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Dec 04, 2025
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2025-2026 Undergraduate Catalog
AI and Quantitative Economics BS
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 Return to: Majors and Combined Degrees
AI and Quantitative Economics is a STEM-designated interdisciplinary B.S. degree program designed to teach students the cutting-edge quantitative skills necessary to analyze economic and business data, focusing on using AI techniques to predict, gain insights, and make decisions. The program combines core concepts from economics, computer science, and data science that aim to prepare students for careers requiring analytical skills and statistical modeling in economics, business, social sciences, government, academia, or any AI-related fields.
Visit the AI and Quantitative Economics academic program page for more information about the academic experience, who you will learn from, opportunities outside of class and what you can do with this degree.
Visit the Economics department page for contact information, a brief overview of the department and the curricular options.
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AI & Society Core (15 credits)
AI & Technology Core (16 credits)
Bridge Courses (6 credits)
AI & Society Capstone (3 credits)
AI in Quantitative Economics Core (38 credits)
Total Credits Required for Major: 86
Additional Degree Requirements Include:
- Additional coursework to fulfill UB Curriculum requirements
- Elective courses as needed to complete the 122 credit hour total
Total Credits Required for Graduation: 122
Total Credit Hours Required represents the minimum credits needed to complete this program, and may vary based on a number of circumstances. This should not be used for financial aid purposes. Academic Requirements
Minimum GPA of 2.000 overall. Curricular Plan
A Curricular Plan provides a roadmap for completing this academic program and the UB Curriculum on time. Your actual plan may vary depending on point of entry to the university, course placement and/or waivers based on standardized test scores, earned alternative credit and/or college transfer credit. All students are encouraged to use this plan in conjunction with other academic planning resources such as your academic advisor, the HUB Academic Advisement Report , My Planner and Path Finder tool. In addition to following this course roadmap, all other admission and academic requirements of this major as listed in the Undergraduate Catalog must be met in order to successfully complete this degree. YEAR 1
Fall Semester Spring Semester
YEAR 2
Fall Semester Spring Semester
YEAR 3
Fall Semester Spring Semester
YEAR 4
Fall Semester Spring Semester
TOTAL CREDITS REQUIRED: 122 Note: Some classes may count toward both a major and UB Curriculum requirement. Learning Outcomes
Society learning outcomes - Classifying and differentiating between major kinds of AI technologies and how they can be used to advance the social good in education, government, business, and other areas of society, while keeping in mind potential societal harms
- Acquiring knowledge of ethical issues surrounding the use and development of AI and applying that knowledge to determine how to use AI technologies ethically in a given context
- Situating new AI technologies with respect to earlier disruptive technologies in order to be able to compare and contrast AI with other major technological advances both in terms of societal good and harm.
- Assessing the impact of AI technologies on the development of policy proposals in both technological and nontechnological domains
- Determining how AI technologies interact with social structures and the ways that they can reinforce existing biases and structural inequities and developing ideas for how their use can mitigate such biases
- Identifying the impact of AI technologies on how people communicate with each other and assessing when these impacts are either harmful or beneficial to the social good
Technology learning outcomes - Mastering basic linear algebra skills such as defining vectors and matrices, computing their properties, and understating how they are used in AI computations
- Gaining an understanding of probability distributions, statistical properties of data, and their applications in AI
- Designing and implementing computational artifacts
- Applying foundational computational thinking skills and artifacts to analyze and interpret real-world data
- Understanding the social and ethical implications of AI computation
Experiential learning outcomes - Function effectively as a member or leader of a team engaged in activities appropriate to the program’s area of focus
- Apply concepts from linguistics and computer science to the application of human language technologies to real-world problems.
Integrative learning outcomes - Apply AI theory and methods to produce computationally-based solutions to problems related to geospatial analytics
Economics learning outcomes - Understand and apply key economic concepts and models in real-world situations
- Use programming languages, statistical tools, and AI tools to analyze and visualize economic data
- Communicate complex economic analysis and AI-driven insights to both technical and non-technical audiences
(HEGIS: 07.99 COMPUTER and INFORMATION SCIENCES UNCLASSIFD, CIP: Artificial Intelligence and Robotics 11.0102) |
Return to: Majors and Combined Degrees
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