BAN5532

INTRO AI FOR DATA SCI/BUS AN

Terms offered
4
Data refreshed
Jun 27, 2026

Description

This is an introductory course in Artificial Intelligence for Data scientist and Business Analysts, for business analysts to leverage data and AI to make informed strategic business decisions. The course is intended for students who desire to learn AI to improve their skills in new emerging advanced technology and eventually become business managers and leaders. Students who are interested in opportunities as data scientists, marketing analysts, product managers, and team leaders looking to further their careers are among the pertinent profiles. The course emphasizes a theoretical and case-based approach that helps to simulate operational instances where AI can be used to enhance corporate goals. Artificial intelligence has progressively expanded its influence over enterprises worldwide, resulting in a steady demand for AI professionals. While the emphasis has been on the opportunities for trained professionals, their efforts must still be oriented towards competitive business results. Modern organizations should take advantage of the skills and expertise of emerging AI professionals to maximize productivity and achieve market leadership. It will address fundamental ideas in machine learning and artificial intelligence, allowing learners to gain an intuitive understanding without being overly focused on complex technical specifics. This course does not require any prior coding expertise, rather it focuses on building AI business models (no coding).

Syllabi

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Offering history & forecast 2023–2026

2023
2026
Sections offered by academic year and season, with estimated offering chances for future years above the divider
YearFallSpringSummerIntersession
2030 2030 Fall: 7% chance, medium confidence. Offered in 0 of 9 fall terms since 2018. 2030 Spring: 7% chance, medium confidence. Offered in 0 of 9 spring terms since 2018. 2030 Summer: 40% chance, low confidence. Offered in every summer term since 2023. Department course count falling about 23% per year. Department faculty is about 55% below its recent average. 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020.
2029 2029 Fall: 6% chance, medium confidence. Offered in 0 of 10 fall terms since 2017. 2029 Spring: 6% chance, medium confidence. Offered in 0 of 10 spring terms since 2017. 2029 Summer: 44% chance, low confidence. Offered in every summer term since 2023. Section count rising about 1% per year over 2 terms. Department course count falling about 17% per year. Department faculty is about 54% below its recent average. 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017.
2028 2028 Fall: 5% chance, medium confidence. Offered in 0 of 11 fall terms since 2016. 2028 Spring: 5% chance, medium confidence. Offered in 0 of 11 spring terms since 2016. 2028 Summer: 48% chance, low confidence. Offered in every summer term since 2023. Section count rising about 1% per year over 3 terms. Department course count falling about 11% per year. Department faculty is about 52% below its recent average. 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017.
2027 2027 Fall: 5% chance, high confidence. Offered in 0 of 12 fall terms since 2015. 2027 Spring: 5% chance, high confidence. Offered in 0 of 12 spring terms since 2015. 2027 Summer: 51% chance, low confidence. Offered in every summer term since 2023. Department course count falling about 11% per year. Department faculty is about 52% below its recent average. 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017.
2026 Not offered Not offered 1 section No data for this term
2025 Not offered Not offered 2 sections Not offered
2024 Not offered Not offered 1 section Not offered
2023 Not offered Not offered 1 section Not offered
Forecast Under 15% 15–39% 40–69% 70–89% 90%+ Not enough history
Fall Spring Summer Intersession Darker means more sections Not offered No data for term

Future estimates combine recent offering frequency with detected multi-year cycles. Hover a cell for confidence and evidence; low-confidence estimates are marked with ~.

Meeting times

Most recent first
  • Mon, Wed, 6:00 PM–9:00 PM ONLINE 2025-2
Credit-bearing sections of BAN5532
TermProfessorTypeCreditsEnrollmentCRN
Summer 2026Abdullah AsilkalkanOnline1 14 of 30 enrolled11550
Summer 2025Hari C. KrishnaOnline1 10 of 30 enrolled12014
Summer 2025Abdullah AsilkalkanOnline1 29 of 30 enrolled11550
Summer 2024Hari C. KrishnaOnline1 25 of 30 enrolled11550
Summer 2023Hari C. KrishnaOnline1 11 of 30 enrolled11550