Catalog Navigator : Degree Requirements

For the most up-to-date course descriptions please visit the website.

Master of Information Technology and Analytics (M.I.T.A.) Program Curriculum (effective spring 2021)

Foundation Courses

Choose any three of the following four courses, 3 credits each:

22:544:643 Information Security
22:544:603 Business Data Management
22:544:641 Analytics for Business Intelligence (or 22:544:650 Data Mining)
22:544:613 Introduction to Data Structures and Algorithms (or 16:198:512 Introduction to Data Structures and Algorithms)

Concentrations

Students who opt for a concentration need to complete at least three courses from the respective concentration.

Operations Research and Business Analytics

26:711:651 Linear Programming
26:711:652 Nonlinear Programming
26:711:653 Discrete Optimization
26:960:575 Introduction to Probability
26:960:580 Stochastic Processes

Data Science and Machine Learning

26:198:642 Multimedia Information Systems
22:544:605 Introduction to Software Development
26:198:641 Advanced Database
22:198:646 Data Analysis and Visualization
22:544:631 Algorithmic Machine Learning
22:544:635 Neural Networks and Deep Learning
22:544:634 Optimization Methods for Machine Learning
22:544:637 Reinforcement Learning

Cyber Security

22:544:643 Information Security
26:198:645 Data Privacy
22:544:605 Introduction to Software Development
22:544:640 Fundamentals of Blockchain and Distributed Ledgers

Elective Courses

Master level and Ph.D. level courses are listed separately. Students registering for a Ph.D. level course require a special permission.

Master level courses:

22:544:688 MITA Capstone Project
22:544:605 Introduction to Software Development
22:544:608 Business Forecasting
22:544:638 MITA Internship (0 credits)
22:544:646 Data Analysis and Visualization
22:544:660 Business Analytics Programming
22:544:670 Information Technology Strategy
22:799:659 Supply Chain Solutions with ERP/SAP I
22:799:660 Supply Chain Solutions with ERP/SAP II
22:799:661 Introduction to Project Management
16:198:520 Introduction to Artificial Intelligence

Ph.D. level courses:

26:711:651 Linear Programming
26:711:652 Nonlinear Programming
26:711:653 Discrete Optimization
26:960:575 Introduction to Probability
26:960:580 Stochastic Processes
26:198:622 Machine Learning
26:198:641 Advanced Database Systems
26:198:642 Multimedia Information Systems
26:198:645 Data Privacy
26:198:685 Special Topics in Information Systems

Applications of Machine Learning to Big Data
Big Data: Management, Analysis, and Applications
Data-Intensive Analytics

26:711:555 Stochastic Programming
26:711:557 Dynamic Programming
26:711:685 Special Topics in Operations Research/Management Science
26:960:576 Financial Time Series
26:960:577 Introduction to Statistical Linear Models
22:711:685 Dynamic Pricing and Revenue Management

Policy on Ph.D. level courses (26 level codes)

The M.I.T.A. students are allowed to take any Ph.D. level course offered in the Department of Management Science and Information Systems as an elective. However, to protect the quality of those Ph.D. level courses, which are primarily for Ph.D. students, they are not explicitly mentioned in the electives list. To use Ph.D. and other graduate courses from other departments as electives, students must request and receive approval from the program directors on a case-by-case basis.

Policy on Business Courses in RBS

Some students with a strong prior technical background may be interested in taking graduate courses (e.g., M.B.A. courses) with strong business content from other RBS departments. Qualified M.I.T.A. students may take up to two such courses with the program directors' approval.

You can view current and past schedules for Rutgers here: http://sis.rutgers.edu/soc/#home.

Course descriptions of courses beginning with school 22 can be found at the M.B.A. Curriculum page, course descriptions for courses beginning with school 26 are listed under the Ph.D. course descriptions.