2025–2026 Bulletin Addendum MS in Computer Science
This Addendum entry reflects the following change to an existing degree program:
- Addition of AI & Machine Learning concentration
Effective date: September 1, 2025
The Master of Science (MS) in Computer Science program is for computer professionals and people who intend to move into the computer field from other areas of study.
This program requires an average of 12–20 months to complete. Students may begin the program in the fall, spring, or summer term.
Learning Outcomes
- Advanced knowledge of computer language theory, software design, as well as databases, networks, or security.
- Proficiency in algorithms, operating systems, computer language usage, software development, and the management of data, networks, or security.
- Competence sufficient to investigate continually emerging new software technologies, applications, and approaches.
- An understanding of, and ability to apply, the values and principles of professional ethics.
- Effective communication, teamwork, and leadership skills.
Admissions Information
For current admissions information, please visit the Metropolitan College website.
Prerequisites
Applicants to the program are required to have a bachelor’s degree from an accredited institution and academic experience with programming, discrete mathematics, and computer systems. If college-level credit is not in evidence, the Admissions Committee will determine which prerequisite courses will need to be taken. Students who complete the program’s prerequisites at Boston University can earn an undergraduate Certificate in Computer Science. Contact Admissions & Enrollment Services at 617-353-6000 or met@bu.edu for more information.
Degree Requirements
Students are required to finish the core curriculum and either a five-course concentration or five general electives. The minimum passing grade for a course in the graduate program is a C (2.0), but an average grade of B (3.0) must be maintained to be in good academic standing and be eligible to graduate.
A total of 10 courses (40 units) is required, as follows:
Core Curriculum (five courses/20 units)
- MET CS 535 Computer Networks
or MET CS 579 Database Management
- MET CS 566 Analysis of Algorithms
- MET CS 575 Operating Systems
- MET CS 662 Computer Language Theory
- MET CS 673 Software Engineering
Students who have completed courses on core curriculum subjects as part of their undergraduate degree program may request permission from the Department of Computer Science to replace the corresponding core courses with graduate-level computer science electives. Please refer to the MET CS Academic Policies Manual for further details.
General Electives (five courses/20 units)
Students who are not choosing a concentration must select five general electives (20 units) from the list below with at least three courses at the 600 level or above. When choosing electives, students should make sure that they have all prerequisites required by the selected course.
- MET CS 544 Foundations of Analytics and Data Visualization
- MET CS 550 Computational Mathematics for Machine Learning
- MET CS 555 Foundations of Machine Learning
- MET CS 561 Financial Analytics
- MET CS 570 Biomedical Sciences and Health IT
- MET CS 580 Health Informatics
- MET CS 581 Health Information Systems
- MET CS 599 Biometrics
- MET CS 601 Web Application Development
- MET CS 602 Server-Side Web Development
- MET CS 622 Advanced Programming Techniques
- MET CS 664 Artificial Intelligence
- MET CS 665 Software Design and Patterns
- MET CS 674 Database Security
- MET CS 677 Data Science with Python
- MET CS 683 Mobile Application Development with Android
- MET CS 684 Enterprise Cybersecurity Management
- MET CS 685 Network Design and Management
- MET CS 688 Web Mining and Graph Analytics
- MET CS 689 Designing and Implementing a Data Warehouse
- MET CS 690 Network and Cloud Security
- MET CS 693 Digital Forensics and Investigations
- MET CS 694 Mobile Forensics and Security
- MET CS 695 Cybersecurity
- MET CS 699 Data Mining
- MET CS 701 Rich Internet Application Development
- MET CS 763 Secure Software Development
- MET CS 766 Deep Reinforcement Learning
- MET CS 767 Advanced Machine Learning and Neural Networks
- MET CS 775 Advanced Networking
- MET CS 777 Big Data Analytics
- MET CS 779 Advanced Database Management
- MET CS 781 Advanced Health Informatics
- MET CS 787 AI and Cybersecurity
- MET CS 788 Generative AI
- MET CS 789 Cryptography
- MET CS 790 Computer Vision in AI
- MET CS 793 Special Topics in Computer Science
With advisor’s approval, students may choose to take courses outside of the general electives list.
Master’s Thesis Option in Computer Science (8 units)
Students majoring in computer science may elect a thesis option, to be completed within 12 months. This option is available to MS in Computer Science candidates who have completed at least seven courses toward their degree and have a grade point average (GPA) of 3.7 or higher. Students are responsible for finding a thesis advisor and a principal reader within the department. The advisor must be a full-time faculty member; the principal reader may be part-time faculty with a PhD (unless waived by department).
- MET CS 810/811 Master’s Thesis in Computer Science
Concentrations
AI & Machine Learning
The Concentration in AI & Machine Learning provides intensive exploration of the theory and practice of neural nets, generative AI, automated reasoning, AI security, intelligent image processing, and reinforcement learning. AI ethics as well as supervised and unsupervised learning are studied. This concentration enables graduates to design and implement intelligent applications in engineering, business, and industry.
Learning Outcomes
- Advanced Machine Learning and Deep Learning: Students will be able to solve complex problems such as computer vision, natural language processing, and speech recognition using machine learning algorithms, including supervised and unsupervised learning models, neural network architectures, and deep learning techniques.
- Artificial Intelligence Development: Students will be able to design and implement agents and algorithms for self-learning systems, leveraging AI models for data representation and prediction, implementing evolutionary and genetic algorithms for optimization, and developing software systems that incorporate AI models to enhance capabilities.
- Ethical AI and Communication: Students will be able to evaluate the ethical implications of AI systems, ensuring model fairness, accountability, and transparency, and effectively communicating technical AI concepts to nontechnical stakeholders.
Concentration Requirements
In addition to the Master of Science (MS) in Computer Science core curriculum (20 units), students pursuing a concentration in AI & Machine Learning must also take the following concentration requirements and electives:
(five courses/20 units)
- MET CS 664 Artificial Intelligence
- MET CS 677 Data Science with Python
- MET CS 767 Advanced Machine Learning and Neural Networks
Plus two courses selected from the following list:*
- MET CS 688 Web Mining and Graph Analytics
- MET CS 699 Data Mining
- MET CS 766 Deep Reinforcement Learning
- MET CS 777 Big Data Analytics
- MET CS 787 AI and Cybersecurity
- MET CS 788 Generative AI
- MET CS 790 Computer Vision in AI
* Selection must include at least one of the following: MET CS 766, MET CS 787, MET CS 788, MET CS 790.
Computer Networks
The Concentration in Computer Networks offers a broad foundation of information technology, along with an in-depth exploration of computer data communication and modern networking. The computer networks concentration provides a comprehensive examination of network design and implementation, network performance analysis and management, network security, and the latest networking technology. The program is designed to empower students with extensive hands-on experience in order to analyze, design, procure, manage, and implement cutting-edge computer networking solutions and technologies.
Learning Outcomes
- Demonstrate advanced knowledge of data communication networks and protocols. Identify issues involved in multiaccess media and devices as applied to wired and wireless networks. Identify key areas for performance analysis and debugging of networks, along with techniques for network management in small- to large-scale networks.
- Demonstrate proficiency in data communication protocols and networks, including such areas as: flow control, distributed synchronization, error detection and correction, and routing techniques, and where to implement them.
- Demonstrate competence sufficient to design, specify, and develop data transfer protocols for specific purposes. Design, specify, plan, and define networks of any size. Analyze, evaluate, and select networking devices applicable to the network area that they are being implemented in, whether LAN, MAN, WAN, or wireless.
Concentration Requirements
In addition to the Master of Science (MS) in Computer Science core curriculum (20 units), students pursuing a concentration in Computer Networks must also take the following concentration requirements and electives:
(five courses/20 units)
- MET CS 635 Network Media Technologies
- MET CS 685 Network Design and Management
- MET CS 690 Network and Cloud Security
- MET CS 775 Advanced Networking
- One course selected from the list of computer science general electives
Data Analytics
The Concentration in Data Analytics will explore the intricacies of data analytics and expose students to various topics and tools related to data processing, analysis, and visualization. Students will learn probability theory, statistical analysis methods and tools, generating relevant visual presentations of data, and concepts and techniques for data mining, text mining, and web mining. Individuals who complete this program will have a solid knowledge of concepts and techniques in data analytics as well as a solid exposure to the methods and tools for data mining and knowledge discovery in addition to the broad background in the theory of practice of computer science from the core courses.
Learning Outcomes
- Familiarity with applied probability and statistics and their relevance in day-to-day data analysis.
- The ability to explore the various data visualization techniques and their applications using real-world data sets.
- An understanding of web analytics and metrics; how to procure and process unstructured text; and hidden patterns.
- Skills in facilitating knowledge discovery using data-mining techniques over vast amounts of data.
Concentration Requirements
In addition to the Master of Science (MS) in Computer Science core curriculum (20 units), students pursuing a concentration in Data Analytics must also take the following concentration requirements and electives:
(five courses/20 units)
- MET CS 544 Foundations of Analytics and Data Visualization
- MET CS 555 Foundations of Machine Learning
- MET CS 688 Web Mining and Graph Analytics
- MET CS 699 Data Mining
- One course selected from the list of computer science general electives
Security
The Concentration in Security provides in-depth knowledge of emerging security threats and solutions to prepare technical leaders to identify, develop, and implement highly secure systems and networks that support organizational goals.
Learning Outcomes
- Demonstration of advanced knowledge of cybersecurity concepts, models, principles, and practices, and ability to apply the knowledge to identify and solve the cybersecurity problems.
- The ability to identify and explain social, legal, and ethical issues related to cybersecurity and privacy, and how they guide and apply cybersecurity design, planning, and decisions.
- Demonstration of advanced knowledge of crypto algorithms and their applications.
- Demonstration of advanced knowledge of network security and its applications.
- The ability to identify and develop technologies and tools to prevent, detect, react, and recover from attacks in various contexts using both offensive and defensive thinking.
- The ability to effectively communicate (verbally and in writing), work in teams, and provide leadership.
Concentration Requirements
In addition to the Master of Science (MS) in Computer Science core curriculum (20 units), students pursuing a concentration in Security must also take the following concentration requirements and electives:
(five courses/20 units)
- MET CS 690 Network and Cloud Security
- MET CS 695 Cybersecurity
- MET CS 789 Cryptography
- One course selected from the list below:
- MET CS 599 Biometrics
- MET CS 674 Database Security
- MET CS 684 Enterprise Cybersecurity Management
- MET CS 693 Digital Forensics and Investigations
- MET CS 694 Mobile Forensics and Security
- MET CS 763 Secure Software Development
- MET CS 787 AI and Cybersecurity
- MET CS 793 Special Topics in Computer Science
- One course selected from the list of computer science general electives
Declaration of More Than One Concentration
Students in the Master of Science in Computer Science program have the option to concentrate in more than one area. Each concentration must be finished before the student officially graduates from their program. No additional concentrations may be added after graduation. In the case of some courses overlapping between one or more concentrations, only one course may count toward both concentrations. If more than one course overlaps, the student must take an elective in its place so that each concentration is completed.
Second Master’s Degree Option
In appreciation of the converging needs of management and technology, the departments of Actuarial Science, Administrative Sciences, and Computer Science collaborate to offer a unique opportunity to students currently enrolled in their degree programs as well as alumni of those programs. Learn more.
