Artificial Intelligence for Business Joint Concentration

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:

1. Foundations of AI in Business (or F)
2. Impact and Ethical Implications of AI in Business (or I)

Courses in category “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 in category “I” cover the business, economic, legal, ethical, psychological, or societal impact of AI.

Requirements for Concentration:

Students will be required to fulfill a course requirement for each pillar:

“F”– STAT 4230: Applied Machine Learning in Business (1 CU)

“I”– LGST 2420: Big Data, Big Responsibilities: Toward Accountable Artificial Intelligence (0.5 CU)

Thus, 1.5 CU comes from required courses.

Students are then required to complete an equivalent of 4 CU in total, by taking other courses in the two course categories:

• 2 CU’s must be from Category “F” (of which 1 CU is a required class, see above).
• 2 CU’s must be from Category “I” (of which 0.5 CU is a required class, see above).

Of these, 1 CU can come from a list of approved courses outside Wharton. 

Courses outside of Wharton must be approved by the faculty advisors.

Category “F” Approved Courses (2 CU’S REQUIRED)

FNCE 2370: Data Science for Finance
FNCE 2800: FinTech
HCMG 3570: Healthcare Data and Analytics
MKTG 7370: Introduction to Brain Science for Business
MKTG 2680: Contagious: How Things Catch On
OIDD 2210: Introduction to Management Science / Optimization & Analytics
OIDD 4690: Information Strategy & Economics
OIDD 4770: Introduction to Python for Data Science
OIDD 4810: Convex Optimization for Statistics and 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

Category “I” Approved Courses (2 CU’S REQUIRED)

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

Courses taken on a pass/fail basis cannot be counted toward the concentration. A maximum of 1 CU of an Independent Study Project (ISP) can count toward the concentration with advisor approval.