Data Science & Quantitative Economics (M.S.)
The economics and computer and information sciences (CIS) departments offer an interdisciplinary M.S. degree program in data science and quantitative economics. This interdisciplinary degree program was developed in response to the increasing importance of computational methods and data analytic skills in the job market.
Students who pursue this degree option gain a deeper understanding of economic theory and computational methods, while engaging in research projects that link data science and economics. This degree entails 10 courses (30 credits) essential to both economic and data science domains, which can be taken sequentially or concurrently as needed.
The benefit of this program is that it allows students to combine powerful theoretical approaches with modern tools to understand complex problems. It prepares students well for careers in data-driven professions by providing strong analytic training with hands-on applications across a variety of fields such as finance, health policy, environmental policy, economic development, family and disability studies, and monetary policy.
Learning Goals
The master's in data science & quantitative economics program enables students to attain, by the time of graduation:
- A background in economic theory through instruction in ECON 6010 Microeconomic Theory I and ECON 6020 Macroeconomic Theory I.
- Updated skills in math and statistics through CISC 5450 Mathematics for Data Science or ECON 5710 Mathematical Analysis in Economics, and through ECON 6910 Applied Econometrics or ECON 6950 Financial Econometrics.
- Advanced technical skills to build and assess computational models and apply data science concepts and methods to economics data through CISC 5790 Data Mining and CISC 5800 Machine Learning.
- An understanding of the theoretical framework of big data processing and hands-on experience in big data analytics and its applications.
- An ability to effectively communicate data science-related information to any audience and transform findings into actionable solutions to real-world problems.
CIP Code
45.0603 - Econometrics and Quantitative Economics.
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 M.S. in data science & quantitative economics has the same admissions requirements as the M.A. in economics and the M.S. in data science.
These requirements are: completed online application, 3 letters of recommendation (for non-Fordham students, 2 letters for Fordham undergraduates), official transcripts from all prior undergraduate and/or graduate institutions, statement of intent, official GRE test scores, and official TOEFL or IELTS scores for non-native English speakers.
English Proficiency Requirements
International applicants whose native language is not English are required to complete and submit to GSAS prior to matriculation their official scores from one of the following accepted English language competency exams:
- Test of English as a Foreign Language (TOEFL) - GSAS accepts the following TOEFL tests:
- TOEFL iBT (including the Home Edition and Paper Edition
- TOEFL Essentials
- International English Language Testing System (IELTS)—Cambridge English Proficiency Level
- Duolingo English Test
- PTE Academic
- Cambridge English Qualifications - We accept the B2 First, C1 Advanced, or C2 Proficiency exams
Official TOEFL, IELTS, DET, PTE Academic, or Cambridge English Qualifications scores should be sent directly by the testing service to the Office of Graduate Admissions, Fordham University, Graduate School of Arts and Sciences (our ETS TOEFL score code #2259).
Preferred minimum score requirements:
| Exam | Score |
|---|---|
| TOEFL iBT | 100 |
| IELTS | 7.0 band score |
| DET | 130 |
| PTE Academic | 68 |
| Cambridge English Qualifications | 185 Overall Score on the B2, C1 Advanced, or C2 Proficiency exam |
Exemptions to the English Language Requirement
Exemptions from this requirement can be requested by the applicant in her/his application, or can be made in writing by the applicant to fuga@fordham.edu. Exemptions are generally permitted for international applicants who:
- are native English speakers from countries where English is an official language; and/or
- have completed, within the past five years, at least two years of study at an undergraduate or graduate institution in the United States or in a country where English is the official language of instruction.
GSAS retains the right to request language evaluation from any applicant. The Fordham English Language Test (FELT), administered by Fordham's Institute of American Language and Culture (IALC), may be required for those students whose English proficiency scores do not meet GSAS program requirements. Additional coursework may also be recommended by the IALC.
Students are permitted to register for two GSAS courses during the academic term in which they are completing any IALC-recommended coursework, which generally occurs during their first semester of study.
Please note: Tuition costs associated with the learning of English as a second language are the responsibility of the student and will not be covered by a GSAS tuition scholarship. GSAS merit-based tuition scholarships are not applicable to the costs of additional coursework recommended by the IALC.
For more information about admissions to the Graduate School of Arts and Sciences, please visit their page on the Fordham website.
Prerequisites
Economics
An undergraduate degree in a field emphasizing economics and/or quantitative skills—such as a degree in economics or mathematical economics; or a degree in math, finance, psychology, computer science, or business with a minor in economics—is expected. The following courses or equivalent should be taken prior to beginning the M.S. in data science & quantitative economics program:
- Intermediate-level Macroeconomics and Microeconomics
- Math for Economists OR Calculus I and Linear Algebra
- Statistics I and Statistics II
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 previous semester). Alternatively, we offer a "bridge course," ECON 5012 Foundations of Economics, for students in quantitative fields without an economics background. This bridge course can be taken concurrently with courses that fulfill degree requirements.
Data Science
- 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 probabilities concepts.
- Basic programming knowledge and a familiarity with Python programming is 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 (CISC 0940), which is administered by the Computer and Information Sciences department prior to the beginning of each entry term. The exam covers 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 M.S. in data science & quantitative economics requires completion of 30 credits, as follows:
| Course | Title | Credits |
|---|---|---|
| Economics Core | ||
| ECON 6010 | Microeconomic Theory I | 3 |
| ECON 6020 | Macroeconomic Theory I | 3 |
| ECON 6910 | Applied Econometrics | 3 |
| or ECON 6950 | Financial Econometrics | |
| Data Science Core | ||
| CISC 5790 | Data Mining | 3 |
| CISC 5800 | Machine Learning | 3 |
| Math Core | ||
| ECON 5710 | Mathematical Analysis in Economics | 3 |
| or CISC 5450 | Mathematics for Data Science | |
| Electives | ||
| One Economics elective drawn from any of the following areas: 1 | 3 | |
Applied Microeconomics | ||
Finance | ||
Special Topics | ||
| One Data Science elective 2 | 3 | |
| One Economics OR Data Science elective 1 | 3 | |
| Capstone/Internship/Thesis (one of the following) | 3 | |
| Capstone Project Option | ||
| Capstone Project in Data Science | ||
or ECON 6080 | Capstone Project in Economics | |
| Internship Option | ||
| Data Science Practicum | ||
or ECON 6081 | Economics Practicum | |
| Thesis Option 3 | ||
| Select one of the following: | ||
| Master's Thesis in Data Science I and Master's Thesis in Data Science II | ||
| Master's Thesis in Economics I and Master's Thesis in Economics II | ||
| Total Credits | 30 | |
- 1
See below for lists of courses fulfilling this requirement. Economics electives can be drawn from any of the three areas.
- 2
See list below of courses that fulfill this requirement.
- 3
Completion of a thesis requires 6 credits. Students who complete a 6-credit thesis will take one less elective as part of the degree.
Economics Electives
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 Electives
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 |