Plan of Study
The MSE Online is a part-time, 24-month-long program, consisting of six semesters, one of which is dedicated to term paper writing. The program typically begins in the Fall semester, and consists of synchronous meetings with program faculty, and asynchronous lectures and assignments.
Courses are typically divided into 7-week long "mini" courses that allow students to concurrently learn a wider range of topics, while scaffolding learning to more advanced topics later in the program. For example, the sample Semester 1 has three mini courses, two that run in parallel for the first half of the semester (17-611 and 17-612), and one that runs by itself in the second half of the semester (17-623). The communications classes are 3-units and meet once a week for the entire semester.
A dedicated student can anticipate completing the program in 6 semesters (2 years). The maximum amount of time allowed to complete the program is 7 years.
Sample Course of Study
Semester 1
17-603 Communications for Software Leaders I
17-611 Statistics for Decision Making
17-612 Business & Marketing Strategy
17-623 Quality Assurance
Semester 2
17-604 Communications for Software Leaders II
17-632 Software Project Management
17-635 Software Architecture
17-642 Software Management Theory
Semester 3
17-643 Quality Management
+ 12 Elective Units (see Note on Elective Courses below)
Semester 4
17-614 Formal Methods
17-626 Requirements for Information Systems or 17-627 Requirements for Embedded Systems
17-622 Agile Methods
Semester 5
17-636 DevOps: Engineering for Secure Development and Deployment
+ 12 Elective Units (see Note on Elective Courses below)
Semester 6
17-679 Thesis Writing for Software Leaders
Note on Elective Courses
Examples of elective courses taken include:
05-692 Interaction Design Overview10-601 Introduction to Machine Learning
10-703 Deep Reinforcement Learning & Control
11-611 Natural Language Processing
15-319, 15-619 Cloud Computing
17-625 API Design
17-630 Prompt Engineering
17-634 Applied Machine Learning
17-644 Applied Deep Learning
17-647 Data Intensive and Scalable Systems
17-660 Designing and Managing Software Systems Platforms
17-685 Dynamic Network Analysis
17-691 Machine Learning in Practice
17-692 Product Management Essentials for Engineers
17-695 Design Patterns
17-765 Autonomous Self-Adaptive Systems Using Reinforcement Learning