Pitfalls, Ethics, Communication, and Leadership in Business Analytics
Explores practical pitfalls, ethical considerations, and leadership challenges in business analytics applications.
Summary
What you’ll be able to do
Recognize potential warning signs of predictive models failing or overpromising.
Engage with ethical issues concerning analytics applications regarding autonomy, beneficience, and fairness.
Audit algorithms/predictions for potential biases.
Make strategic choices among analytics projects, including effective management of human resources.
Source: UW Guide
Grade history
↗No recorded grade history.
All recorded terms · compare terms & instructors
Prerequisites
Course mapGEN BUS 657(or GEN BUS 888 prior to Fall 2025) and GEN BUS 883
- GENBUS 657
- GEN BUS 888 prior to Fall 2025
- GENBUS 883
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
- Any of
- GENBUS 657
- GEN BUS 888 prior to Fall 2025
- GENBUS 883
- Any of
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Focus on applications, highlighting key practical issues in analytics projects. Discussion of pitfalls with regards to analytics applications, ethical aspects of analytics applications, as well as talent management and leadership in the context of analytics.
difficulty & workload
No workload feedback recorded.
Topics
- Pitfalls in analytics applications
- Ethical aspects of analytics
- Talent management and leadership in analytics
Skills
- Recognize warning signs of predictive model failure or overpromising
- Engage with ethical issues concerning analytics applications
- Audit algorithms and predictions for potential biases
- Make strategic choices among analytics projects and manage human resources
Grades
Grade distribution · % of letter grades
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More grade details Grade mix, volume & source data
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
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Selected offering source records
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Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "06f974ce122a6d18e99d7bb3f4d6b6dd71c7d04d10ba025f62859739c84abeae",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}