Skip to main content

M.S. in Data Analytics - Courses and Degree Requirements

Degree Requirements

The master’s program in Data Analytics requires 30 credits of coursework (10 classes), which will typically be completed in 1-2 years. Courses must be taken as follows:

  • Core knowledge: 5 courses
  • Electives: 4 courses from one of thematic clusters (below)
  • Capstone: Capstone Project in Data Analytics
  • Optional: 2 thesis courses instead of Capstone and Elective

Classes will be offered in the evenings and during the weekends.


Core Courses (Minimum 5 of the following 6) 15 Credits

  1. CISC 5500 Data Analytic Tools and Scripting
  2. CISC 5835 Algorithms for Big Data
  3. CISC 6930 Data Mining
  4. CISC 5950 Big Data Programming
  5. CISC 5800 Machine Learning
  6. CISC 5900 Information Fusion

Electives Courses (4 courses from any one or more of the following thematic clusters) 12 credits

  1. Big Data and Cloud Computing
    1. CISC 5550 Cloud Computing
    2. CISC 6735 Data Visualization
    3. CISC 5700 Cognitive Computing
    4. CISC 5009 Network Essentials
    5. CISC 6525 Artificial Intelligence
  2. Cybersecurity
    1. CISC 5650 Cyber Security Essentials
    2. CISC 6640 Privacy and Security in Big Data
    3. CISC 6680 Intrusion Detection
    4. CISC 5750 Information Security and Ethics
  3. 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)
  4. Financial Informatics
    1. CISC 5352 Financial Programming and Applications
    2.  CISC 6352 Advanced Computational Finance  
    3. ECON 6950 Financial Econometrics
    4. ECON 6910 Applied Economics
  5. 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
  6. Election and Government Informatics
    1. POGA 5100 American Political Behavior
    2. POGA 5130 Political Institutions and Processes
    3. POGA 5251 Political Survey Research
  7. 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
  8. Media Informatics
    1. PMMA 6103 Data Journalism and Interactive Graphics
    2. PMMA 6205 Online Analytics and Metrics

Internship 3 credits

  1. CISC 6081 Data Analytic Practicum

Students interested in an internship with a business or organization related to Data Analytics can take this course which will be counted towards his or her elective.

Capstone 3 credits

  1. CISC 6080 Capstone Project in Data Analytics

Master Thesis 6 credits

  1. CISC 6085 Master Thesis in Data Analytics I
  2. CISC 6086 Master Thesis in Data Analytics II


  • If prerequisites are added to a student’s admission letter then they must be taken in his or her first semester.
  • CISC 5500 Data Analytics Tools and Scripting must be taken in the first semester. (Computer Programming is a prerequisite for this course.)
  • CISC 6930 Data Mining must be taken in the first semester if possible and preferably be taken before CISC 5800 and CISC 5900.


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.

  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)

Bridging courses are available for students who are missing one or more of the aforementioned prerequisites. With the permission of the Program Director, these courses can be taken concurrently with the Data Analytics courses.