Spring 2027

Digital Platform Analytics

Introduction to digital platform analytics covering data collection, pre-processing, analysis, presentation, structured/unstructured data, supervised/unsupervised learning, text mining, recommendation systems, and algorithm bias.

offering recorded3 credits
Recorded instructors · Spring 2027 Qinglai He3.8/5

Summary

1 / 6

Past student reviews · Qinglai He

The course received a low difficulty rating from a student who found the class engaging due to the instructor's passion for the topics.

What you’ll be able to do

  • Recognize and apply fundamental data analysis procedures, including data collection, pre-processing, analysis, and presentation.

  • Analyze structured and unstructured data in a variety of contexts.

  • Develop supervised and unsupervised data mining skills, including linear regression, logistics regression, decision tree, KNN, K-means, and simple text mining techniques.

  • Evaluate data analysis frontiers, including recommendation systems, data ethics, privacy, and algorithm bias.

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

None

  • None
INFOSYS 423

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

Prerequisite text tree
  • None

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 5.00/5 from 2 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.

Past student reviews · Qinglai He

Students described Qinglai He as one of their best and most caring professors, noting her passion made the class engaging. They reported she deeply wanted every student to succeed and was genuinely sweet, contributing to a highly positive learning experience.

Recent recorded grades — Spring 2024: 3.44 GPA, 66.7% A/AB (n=9 letter grades); Spring 2025: 3.50 GPA, 69.1% A/AB (n=55 letter grades); Spring 2026: 3.64 GPA, 82.1% A/AB (n=67 letter grades).

Historical instructors & teaching patterns

QINGLAI HE is recorded teaching in Spring 2024, Spring 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 / 360
LEC 002Classroom Instruction0 / 360

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 · Qinglai He

Professor Qinglai He was described as passionate and caring, creating an engaging environment where she genuinely wanted every student to succeed.

Recent recorded grades — Spring 2024: 3.44 GPA, 66.7% A/AB (n=9 letter grades); Spring 2025: 3.50 GPA, 69.1% A/AB (n=55 letter grades); Spring 2026: 3.64 GPA, 82.1% A/AB (n=67 letter grades).

difficulty & workload

Past student reviews · Qinglai He

The course received a low difficulty rating from a student who found the class engaging due to the instructor's passion for the topics.

Past student reviews · Qinglai He

Students called Qinglai He the best professor they had ever had, noting she was incredibly caring and good at her job.

Topics

  • Data collection, pre-processing, analysis, and presentation
  • Structured and unstructured data
  • Supervised/unsupervised learning, text mining, recommendation systems, algorithm bias

Skills

  • Data collection, pre-processing, analysis, and presentation
  • Supervised and unsupervised data mining methods
  • Text mining, recommendation systems, and algorithm bias evaluation

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
INFOSYS 423 · Spring 2027

Digital Platform Analytics

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": "1274:234:026602",
    "course_id": "INFOSYS 423",
    "course_uid": "course_66917ac2b710b102608b82c8",
    "term_id": "1274",
    "source_course_id": "026602",
    "source_subject_id": "234",
    "title": "Digital Platform Analytics",
    "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
Download part 1
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": "24921ca6456f31bd89280f0ebf0cea609b5abb94c17aed237f8acddbae62601e",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}