Effectively analyzing big data helps agencies and organizations become better, smarter, and faster—which is why experts in data analytics are in great demand. Fordham’s master’s degree in data analytics will prepare you for a career in this fast-growing field by giving you the tools to find the story behind vast amounts of data.
You’ll develop the technical abilities and the communication skills to analyze and interpret data in real time. You’ll also benefit from our small class sizes where you’ll have the chance to interact with faculty members who have expertise both in the field and in the classroom.
Analyze and manipulate large sets of data
Gain in-depth knowledge of at least one area of specialization
Ability to take electives in economics and urban studies
Curriculum requires 10 courses for a total of 30 credits, including five core courses, four electives, and a capstone project or thesis
Designed as a one-to-two year program
Evening courses to accommodate working professionals
Applicants with undergraduate degrees in non-computer science areas are welcome
GRE is recommended, but not required
International students: TOEFL minimum 85*, IELTS equivalent 6.5 (visit ETS TOEFL or call 1-800-GO-TOEFL. Fordham’s GSAS Institution code is 2259. Allow four to six weeks for scores to be processed and sent to Fordham)
*Applicants with TOEFL scores below 85 may still apply
Experts in data analysis who can draw meaningful insights from data are highly sought after in a number of fields, including government, finance, marketing, and health care. Job titles include:
Official GRE scores ARE REQUIRED (not simply "recommended") if an applicant wishes to be considered for GSAS merit-based financial aid. GREs for this program are otherwise recommended..
Are conditional acceptances offered if prerequisites are required?
There are no conditional acceptances, only changes to degree requirements, such as added coursework taken while students are in the program or obtaining a satisfactory grade on a placement test, in order to meet pre-requisite requirements.