The Artificial Intelligence for Business joint concentration between the OID and STAT departments is designed to address two broad topics: (1) the (more) technical understanding of methods and how they are being applied by firms to solve business problems and (2) the (more) conceptual understanding of how the technology impacts firms and society, including economic, social, and ethical issues that AI deployment introduces.
Reflecting this conceptualization, the concentration has two pillars, F&I:
- Foundations of AI in Business (or F)
- Impact and Ethical Implications of AI in Business (or I)
Courses from Pillar F cover foundational skills needed to understand, work with, and/or evaluate AI systems. Such courses can cover, but are not limited to, subjects such as data engineering, data science, statistics, machine learning, or neuroscience.
Courses from Pillar I cover the business, economic, legal, ethical, psychological, or societal impact of AI.
REQUIREMENTS FOR CONCENTRATION:
The concentration in Artificial Intelligence for Business requires a total of 4 CU.
- Courses taken on a pass/fail basis cannot be counted toward the major.
- A maximum of 1 CU of an Independent Study Project (ISP) can count toward the major with faculty advisor approval.
- A maximum of 1 CU of courses outside of Wharton may be considered with faculty advisor approval.
Students must complete the following courses, one from each pillar, for a total of 1.5 CU:
Pillar F
STAT 4230: Applied Machine Learning in Business (1 CU)
Pillar I
LGST 2420: Big Data, Big Responsibilities: Toward Accountable Artificial Intelligence (0.5 CU)
The remaining 2.5 CU must be taken as follows:
Pillar F – Complete an additional 1 CU:
FNCE 2370: Data Science for Finance
FNCE 2800: FinTech
HCMG 3570: Healthcare Data and Analytics
MKTG 2370: Introduction to Brain Science for Business
MKTG 2680: Contagious: How Things Catch On
OIDD 2210: Introduction to Management Science / Optimization & Analytics
OIDD 3190: Advanced Decision Systems: Agents, Games, and Evolution
OIDD 3250: Computational Simulation Models
OIDD 4690: Information Strategy & Economics
OIDD 4770: Introduction to Python for Data Science
STAT 4240: Text Analytics
STAT 4420: Introduction to Bayesian Data Analysis
STAT 4710: Modern Data Mining
STAT 4810: Convex Optimization for Statistics and Data Science
STAT 4850: Foundations of Deep Learning with Applications
Pillar I – Complete an additional 1.5 CU:
MGMT 2140: Technology and Innovation Strategy
MKTG 2340: Idea Generation & the Systematic Approach for Creativity
MKTG 2270: Analytics and AI in Digital Marketing and Social Media
MGMT 2360: Innovation, Change, and Entrepreneurship
MGMT 2430: Work and Technology: Choices and Outcomes
MKTG 2790: AI in Our Lives: The Behavioral Science of Autonomous Technology
OIDD 2550: Artificial Intelligence, Business, and Society
Artificial Intelligence for Business Joint Concentration Advisors
Prof. Prasanna Tambe (OIDD) – tambe@wharton.upenn.edu
Prof. Giles Hooker (STAT) – ghooker@wharton.upenn.edu
See the Wharton AI & Analytics Initiative (https://ai-analytics.wharton.upenn.edu/) for an outline of the strategic initiative on AI and Analytics (WAIAI), created in direct response to growing student interest in AI courses and AI tools and platforms.