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 6010Microeconomic Theory I3
ECON 6020Macroeconomic Theory I3
ECON 6910Applied Econometrics3
or ECON 6950 Financial Econometrics
Economics Electives 19
Three courses from any of the following areas:
Applied Microeconomics
Specialized Topics
Data Science Courses
Core Courses
CISC 5790Data Mining3
CISC 5800Machine Learning3
CISC 5950Big Data Computing3
Data Science Electives 16
One of the following options: 23
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 5710Mathematical Analysis in Economics3
or CISC 5450 Mathematics for Data Science
Free Electives 36
Total Credits45

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.


Students completing two semesters of data science thesis (6 credits) may complete one fewer 3-credit data science elective.


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 5105Topics in Economic History3
ECON 5260Epidemics and Development Policy3
ECON 5280Urban Economics3
ECON 5415Gender & Economic Development3
ECON 5590Health Economics3
ECON 5600Health and Development3
ECON 6440Development Economics3
ECON 6460Agriculture and Development3
ECON 6480Environmental and Resource Economics3
ECON 6970Applied Microeconometrics3

Finance elective courses

Courses in this group have the EDFI attribute.

Course Title Credits
ECON 5006Programming Economics and Finance3
ECON 6240Financial Economics3
ECON 6340Financial Theory3

Specialized Topics elective courses

Courses in this group have the EDST attribute.

Course Title Credits
ECON 5730Econometrics and Finance Using R - Part I3
ECON 5750Game Theory3
ECON 5760Computational Macroeconomics/Finance3
ECON 6310Monetary Policy3
ECON 6320Monetary Theory3
ECON 6470Growth and Development3
ECON 6510International Trade3
ECON 6530International Economics of Growth and Development3
ECON 6560International Finance3
ECON 6990Topics in Econometric Theory3

Data Science elective courses

Courses in this group have the EDDS attribute.

Course Title Credits
CISC 5500Data Analytics Tools and Scripting3
CISC 5550Cloud Computing3
CISC 5640Nosql Database Systems3
CISC 5835Algorithms for Data Science3
CISC 5900Information Fusion3
CISC 6000Deep Learning3
CISC 6210Natural Language Processing3
CISC 6525Artificial Intelligence3
CISC 6735Wireless Networks3
CISC 6745Data Visualization3