Fall 2026

Introduction to Big Data Systems

Introduction to big data systems focusing on deploying distributed storage and analyzing large datasets using Python.

offering recorded3 credits
Recorded instructors · Fall 2026 Tyler Caraza-Harter2.8/5

Summary

1 / 6

Students report the course is time-consuming with confusing exams, unfair quiz conditions, and projects that are overly difficult compared to lecture content.

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

Fall 2026

Tyler Caraza-Harter

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 2.40/5 from 43 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.

Tyler Caraza-Harter is praised as organized and supportive, with clear lectures and abundant materials. Reviewers value his Big Data knowledge and industry focus.

Recent reviews criticize confusing quizzes, fast pacing, and difficult projects. Students report unfair quiz conditions and excessive theory focus.

Recent recorded grades — Spring 2025: 3.31 GPA, 56.8% A/AB (n=380 letter grades); Fall 2025: 3.33 GPA, 58.9% A/AB (n=270 letter grades); Spring 2026: 3.25 GPA, 53.5% A/AB (n=353 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews for Meenakshi Syamkumar are sharply divided. Some students praised her flexibility, helpful lecture code, and clear explanations, while others criticized her lectures as disorganized, shallow, and harmful to learning. Recent reviews also noted chaotic project management and unfair quiz practices.

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction293 / 3150

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

Tyler Caraza-Harter is praised for being organized, supportive, and knowledgeable about industry practices, with lectures providing a solid foundation for big data and software 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

Students report the course is time-consuming with confusing exams, unfair quiz conditions, and projects that are overly difficult compared to lecture content.

Lectures are criticized for moving too quickly and relying on demos rather than clear explanations, while exam questions are described as esoteric and prioritized over coding.

Topics

  • Structured and unstructured data storage
  • Query languages
  • Streaming data processing
  • Machine learning model training

Skills

  • Deploying and using distributed systems for large-scale data storage and analysis.
  • Implementing unstructured and structured data storage solutions.
  • Processing streaming data and training machine learning models.

Grades

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

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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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

1283 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 · Fall 2026

Introduction to Big Data Systems

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:266:026531",
    "course_id": "COMPSCI 544",
    "course_uid": "course_4c46b17e9b3f9a4f2ed9a0e9",
    "term_id": "1272",
    "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.

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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

Download the original dataset
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.

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Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "df865deff2cd5e537bc59123049adadb820e1c29eafca16b9e0a87f94400d555",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}