Master of Science in Data Analytics

Extract Value

CIS - Student standing by the white board

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.

Program Highlights

  • 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

Program Basics

  • Curriculum requires 10 courses for a total of 30 credits, including four core courses, two advanced knowledge courses, three electives, and a capstone project or thesis
  • Designed as a one-to-two year program
  • Evening courses to accommodate working professionals

Admissions Requirements

  • 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:

  • Data analyst
  • Data scientist
  • Chief information officer

50 Best Jobs in America
According to the Glassdoor ®, four out of the top ten jobs in America are in Data Analytics.


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, social science or city and urban planning.

Professional knowledge or experience equivalent to the following three courses is required. This knowledge can be acquired via regularly offered courses, bridge courses specially designed to prepare students for the CIS graduate programs. When equivalent coursework has not been taken, placement examinations many be employed to determine if the student has the required knowledge.

  1. Computer Programming with basic algorithms (in C, C++, Java, R or Python) (e.g., CISC 5300 C++ Programming or CISC 5380 Programming with Python)
  2. Applied Statistics and Probability ( e.g., CISC 5420: Applied Statistics and Probability)
  3. Discrete Mathematics including basic combinatorics and graph theory (e.g., CISC 5400 Discrete Structures)


Core Courses (4) 12 Credits

  • CISC 5825 Computer Algorithms or CISC 5500 Knowledge and Information Management
  • CISC 6930 Data Mining (or CISC 6950 Algorithms and Data Analysis)
  • CISC 5600 NoSQL Database Systems or CISC 6325 Database Systems
  • CISC 5950 Big Data Programming
  • CISC 8054 Data Analytic Colloquium (0 credits)

Advanced Courses (2) 6 Credits

  • CISC 5800 Machine Learning
  • CISC 5900 Information Fusion

Electives Courses (3 courses from any one or more of the following thematic clusters) 9 credits

Big Data Computing and Networking

  1. CISC 5550 Cloud Computing
  2. CISC 5850 Social Networks
  3. CISC 5700 Cognitive Computing
  4. CISC 6735 Wireless Networks
  5. CISC 6950 Ensemble Methods

Information and Cyber Security

  1. CISC 5650 Cyber Security Essentials
  2. CISC 5728 Security of e-Systems and Networks
  3. CISC 5750 Information Security and Ethics
  4. CISC 6650 Forensic Computing

Bioinformatics and Health Informatics

  1. CISC 6500 Bioinformatics
  2. CISC 6700 Medical Informatics
  3. BISC 6525 Molecular Biology
  4. BISC 6525 Intro to Biostatistics (or BISC 6710 Seminar in Genetics)

Financial Informatics

  1. CISC 5350 Financial Programming
  2. CISC 6300 Computational Finance
  3. CISC 6350 Advanced Financial Programming
  4. ECON 6240 Financial Econometrics
  5. ECON 6910 Applied Economics

Urban and City Informatics

  1. CISC 5738 ICT Systems for Smart Cities
  2. URST 6000 Issues in Urban Studies
  3. URST 6200 Urban Studies Research Skills
  4. BISC 7529 Principles of GIS

Election and Government Informatics

  1. POGA 5100 American Political Behavior
  2. POGA 5130 Political Institutions and Processes
  3. POGA 5251 Political Survey Research

Behavior Informatics

  1. PSYC 6850 Evaluation of Psychological and Social Programs
  2. PSYC 7804 Regression or PSYC 7816 Multivariate Analysis
  3. PSYC 7830 Structural Equation Modeling, PSYC 7850 Hierarchical Linear Models, or PSYC 7920 Item Response Theory

Media Informatics

  1. PMMA 6103 Data Journalism and Interactive Graphics
  2. PMMA 6205 Online Analytics and Metrics

Capstone 3 credits

  1. CISC 8050 Project and Internship
  2. CISC 8054 Data Analytic Colloquium (0 credits)
  3. CISC 8798/8799 Master Thesis in Data Analytics

Tuition Rate for Professional Master's Programs

Please visit the GSAS Tuition and Fees page to view the tuition rate for the Computer and Information Science programs.


  1. What are the deadlines dates for Fall and Spring Admissions?
  1. What's available for graduate housing?
  1. Are GRE's required for this program?
  • The GRE's are not required, unless you are interested in applying for financial aid. Please see GSAS Funding website for further information.
  1. 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.