Dual Degree in Economics (M.A.) and Data Science (M.S.)
The dual degree in economics (M.A.) and data science (M.S.) enables students to gain a deeper understanding of economic theory and computational methods while having the time and expertise to engage in research projects that link data science and economics. The dual degree requires 15 courses (45 credits), which can be taken sequentially or concurrently.
CIP Code
Economics (M.A.)
45.0603 - Econometrics and Quantitative Economics.Data Science (M.S.)
30.7001 - Data Science, General.
You can use the CIP code to learn more about career paths associated with this field of study and, for international students, possible post-graduation visa extensions. Learn more about CIP codes and other information resources.
The dual degree in economics (M.A.) and data science (M.S.) has the same admissions requirements as the M.A. in economics and the M.S. in data science.
These requirements are:
- completed online application
- three letters of recommendation for non-Fordham students; two for Fordham students
- official transcripts from all prior undergraduate and/or graduate institutions
- statement of intent
- official GRE test scores
- official TOEFL or IELTS scores for non-native English speakers.
Students will initially be admitted to either the M.A. in economics or the M.S. in data science and then apply for the dual degree (the other M.A./M.S.) once they are approximately 18 credits into their graduate study (i.e., completed four courses and are currently registered for at least two more).
Application to the second degree will only require an application form, a short statement of intent, and transcripts, with no fee for the application.
Economics Prerequisites
An undergraduate degree in a field emphasizing economics and/or quantitative skills is expected, such as a degree in economics or international political economy, or a degree in math, finance, psychology, computer science, or business with a minor in economics. The following courses or equivalent should be taken prior to beginning the M.A. in economics program:
- Intermediate-level Macroeconomics and Microeconomics
- Math for Economists OR Calculus I and Linear Algebra
- Statistics I and Statistics II (Statistical Decision Making)
If these classes were not completed with a previous degree, then the required classes will be added to a student's admission. These classes must be taken in the first semester or prior to beginning the program (e.g., during the summer or the previous semester).
Data Science Prerequisites
- Applicants with undergraduate degrees in non-computer science areas are welcome.
- An undergraduate degree in a field emphasizing quantitative skills is expected, such as a degree in computer science, information science, engineering, math, physical science, health science, business, economics, psychology, social science, or urban and city planning.
- Knowledge of discrete math, probability, and statistics, including permutations, combinations, descriptive statistics, and basic probability concepts.
- Basic programming knowledge and familiarity with Python programming are expected. This knowledge can be acquired via completion of CISC 5380 Programming with Python.
Admitted students who seek to bypass CISC 5380 Programming with Python must take a placement examination, which is administered by the department prior to the beginning of each entry term. The exam covers the fundamentals of Python programming language. Students who earn a grade lower than a B are required to enroll in CISC 5380 Programming with Python in their first semester of study. This bridge course can be taken concurrently with courses that fulfill degree requirements.
The requirements for the Dual Degree in Economics (M.A.) and Data Science (M.S.) are as follows:
| Course | Title | Credits |
|---|---|---|
| Economics Courses | ||
| Core Courses | ||
| ECON 6010 | Microeconomic Theory I | 3 |
| ECON 6020 | Macroeconomic Theory I | 3 |
| ECON 6910 | Applied Econometrics | 3 |
| or ECON 6950 | Financial Econometrics | |
| Economics Electives 1 | 9 | |
| Three courses from any of the following areas: | ||
Applied Microeconomics | ||
Finance | ||
Specialized Topics | ||
| Data Science Courses | ||
| Core Courses | ||
| CISC 5790 | Data Mining | 3 |
| CISC 5800 | Machine Learning | 3 |
| CISC 5950 | Big Data Computing | 3 |
| Data Science Electives (two courses) 1 | 6 | |
| One of the following options: 2 | 3 | |
| Capstone Project in Data Science | ||
| Master's Thesis in Data Science I and Master's Thesis in Data Science II | ||
| Data Science Practicum (internship) | ||
| Math Core | ||
| ECON 5710 | Mathematical Analysis in Economics | 3 |
| or CISC 5450 | Mathematics for Data Science | |
| Free Electives (two courses) 3 | 6 | |
| Total Credits | 45 | |
- 1
See below lists for courses that may fulfill this requirement. For students who did not complete an undergraduate major in economics and are pursuing this dual-degree program, ECON 5012 Foundations of Economics may also count as an economics elective.
- 2
Students completing two semesters of data science thesis (6 credits) may complete one fewer 3-credit data science elective.
- 3
Any course that counts as an economics or data science elective may fulfill this requirement.
Applied Microeconomics elective courses
Courses in this group have the EDAM attribute.
| Course | Title | Credits |
|---|---|---|
| ECON 5105 | Topics in Economic History | 3 |
| ECON 5260 | Epidemics and Development Policy | 3 |
| ECON 5280 | Urban Economics | 3 |
| ECON 5415 | Gender & Economic Development | 3 |
| ECON 5590 | Health Economics | 3 |
| ECON 5600 | Health and Development | 3 |
| ECON 6440 | Development Economics | 3 |
| ECON 6460 | Agriculture and Development | 3 |
| ECON 6480 | Environmental and Resource Economics | 3 |
| ECON 6970 | Applied Microeconometrics | 3 |
Finance elective courses
Courses in this group have the EDFI attribute.
| Course | Title | Credits |
|---|---|---|
| ECON 5006 | Programming Economics and Finance | 3 |
| ECON 6240 | Financial Economics | 3 |
| ECON 6340 | Financial Theory | 3 |
Specialized Topics elective courses
Courses in this group have the EDST attribute.
| Course | Title | Credits |
|---|---|---|
| ECON 5730 | Econometrics and Finance Using R - Part I | 3 |
| ECON 5750 | Game Theory | 3 |
| ECON 5760 | Computational Macroeconomics/Finance | 3 |
| ECON 6310 | Monetary Policy | 3 |
| ECON 6320 | Monetary Theory | 3 |
| ECON 6470 | Growth and Development | 3 |
| ECON 6510 | International Trade | 3 |
| ECON 6530 | International Economics of Growth and Development | 3 |
| ECON 6560 | International Finance | 3 |
| ECON 6990 | Topics in Econometric Theory | 3 |
Data Science elective courses
Courses in this group have the EDDS attribute.
| Course | Title | Credits |
|---|---|---|
| CISC 5325 | Database | 3 |
| CISC 5352 | Machine Learning in Finance | 3 |
| CISC 5500 | Data Analytics Tools and Scripting | 3 |
| CISC 5550 | Cloud Computing | 3 |
| CISC 5640 | Nosql Database Systems | 3 |
| CISC 5835 | Algorithms for Data Science | 3 |
| CISC 5900 | Information Fusion | 3 |
| CISC 5950 | Big Data Computing | 3 |
| CISC 6000 | Deep Learning | 3 |
| CISC 6210 | Natural Language Processing | 3 |
| CISC 6352 | Advanced Computational Finance | 3 |
| CISC 6525 | Artificial Intelligence | 3 |
| CISC 6745 | Data Visualization | 3 |