Master of Information Technology and Analytics

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:

• 22:544:605 Introduction to Software Development
• 22:544:608 Business Forecasting
• 22:544:636 Simulation Modeling
• 22:544:638 MITA Internship (0 credits)
• 22:544:646 Data Analysis & Visualization
• 22:544:647 Graph Methods and Network Analysis
• 22:544:650 Optimization Modeling for Business Analytics
• 22:544:653 Game Theoretic Methods for Strategic Decision Making
• 22:544:657 Risk Modeling, Optimization and Learning
• 22:544:660 Business Analytics Programming
• 22:544:670 Information Technology Strategy
• 22:544:688 MIT Capstone Project
• 22:799:659 Supply Chain Solutions with ERP/SAP 
• 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.