Master of Information Technology and Analytics

Curriculum effective for students admitted in Spring 2021 and beyond

Foundation

Choose any 3 of the following 4 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 3 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

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

Masters level courses:

  • 22:544:688 MIT 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 & 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
  • 26:198:643 Information Security
  • 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
  • Dynamic Pricing and Revenue Management

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

The MITA students are allowed to take any Ph.D. level course offered in the MSIS Department 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 course in RBS

Some students with a strong prior technical background may be interested in taking graduate courses (e.g., MBA courses) with strong business content from other RBS departments. Qualified MITA students may take up to two such courses with the program directors' approval.

Curriculum effective for students admitted before Spring 2021

Students must complete 30 credits, usually in the form of 10 courses. The 10 courses must include:

  • 3 foundation course
  • 3 core courses from the specific concentration
  • 4 elective courses

Foundation

Choose any 3 of the following 4 courses, 3 credits each

  • 26:198:643 Information Security
  • 22:544:603 Business Data Management
  • 22:544:641 Analytics for Business Intelligence (or 22:544:650 Data Mining)
  • 26:544:688 MIT Capstone Project

Concentrations

At least 3 courses, 3 credits each course

Operations Research and Business Analytics

  • 26:198:685 Introduction to Algorithms & Data Structure (or 16:198:512 Intro to Data Structures and Algorithms)
  • 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

Information Systems

  • 26:198:685 Introduction to Algorithms & Data Structure (or 16:198:512 Intro to Data Structures and Algorithms)
  • 26:198:642 Multimedia Information Systems
  • 22:544:605 Introduction to Software Development
  • 22:630:586 Marketing Management
  • 22:799:659 Supply Chain Solutions with ERP/SAP I

Information Assurance

  • 22:010:577 Accounting for Managers
  • 26:010:653 Auditing
  • 26:198:643 Information Security
  • 26:198:645 Data Privacy
  • 22:544:605 Introduction to Software Development

Elective Courses

At least 4 courses, 3 credits each

  • 26:010:653 Auditing
  • 16:198:552 Computer Networks
  • 26:198:622 Machine Learning
  • 26:198:641 Advanced Database Systems
  • 26:198:642 Multimedia Information Systems
  • 26:198:643 Information Security
  • 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
    • Introduction to Algorithms & Data Structure
  • 16:332:568 Software Engineering of Web Applications
  • 22:544:523 Business Statistics
  • 22:544:575 Data Analysis & Decisions
  • 22:544:605 Introduction to Software Development
  • 22:544:608 Business Forecasting
  • 22:544:638 MITA Internship (0 credits)
  • 22:544:646 Data Analysis & Visualization
  • 22:544:660 Business Analytics Programming
  • 22:544:670 Information Technology Strategy
  • 22:630:604 Marketing Research
  • 26:630:675 Marketing Models
  • 22:630:679 Web Analytics
  • 26:711:530 Semidefinate and Second Order Cone Programming
  • 26:711:555 Stochastic Programming
  • 26:711:557 Dynamic Programming
  • 26:711:564 Optimization Models in Finance
  • 26:711:685 Special Topics in Operations Research/Management Science
    • Game Theory
    • Convex Analysis and Optimization
    • Theory of Boolean Functions
  • 22:799:580 Operations Analysis
  • 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
  • 26:799:660 Supply Chain Modeling and Algorithms
  • 26:799:661 Stochastic Models for Supply Chain Management
  • 26:799:685 Special Topics in Supply Chain Management
  • 26:960:575 Introduction to Probability
  • 26:960:576 Financial Time Series
  • 26:960:577 Introduction to Statistical Linear Models

Aspiring Ph.D. Candidates

Students who will later pursue doctoral study in information technology or accounting information systems will have the opportunity to take many of the same courses as students in the Rutgers Business School doctoral programs in information technology, accounting information systems, and operations research.

If you plan to apply to the RBS Ph.D. program in Information Technology, Accounting Information Systems, or Operations Research, your project should be a doctoral-level research paper, and you should include as many school 26 courses as possible while in the Master of Information Technology program.

Additional Information

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

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