Spring 2027

Text Analytics and Business Application

Introduction to text mining and NLP for business, covering data cleaning, representation, and predictive analytics.

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

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.93 GPA, 96.6% A/AB (n=29 letter grades); Spring 2025: 3.62 GPA, 76.5% A/AB (n=17 letter grades); Spring 2026: 3.81 GPA, 88.9% A/AB (n=18 letter grades).

What you’ll be able to do

  • Retrieve, assemble, and clean text data for use in analytics applications.

  • Utilize text representation approaches, including the bag of words and Term Frequency - Inverse Document Frequency (TF-IDF), for visualizing text data.

  • Build classification and regression models using features created from text data.

  • Utilize Latent Dirichlet Application (LDA) for topic modeling in the context of a business application.

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 or declared in graduate Business Exchange program

    • Graduate/professional standing
    • declared in graduate Business Exchange program
    take one
INFOSYS 723

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

Prerequisite text tree
  • Any of
    • Graduate/professional standing
    • declared in graduate Business Exchange program

Professors

Spring 2027
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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.

Recent recorded grades — Spring 2024: 3.93 GPA, 96.6% A/AB (n=29 letter grades); Spring 2025: 3.62 GPA, 76.5% A/AB (n=17 letter grades); Spring 2026: 3.81 GPA, 88.9% A/AB (n=18 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 / 300

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 2024: 3.93 GPA, 96.6% A/AB (n=29 letter grades); Spring 2025: 3.62 GPA, 76.5% A/AB (n=17 letter grades); Spring 2026: 3.81 GPA, 88.9% A/AB (n=18 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Representation approaches and topic modeling
  • NLP applications: chatbots and recommender systems

Skills

  • Text data preparation and analytics application
  • Text representation methods like Bag of Words and TF-IDF
  • Building classification and regression models from text features
  • Topic modeling with Latent Dirichlet Allocation

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
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D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

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
INFOSYS 723 · Spring 2027

Text Analytics and Business Application

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": "1274:234:026609",
    "course_id": "INFOSYS 723",
    "course_uid": "course_40c1c73bbdc9a1068fe35f7c",
    "term_id": "1274",
    "source_course_id": "026609",
    "source_subject_id": "234",
    "title": "Text Analytics and Business Application",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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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 2026-10-14

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.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2f29708f0c91008685dce96c10acc550fa44915cb9b9b1b90d7097cb555a18e8",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}