Spring 2027

Data Exploration, Cleaning, and Integration for Data Science

Focuses on variety and veracity challenges in data science, covering data exploration, cleaning, and integration for large datasets.

offering recorded3 credits
Recorded instructors · Spring 2027 Anhai Doan4.1/5

Summary

1 / 3

Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=51 letter grades); Spring 2026: 3.77 GPA, 81.8% A/AB (n=66 letter grades).

What you’ll be able to do

  • Identify and examine the key challenges of managing variety and veracity with large data sets. These include data acquisition, data extraction, data exploration, cleaning, matching, and merging data.

  • Summarize the variety and veracity solution approaches in academic and industry.

  • Design and apply course concepts to experiential learning through a research project.

  • Effectively communicate through written reports, oral presentations, and discussions.

Source: UW Guide

Grade history

↗
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 774 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

Spring 2027

Anhai Doan

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.69/5 from 16 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

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
Download part 1

Student experience

the class

Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=51 letter grades); Spring 2026: 3.77 GPA, 81.8% A/AB (n=66 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Big Data characteristics (volume, velocity, variety, veracity)
  • Data management, machine learning, big data scaling, cloud, crowdsourcing, and user interaction techniques
  • Data acquisition, extraction, exploration, cleaning, matching, and merging

Skills

  • Managing variety and veracity in large data sets, including acquisition, extraction, exploration, cleaning, matching, and merging
  • Summarizing variety and veracity solution approaches in academic and industry contexts
  • Communicating effectively through written reports, oral presentations, and discussions

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.

Download part 1
Selected offering source records
COMPSCI 774 · Spring 2027

Data Exploration, Cleaning, and Integration for 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": "1274:266:026802",
    "course_id": "COMPSCI 774",
    "course_uid": "course_5ab2d4759486b00da0bb13b5",
    "term_id": "1274",
    "source_course_id": "026802",
    "source_subject_id": "266",
    "title": "Data Exploration, Cleaning, and Integration for Data Science",
    "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

No records available.

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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "8116ee6576e1ff918cf52f7e0efb5c0f724e76fbadd382059d0f28329c824201",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}