmosphere and the professor's ability to extract core concepts from papers, though the weekly reading load is noted as substantial.

Topics

  • Cluster architecture
  • Design goals: flexibility, performance, and fault tolerance
  • Popular execution frameworks
  • Basic abstractions in big data systems
  • Applications: batch analytics, stream processing, graph processing, and machine learning

Skills

  • Design and implementation of big data processing systems
  • Understanding cluster architecture
  • Applying design goals like flexibility, performance, and fault tolerance
  • Using popular execution frameworks and basic abstractions
  • Applying big data systems to analytics, streaming, graph processing, and ML

Grades

Latest available · Spring 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 Spring 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 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

1289 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
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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.

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Dataset & model

Recorded 2026-09-07

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Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
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": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "520da3dd56f7b5853e33e5e8fb1b420e6c6ed700dc80d18549b0369f4f39d05b",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}