Campus: NYC, Westchester
The Computational Economics (BS) major is an interdisciplinary major that teaches cutting-edge quantitative skills using the power of computer science and information technology. Students will learn how to program, work with big data, and apply sophisticated quantitative techniques (i.e. AI, Machine Learning, Econometrics) to answer questions in economics and business practices. The major is STEM designated and provides students with highly demanded skills across a variety of industries and jobs in the private and public sector.
| Requirement | Credits |
|---|---|
| University Core Requirements | 44 |
| Major Requirements | 60-62 |
| Open Electives | 7-17 |
| Total Credits | 120 |
| Code | Title | Credits |
|---|---|---|
| University Core Courses | ||
| ENG 110 | Composition | 3 |
| ENG 120 | Critical Writing | 4 |
| ENG 201 | Writing in the Disciplines | 3-4 |
| COM 200 | Public Speaking | 3 |
| Math Options | ||
| MAT 144 | Introduction to Probability and Statistics for Economics | 4-8 |
| or MAT 131 & MAT 234 | Calculus I and Introduction to Probability and Statistical Analysis | |
| or MAT 111 & MAT 134 | Elementary Calculus I and Introduction to Probability and Statistics | |
| or MAT 104 & MAT 117 | Finite Mathematics and Elementary Statistics | |
| CS 121 | Introduction to Computer Science | 4 |
| or CIS 101 | Introduction to Computing | |
| or CIT 110 | Introduction to Information Technology | |
| Code | Title | Credits |
|---|---|---|
| Economics Core Courses | ||
| ECO 105 | Principles of Economics: Macroeconomics | 0-3 |
| ECO 106 | Principles of Economics: Microeconomics | 0-3 |
| ECO 230 | Intermediate Macroeconomics | 3 |
| ECO 234 | Intermediate Microeconomics | 3 |
| ECO 240 | Quantitative Analysis and Forecasting | 3-4 |
| ECO 380 | Mathematical Economics | 3-4 |
| ECO 385 | Econometrics: Models and Organizations | 3 |
| ECO 389 | Economic Data Analysis (R & Python) | 3 |
| CS/CIT Core Courses | ||
| CIT 241 | Database Management | 4 |
| CS 377 | Mathematical Foundations of Machine Learning | 4 |
| CIT 380 | Applied AI with Deep Learning | 4 |
| CS/CIT Electives | 8 | |
| Additional ECO or CS/CIT Electives | 6-8 | |
| Capstone Course | ||
| CS 489 | Computational Economics Capstone | 3 |
| First Year | ||
|---|---|---|
| Fall | Credits | |
| UNV 101 | First-Year Seminar: Introduction to University Community | 1 |
| ENG 110 | Composition | 3 |
| CS 121 | Introduction to Computer Science | 4 |
| ECO 106 | Principles of Economics: Microeconomics | 3 |
| One Learning Community (LC) Course | 3 | |
| Take first Mathematics requirement. See advisor. | 0-4 | |
| Credits | 14-18 | |
| Spring | ||
| ENG 120 | Critical Writing | 4 |
| ECO 105 | Principles of Economics: Macroeconomics | 3 |
| Take any one remaining Area of Knowledge course or Open Elective course | 3 | |
| CIT 110 | Introduction to Information Technology | 3 |
| Take the second Mathematics requirement. See advisor. | 0-4 | |
| Credits | 13-17 | |
| Second Year | ||
| Fall | ||
| ECO 230 | Intermediate Macroeconomics | 3 |
| First Language Course, if applicable | 3 | |
| Take any one remaining Area of Knowledge course and Writing Enhanced (WE) course | 3 | |
| MAT 144 | Introduction to Probability and Statistics for Economics (Only take if MAT requirement not fulfilled) NYC Campus only | 4 |
| ECO 380 | Mathematical Economics | 3-4 |
| Credits | 16-17 | |
| Spring | ||
| Second Language Course, if applicable | 3 | |
| Take any one remaining Area of Knowledge and Writing Enhanced (WE) course | 3 | |
| Take any one remaining Area of Knowledge | 3 | |
| CIT 241 | Database Management | 4 |
| ECO 240 | Quantitative Analysis and Forecasting | 3-4 |
| Credits | 16-17 | |
| Third Year | ||
| Fall | ||
| COM 200 | Public Speaking | 3 |
| CS 377 | Mathematical Foundations of Machine Learning | 4 |
| Take Lab Science course | 3 | |
| One elective course in subject ECO | 3 | |
| ECO 385 | Econometrics: Models and Organizations | 3 |
| Credits | 16 | |
| Spring | ||
| ENG 201 | Writing in the Disciplines | 3-4 |
| ECO 389 | Economic Data Analysis (R & Python) | 3 |
| Take one CS/CIT course | 4 | |
| ECO 234 | Intermediate Microeconomics | 3 |
| Credits | 13-14 | |
| Fourth Year | ||
| Fall | ||
| Take one Anti-Racist Education (ARE) course | 3 | |
| CS 489 | Computational Economics Capstone | 3 |
| Take any one remaining Area of Knowledge course | 3 | |
| Take one Civic Engagement (CE) course | 3 | |
| CIT 380 | Applied AI with Deep Learning | 4 |
| Take any one remaining Area of Knowledge course | 3 | |
| Credits | 19 | |
| Spring | ||
| Take any one remaining Area of Knowledge course | 3 | |
| Take one CS/CIT course | 4 | |
| Take one Anti-Racist Education (ARE) course | 3 | |
| One elective course in subject ECO | 3 | |
| Credits | 13 | |
| Total Credits | 120-131 | |
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2022-2023 Undergraduate Catalog
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