Spring 2027

Data Technology for Business Analytics

Covers data analytics technologies including extracting structured/unstructured data, using Python for web scraping and social network mining, and leveraging libraries for advanced analysis.

offering recorded2–3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Spring 2025: 3.58 GPA, 80.0% A/AB (n=30 letter grades); Fall 2025: 3.86 GPA, 94.1% A/AB (n=101 letter grades); Spring 2026: 3.69 GPA, 84.4% A/AB (n=32 letter grades).

What you’ll be able to do

  • Extract data from both structured or unstructured databases

  • Transform and combine data with other relevant information and load into targeted systems such as data warehouses, data marts or analytical applications

  • Use Python to execute simple web scraping, mine data from social networks, and use the libraries for advanced data analysis beyond simple descriptive analytics

  • Implement Online Analytical Processing (OLAP) and create multi-dimension data cubes

  • Execute social mining techniques and create valuable information from text-mining for decision-making

  • Discuss current landscape in data warehousing, big data, and other emerging topics

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
GENBUS 760 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

No instructors recorded for this selection.

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

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2025: 3.58 GPA, 80.0% A/AB (n=30 letter grades); Fall 2025: 3.86 GPA, 94.1% A/AB (n=101 letter grades); Spring 2026: 3.69 GPA, 84.4% A/AB (n=32 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Extracting structured and unstructured data
  • Online Analytical Processing (OLAP) and multi-dimension data cubes
  • Data warehousing, big data, and emerging topics

Skills

  • Extract data from structured and unstructured databases
  • Transform, combine, and load data into data warehouses and analytical applications
  • Use Python for web scraping, social network mining, and advanced data analysis
  • Implement OLAP and create multi-dimension data cubes
  • Execute social mining and text-mining for decision-making

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.

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Selected offering source records
GENBUS 760 · Spring 2027

Data Technology for Business 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": "1274:231:025348",
    "course_id": "GENBUS 760",
    "course_uid": "course_05bf90f93cd632fab7dab908",
    "term_id": "1274",
    "source_course_id": "025348",
    "source_subject_id": "231",
    "title": "Data Technology for Business Analytics",
    "credits_min": 2,
    "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": "6a9e7db82494bc4bcbd67274077a6d470c13a962d643fe71b48d6f76a0a7398e",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}