Students
Tuition Fee
USD 54,300
Start Date
2027-08-01
Medium of studying
On campus
Duration
3 semesters

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Details
Program Details
Degree
Masters
Major
Data Science | Economics
Area of study
Information and Communication Technologies | Mathematics and Statistics
Education type
On campus
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
USD 54,300
Intakes
Program start dateApplication deadline
2027-08-01-
About Program

Program Overview


Master of Science in Data Science and Quantitative Economics

A fusion of economic theory and data science

This M.S. program is the first of its kind on the east coast. In today's dynamic job market, employers increasingly seek candidates with a diverse skill set that integrates data analysis and economic expertise. Proficiency in traditional economic tools, statistical methods, and cutting-edge data science techniques is highly valued across a wide range of sectors and industries - from finance and health policy to economics development and family/disability studies.


Fordham's M.S. in data science and quantitative economics, offered through both the Economics and Computer and Information Science departments, addresses this urgent need by offering a distinct, interdisciplinary graduate program that will enable students to develop a deeper understanding of economic theory and computational methods.


Our students will gain a competitive edge through our comprehensive analytical training, which combines theoretical approaches with hands-on experience using state-of-the-art tools. This will equip graduates with the ability to tackle complex data problems with confidence and professionalism, giving them a clear advantage in today's data-driven world.


Program Details

  • Degree type: Master of Science
  • Credit hours: 30
  • Cost per credit: $1,810
  • Duration: 3 semesters of full-time study or 5 semesters on a half-time basis
  • Classes begin: August 2026
  • Applications due: April 1, 2026 (no merit-based financial aid); April 2, 2026 (rolling)
  • Location/Modality: Late afternoon and evening courses offered at the Lincoln Center and Rose Hill Campuses
  • Is there an accelerated option? No
  • Full-time or part-time

STEM Designation

The M.S. in data science and quantitative economics is a registered STEM-designated program, which permits a 24-month extension of post-completion Optional Practical Training (OPT), for a total eligibility of up to 36 months.


Program Highlights

  • First and only program of its kind currently available on the East Coast
  • Designed to be completed in 3 semesters of full-time study or 5 semesters on a half-time basis
  • Taught by faculty from both the economics and computer and information science departments, our interdisciplinary curriculum consists of 10 courses (30 credits):
    • 3 core economics courses
    • 2 core data science courses
    • 1 required math course taken either in the data science program (CISC 5450 Mathematics for Data Science) or the economics program (ECON 5710 Math Analysis)
    • 3 electives: (1) from data science, (1) from economics; and (1) free elective from either program
    • 1 capstone course, practicum, or a thesis with co-mentoring by a faculty member
  • Late afternoon or evening courses are offered at both Rose Hill and Lincoln Center campuses

Career Outlook

As the economy becomes increasingly data-driven, employers seek candidates who possess a deep understanding of economic theory and modern empirical statistical techniques, as well as strong computational skills.


Upon completion of the program, our graduates will gain the competitive edge to succeed in a wide range of careers. Employers may include private sector, NGO, and public sector across a variety of industries.


Career Insights and Opportunities

According to metrics drawn from LinkedIn, the following high-tech employers seek career professionals with degrees in STEM fields:


Additionally, employers are looking for candidates who possess these specific skills and competencies:


  • Data Science : Python, R, Stata, Matlab, C/C++, Go, Java, Excel
  • Big Data Technologies : Map Reduce, Spark, Hive
  • Data Visualization : Tableau, Qlickview, Quicksight
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