Ready to Excel in High-Growth Technology Roles?
The Master of Science in Computer Science (MSCS) at Boston University’s Metropolitan College (MET) is designed to help you compete for positions in high-growth technology areas. The MSCS provides the skills necessary for challenging jobs as software developers, information security analysts, network administrators, database developers, data analysts, IT project managers, and health informatics professionals.
The MSCS degree program offers optional concentrations in:
Program at a Glance
- On Campus
- Part-Time or Full-Time Study
- STEM Designated
- 40 Credits
- 12–20 Months to Completion
- 17 Core Faculty
- No GRE/GMAT
- Tuition & Fees Range—Part-Time Study*: $30,800–$34,200
*Based on 2024–2025 Boston University tuition and fees. Merit scholarship may reduce cost.
Develop In-Demand Computer Science Skills for Your Career
Amid growing demand for—and reliance upon—big data, cloud computing, machine learning information security, and networking, jobs in the computer science and information technology sector continue to grow at a faster rate than other occupations, with overall projected growth of 13 percent through 2030 and a median annual wage of more than $91K in 2020 (US Bureau of Labor Statistics Occupational Outlook Handbook). Because of the specialized nature of the work, competition for talent is fierce.
To help you to master emerging software technologies, applications, and approaches, BU MET’s master’s in Computer Science curriculum develops advanced understanding and command of software design, and ensures that you become proficient in programming, algorithms, operating systems, databases, software development, and the management of data, networks, or security.
A National Center of Academic Excellence
Boston University has been designated a Center of Academic Excellence (CAE) in Cyber Defense and Research by the National Security Agency and Department of Homeland Security. Our information security programs are certified by the Committee on National Security Systems (CNSS).
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Computer Science Career Outlook
Top computer science careers in data science, software development, and other popular areas of IT.
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“One of the main reasons I have enjoyed and have been so impressed with the MET CS program so far is the practicality of the classes. The professors have a good mix of both academic and industry experience, making it a very well-rounded program.”—Tara Adams (MET’19), Product Manager, Paytronix Systems, Inc.
Why Choose BU MET’s Computer Science Master’s Program
- Recognized & Certified: Boston University is recognized by the National Security Agency and the Department of Homeland Security as a Center of Academic Excellence (CAE) in Cyber Defense and Research. BU MET’s information security programs are certified by the Committee on National Security Systems (CNSS).
- Active Learning Environment: BU MET’s computer science courses ensure you get the attention you need, while introducing case studies and real-world projects that emphasize technical and theoretical knowledge—combining in-depth, practical experience with the critical skills needed to remain on the forefront of the information technology field. In addition, BU’s Center for Reliable Information Systems and Cyber Security (RISCS) offers opportunities to collaborate and participate in research on system reliability and information security.
- Career Counseling: MET’s Career Development office and BU’s Center for Career Development offer a variety of job-hunting resources, including one-on-one career counseling by appointment for both online and on-campus students.
- Engaged Faculty: In BU MET’s Computer Science master’s program, you benefit from working closely with highly qualified faculty and industry leaders in a wide range of technology fields who are committed to teaching the latest technologies within the framework of ideas, concepts, and methods that drive innovation.
- Extensive Network: Study computer science alongside peers with solid IT and business experience, learn from faculty who have valuable contacts across several sectors, and benefit from an alumni community with strong professional connections.
- STEM Designated: Eligible graduates on student visas have access to an Optional Practical Training (OPT) of 12 months and an extension for up to 24 additional months.
- Student Support: Enjoy an exceptional student-to-instructor ratio, ensuring close interaction with faculty mentors and access to support.
- Valuable Resources: Make use of Boston University’s extensive resources, including the Center for Career Development, Educational Resource Center, Fitness & Recreation Center, IT Help Centers, Mugar Memorial Library, Center for Antiracist Research, Howard Thurman Center for Common Ground, George Sherman Union, Rafik B. Hariri Institute for Computing and Computational Science & Engineering, and many others.
- Flexible Options: Study at the pace that works for you, evenings on campus with courses that begin fall, spring, and summer.
- Track Record: Learn from the best—BU MET’s Department of Computer Science was established in 1979 and is the longest-running computer science department at BU. Over its four decades, the department has played an important role in the emergence of IT at the University and throughout the region.
- Merit Scholarships: US citizens and permanent residents are automatically considered during the application process and nominated based on eligibility. Learn more.
Master the Tools to Excel in Computer Science
Offered through BU MET’s Department of Computer Science, the Master of Science in Computer Science (MSCS) is intended for computer professionals and for people who wish to move into the computer field from other areas of study. Our faculty consists of academic leaders who are engaged in innovative research and skilled industry experts with firsthand experience in data analytics, machine learning, AI, computer networks, cybersecurity, IT project management, and software development.
Computer Science Concentration Options
BU MET’s MS in Computer Science degree program offers concentrations in:
Graduate with Expertise
Metropolitan College’s Computer Science master’s degree will equip you with:
- Advanced knowledge of computer language theory and software design, as well as databases, networks, and 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.
- The ability to apply the principles of information security and assurance in a variety of contexts.
- Effective communication, teamwork, and leadership skills.
Certificate-to-Degree Pathway
BU MET graduate certificate programs can serve as building blocks to a master’s degree. Students currently enrolled in a graduate certificate who are interested in transitioning into a master’s degree should contact their academic advisor to declare their interest in this pathway. A new master’s degree application is not required. Connect with a graduate admissions advisor at csadmissions@bu.edu to learn more about this option.
The following graduate certificate programs have courses that may be applied to the master’s degree:
Master’s in Computer Science Curriculum
A total of 40 credits is required.
A 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 to be eligible to graduate.
Core Curriculum
(Five courses/20 credits)
MET CS 566 Analysis of Algorithms
Fall ‘24
Undergraduate Prerequisites: (CS341 or CS342 or CS526) or instructor's consent - earn basic methods for designing and analyzing efficient computer algorithms and practice hands-on programming skills. Topics include sorting, searching, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, string matching, and NP-completeness. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Chertushkin |
COM 215 |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Belyaev |
SOC B57 |
W |
6:00 pm – 8:45 pm |
A3 |
IND |
Belyaev |
HAR 302 |
R |
6:00 pm – 8:45 pm |
A5 |
IND |
Zhang |
KCB 106 |
M |
6:00 pm – 8:45 pm |
O1 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
MET CS 575 Operating Systems
Fall ‘24
Undergraduate Prerequisites: (METCS472) and (CS 231 or CS 232) or instructor's consent - Overview of operating system characteristics, design objectives, and structures. Topics include concurrent processes, coordination of asynchronous events, file systems, resource sharing, memory management, security, scheduling and deadlock problems. Prereq: MET CS472, and MET CS231 or MET CS232, or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Nourai |
CAS 222 |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Nourai |
CAS 222 |
R |
6:00 pm – 8:45 pm |
MET CS 662 Computer Language Theory
Fall ‘24
Undergraduate Prerequisites: (METCS566) or instructor's consent - Theory of finite automata and regular expressions and properties of regular sets. Context- free grammars, context-free languages, and pushdown automata. Turing machines, undecidability problems, and the Chomsky hierarchy. Introduction to computational complexity theory and the study of NP-complete problems. Prerequisite: MET CS 248 or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Naidjate |
CAS 218 |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Naidjate |
CAS 233 |
R |
6:00 pm – 8:45 pm |
MET CS 673 Software Engineering
Fall ‘24
HUB
Undergraduate Prerequisites: MET CS342 and at least one 500-level computer programming-intensive sc ience course (or instructor's consent). MET CS 564 or MET CS 565 are r ecommended. - Overview of techniques and tools to develop high quality software. Topics include software development life cycle such as Agile and DevOps, requirements analysis, software design, programming techniques, refactoring, testing, as well as software management issues. This course features a semester-long group project where students will design and develop a real world software system in groups using Agile methodology and various SE tools, including UML tools, project management tools, programming frameworks, unit and system testing tools , integration tools and version control tools.
Prereq: This is a capstone course to be taken after at least two programming intensive courses toward the end of a program of study. Familiarity with OO design concepts and proficiency in at least one high-level programming language is required. Or, Instructor's consent. Familiarity with web or mobile application development preferred.
Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Oral and/or Signed Communication, Teamwork/Collaboration. [ 4 cr. ]
BU Hub Learn More - Digital/Multimedia Expression
- Oral and/or Signed Communication
- Teamwork/Collaboration
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Eryilmaz |
KCB 107 |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Zhang |
MET 122 |
R |
12:30 pm – 3:15 pm |
O1 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
And one of the following*:
MET CS 535 Computer Networks
Fall ‘24
Undergraduate Prerequisites: (METCS575) ; Undergraduate Corequisites: Undergraduate students can not take any combination of courses from th e list: CS 425, CS 535, CS 625. Only one of these courses can be coun ted toward their requirements. - This course provides a robust understanding of networking. It teaches the fundamentals of networking systems, their architecture, function and operation and how those fundamentals are reflected in current network technologies. Students will learn the principles that underlie all networks and the application of those principles (or not) to current network protocols and systems. The course explains how layers of different scope are combined to create a network. There will be a basic introduction to Physical Media, the functions that make up protocols, such as error detection, delimiting, lost and duplicate detection; and the synchronization required for the feedback mechanisms: flow and retransmission control, etc. Students will be introduced to how these functions are used in current protocols, such as Ethernet, WiFi, VLANs, TCP/IP, wireless communication, routing, congestion management, QoS, network management, security, and the common network applications as well as some past applications with unique design solutions. Prereq: MET CS 575 and MET CS 201 or MET CS 231 or MET CS 232. Or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 625 or MET CS 425 (undergraduate). Only one of these courses can be counted towards degree requirements. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Day |
KCB 104 |
T |
12:30 pm – 3:15 pm |
O2 |
IND |
Day |
|
ARR |
12:00 am – 12:00 am |
MET CS 579 Database Management
Fall ‘24
Undergraduate Prerequisites: (METCS231 OR METCS232) or consent of instructor. ; Undergraduate Corequisites: Restrictions: This course may not be taken in conjunction with CS 669 or CS 469 (undergraduate). Only one of these courses can be counted to wards degree requirements. - This course provides a theoretical yet modern presentation of database topics ranging from Data and Object Modeling, relational algebra and normalization to advanced topics such as how to develop Web-based database applications. Other topics covered - relational data model, SQL and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; object- oriented database systems. Prereq: MET CS 231 or MET CS 232; or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 669. Refer to your Department for further details. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Lee |
BRB 122 |
T |
6:00 pm – 8:45 pm |
A3 |
IND |
Lee |
CGS 323 |
M |
6:00 pm – 8:45 pm |
Students who have completed courses on core curriculum subjects as part of their undergraduate degree program or have relevant work-related experience 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 credits)
Students who are not choosing a concentration must select five General Electives. At least three courses must be 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 532 Computer Graphics
Graduate Prerequisites: MET CS 248 and MET CS 341 or MET CS 342 or consent of the instructor - This course is primarily the study of design of graphic algorithms. At the end of the course you can expect to be able to write programs to model, transform and display 3- dimensional objects on a 2-dimensional display. The course starts with a brief survey of graphics devices and graphics software. 2-d primitives such as lines and curves in 2- d space are studied and a number of algorithms to draw them on a rectangular surface are introduced, followed by a study of polygons, scan conversion and other fill methods. Attributes of the primitives are studied as well as filtering and aliasing. Geometric transformations in 2 dimensions are introduced in homogeneous coordinates, followed by the viewing pipeline, which includes clipping of lines, polygons and text. Hierarchical graphics modeling is briefly studied. The graphics user interface is introduced and various input functions and interaction modes are examined. 3-d graphics is introduced through object representations through polygonal methods, spline techniques, and octrees. This is followed by 3-d transformations and the 3-d viewing pipeline. The course ends with a study of algorithms to detect the visible surfaces of a 3-d object in both the object space and the image space. Laboratory Course. Prereq: MET CS 248 and MET CS 341 or MET CS 342. Or instructor's consent. [ 4 cr. ]
MET CS 544 Foundations of Analytics and Data Visualization
Fall ‘24
Formerly titled CS 544 Foundations of Analytics with R.
The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: MET CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Kalathur |
MCS B31 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Diwania |
CAS 208 |
T |
6:00 pm – 8:45 pm |
A3 |
IND |
Pan |
CAS 306 |
W |
6:00 pm – 8:45 pm |
A4 |
IND |
Pan |
CAS 213 |
R |
6:00 pm – 8:45 pm |
O1 |
IND |
Kalathur |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Kalathur |
|
ARR |
12:00 am – 12:00 am |
MET CS 550 Computational Mathematics for Machine Learning
Fall ‘24
Undergraduate Prerequisites: Basic knowledge of Python or R; or consent of instructor. - Mathematics is fundamental to data science and machine learning. In this course, you will review essential mathematical concepts and fundamental procedures illustrated by Python and/or R code and visualizations. Computational methods for data science presented through accessible, self-contained examples, intuitive explanations, and visualization will be discussed. Equal emphasis will be placed on both mathematics and computational methods that are at the heart of many algorithms for data analysis and machine learning. You will also advance your mathematical proficiency enabling you to effectively apply your skills to data analytics and machine learning. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Pinsky |
CDS 263 |
T |
6:00 pm – 8:45 pm |
MET CS 555 Foundations of Machine Learning
Fall ‘24
Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
|
PHO 201 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Alizadeh-Shabdiz |
PSY B51 |
R |
6:00 pm – 8:45 pm |
A3 |
IND |
Alizadeh-Shabdiz |
MET 101 |
R |
9:00 am – 11:45 am |
O2 |
IND |
Alizadeh-Shabdiz |
|
ARR |
12:00 am – 12:00 am |
MET CS 561 Financial Analytics
This course presents financial algorithms used in applications of computer science in financial decision analysis, risk management, data mining and market analysis, and other modern business processes. The course covers theoretical background on probabilistic methods used for financial decision making and their application in number of fields such as financial modeling, venture capital decision making, operational risk measurement and investment science. Number of financial applications and algorithms are being presented for portfolio risk analysis, modeling real options, venture capital decision making, etc. The course concludes with algorithms for financial risk assessment and presents the security concepts and challenges of financial information systems. [ 4 cr. ]
MET CS 570 Biomedical Sciences and Health IT
Fall ‘24
This course is designed for IT professionals, and those training to be IT professionals, who are preparing for careers in healthcare-related IT (Health Informatics). This course provides a high-level introduction into basic concepts of biomedicine and familiarizes students with the structure and organization of American healthcare system and the roles played by IT in that system. The course introduces medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes. IT case studies demonstrate the key roles of health informatics and how IT tools and resources help medical professionals integrate multiple sources of information to make diagnostic and therapeutic decisions. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
O1 |
IND |
Keskin |
|
ARR |
12:00 am – 12:00 am |
MET CS 580 Health Informatics
Fall ‘24
Undergraduate Prerequisites: (METCS570) - This course presents the fundamental principles, concepts, and technological elements that make up the building blocks of Health Informatics. It introduces the characteristics of data, information, and knowledge in the domain, the common algorithms for health applications, and IT components in representative clinical processes. It presents the conceptual framework for handling biomedical data collection, storage, and optimal use. It covers the concepts of population health and precision medicine and the information systems that support them. It introduces basic principles of knowledge management systems in biomedicine, various aspects of Health Information Technology standards, and IT aspects of clinical process modeling. Students design a simple Health Informatics solution as a term project. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
O2 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
MET CS 581 Health Information Systems
Fall ‘24
Health Information Systems are comprehensive application systems that automate the activities of healthcare delivery including clinical care using electronic health records (EHRs), coordination of care across providers, telehealth, management of the business of healthcare such as revenue cycle management, and population health management. The course covers the functionality of these systems, the underlying information technology they require and their successful operations. It addresses challenges in this rapidly changing field such as complex data, security, interoperability, mobile technology and distributed users. The course emphasizes applied use of health information systems through case studies, current articles, and exercises. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Levinger |
CAS 213 |
M |
6:00 pm – 8:45 pm |
E1 |
IND |
Levinger |
CAS 213 |
M |
6:00 pm – 8:45 pm |
MET CS 599 Biometrics
In this course we will study the fundamental and design applications of various biometric systems based on fingerprints, voice, face, hand geometry, palm print, iris, retina, and other modalities. Multimodal biometric systems that use two or more of the above characteristics will be discussed. Biometric system performance and issues related to the security and privacy aspects of these systems will also be addressed. [ 4 cr. ]
MET CS 601 Web Application Development
Fall ‘24
Prerequisites: WAD 100 - Learn essential front-end development skills, starting with foundational JavaScript techniques, such as DOM manipulation and event handling, and advancing to interactive web technologies like HTML's Drag and Drop, Canvas, and SVG. You will be exposed to asynchronous operations, including AJAX, the Fetch API, and Web Workers, and learn to craft responsive designs using Flexbox, CSS Grid, and advanced CSS selectors. A comprehensive exploration of TypeScript and its main feature, static typing, and capabilities will also be covered. The course concludes with a comprehensive dive into ReactJS, covering its core architectural concepts, component-based structure, and state management techniques [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Sheehan |
CAS 116 |
R |
6:00 pm – 8:45 pm |
O2 |
IND |
Hur |
|
ARR |
12:00 am – 12:00 am |
MET CS 602 Server-Side Web Development
Fall ‘24
The Server-Side Web Development course concentrates primarily on building web applications using PHP/MySQL and Node.js/MongoDB. The course is divided into various modules covering in depth the following topics: PHP, MySQL, Object oriented PHP, PHP MVC, Secure Web applications, Node.js and MongoDB. Along with the fundamentals underlying these technologies, several applications will be showcased as case studies. Students work with these technologies starting with simple applications and then examining real world complex applications. At the end of this course, students would have mastered the web application development on the server-side. Prerequisite: MET CS 601. Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Sheehan |
EPC 206 |
T |
6:00 pm – 8:45 pm |
MET CS 622 Advanced Programming Techniques
Fall ‘24
HUB
Polymorphism, containers, libraries, method specifications, large-scale code management, use of exceptions, concurrent programming, functional programming, programming tests. Java will be used to illustrate these concepts. Students will implement a project or projects of their own choosing, in Java, since some concepts are expressible only in Java. Prerequisite: MET CS 342 or equivalent knowledge of Java. Or MET CS 521 and MET CS 526. Or instructor's consent. Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. [ 4 cr. ]
BU Hub Learn More - Creativity/Innovation
- Critical Thinking
- Quantitative Reasoning II
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Rawassizadeh |
KCB 107 |
W |
6:00 pm – 8:45 pm |
MET CS 664 Artificial Intelligence
Fall ‘24
Graduate Prerequisites: MET CS 248 and MET CS 341 or MET CS 342. - Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagations, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Laboratory course. Prereq: MET CS 341, MET CS 342, MET CS 520 or MET CS 521. Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Kalathur |
CAS 225 |
W |
6:00 pm – 8:45 pm |
O1 |
IND |
Braude |
|
ARR |
12:00 am – 12:00 am |
MET CS 665 Software Design and Patterns
Fall ‘24
Graduate Prerequisites: (METCS341 or METCS342 and METCS565) or consent of the instructor - Software design principles, the object-oriented paradigm, unified modeling language; creational, structural, and behavioral design patterns; OO analysis and design; implementation of semester project. Laboratory course. Prereq: (MET CS 526 or MET CS 622) and one of the following (MET CS 341, MET CS 342, MET CS 520, or MET CS 521). Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Orsini |
CAS 324 |
T |
6:00 pm – 8:45 pm |
MET CS 674 Database Security
Fall ‘24
Graduate Prerequisites: CS 579 or CS 669 or consent of the instructor - The course provides a strong foundation in database security and auditing. This course utilizes Oracle scenarios and step-by-step examples. The following topics are covered: security, profiles, password policies, privileges and roles, Virtual Private Databases, and auditing. The course also covers advanced topics such as SQL injection, database management security issues such as securing the DBMS, enforcing access controls, and related issues. Prereq: MET CS 579 or MET CS 669; or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
O1 |
IND |
Carroll |
|
ARR |
12:00 am – 12:00 am |
MET CS 677 Data Science with Python
Fall ‘24
Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Pinsky |
PSY B51 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Enxing |
CAS 214 |
T |
6:00 pm – 8:45 pm |
A3 |
IND |
Pinsky |
CDS 264 |
W |
6:00 pm – 8:45 pm |
A4 |
IND |
Mohan |
KCB 102 |
T |
6:00 pm – 8:45 pm |
O2 |
IND |
Liu |
|
ARR |
12:00 am – 12:00 am |
MET CS 683 Mobile Application Development with Android
Fall ‘24
Graduate Prerequisites: (METCS342) or instructor's consent. - This course discusses the principles and issues associated with mobile application development using Android as the development platform. Topics covered will include Android application components (Activities, Services, Content Providers and Broadcast Receivers), ICC (Inter-component Communication), UI design, data storage, asynchronous processing, 2D graphics, and Android security. Students will develop their own apps in Java and/or Kotlin using Android Studio in their semester-long projects. Prior knowledge of Java programming is required. Prerequisite: MET CS 342 OR MET CS 520 OR MET CS 521. Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
|
PHO 205 |
R |
6:00 pm – 8:45 pm |
E1 |
IND |
|
PHO 205 |
R |
6:00 pm – 8:45 pm |
MET CS 684 Enterprise Cybersecurity Management
Fall ‘24
This course covers important topics that students need to understand in order to effectively manage a successful cybersecurity and privacy program, including governance, risk management, asset classification and incidence response. Students are first introduced to cybersecurity & privacy policy frameworks, governance, standards, and strategy. Risk tolerance is critical when building a cybersecurity and privacy program that supports business goals and strategies. Risk management fundamentals and assessment processes will be reviewed in depth including the methodology for identifying, quantifying, mitigating and controlling risks. Asset classification and the importance of protecting Intellectual Property (IP) will prepare students to understand and identify protection mechanisms needed to defend against malicious actors, including industry competitors and nation states. Incident Response programs will cover preparation and responses necessary to triage incidents and respond quickly to limit damage from malicious actors. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Campbell |
KCB 102 |
W |
6:00 pm – 8:45 pm |
O2 |
IND |
Pak |
|
ARR |
12:00 am – 12:00 am |
MET CS 685 Network Design and Management
Fall ‘24
Undergraduate Prerequisites: (METCS535 OR METCS625) or instructor's consent - . This course will cover contemporary integrated network management based on FCAPS (Fault, Configuration, Administration, Performance, and Security management) model. The introduction to the course will be an overview of data transmission techniques and networking technologies. The middle part of the course will be on Network Management Model, SNMP versions 1, 2 and 3, and MIBs. In the second part of the course, particular focus and emphasis will be given to current network management issues: various wireless networks technologies (WLAN, WiFi, WiMax), Voice-over-IP, Peer-to-Peer Networks, networking services, Identity Management, and Services Oriented Architecture Management. Prereq: MET CS 535 or MET CS 625. or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Arena |
MCS B29 |
R |
6:00 pm – 8:45 pm |
MET CS 688 Web Mining and Graph Analytics
Fall ‘24
Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms, link analysis and community detection based on graphs. Laboratory Course. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
|
CAS 201 |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Ghaedi |
MCS B31 |
R |
6:00 pm – 8:45 pm |
A3 |
IND |
Vasilkoski |
CAS 116 |
T |
6:00 pm – 8:45 pm |
O1 |
IND |
Joner |
|
ARR |
12:00 am – 12:00 am |
MET CS 689 Designing and Implementing a Data Warehouse
Fall ‘24
Graduate Prerequisites: CS 579 or CS 669 or consent of the instructor - This course surveys state-of-the art technologies in DW and Big Data. It describes logical, physical and semantic foundation of modern DW infrastructure. Students will create a cube using OLAP and implement decision support benchmarks on Hadoop/Spark vs Vertica database. Upon successful completion, students will be familiar with tradeoffs in DW design and architecture. Prereq: MET CS 579 or MET CS 669 and either MET CS 520 or MET CS 521. Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Polnar |
COM 215 |
M |
6:00 pm – 8:45 pm |
MET CS 690 Network Security
Fall ‘24
Undergraduate Prerequisites: (METCS535 OR METCS625) or instructor's consent. - This course will cover advanced network security issues and solutions. The main focus on the first part of the course will be on Security basics, i.e. security services, access controls, vulnerabilities, threats and risk, network architectures and attacks. In the second part of the course, particular focus and emphasis will be given to network security capabilities and mechanisms (Access Control on wire-line and wireless networks), IPsec, Firewalls, Deep Packet Inspection and Transport security. The final portion of the course will address Network Application security (Email, Ad-hoc, XML/SAML and Services Oriented Architecture security. As part of our course review we will explore a number of Network Use Cases. Prereq: MET CS 535 or MET CS 625; Familiarity with OSI and TCP/IP protocol stack; Background-familiarity with binary numbers, prime numbers, binary- hexadecimal-decimal conversions, etc; Familiarity with computer programming concepts; or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A2 |
IND |
Zhang |
CAS 226 |
M |
6:00 pm – 8:45 pm |
E2 |
IND |
Zhang |
CAS 226 |
M |
6:00 pm – 8:45 pm |
MET CS 693 Digital Forensics and Investigations
Fall ‘24
Provides a comprehensive understanding of digital forensics and investigation tools and techniques. Learn what computer forensics and investigation is as a profession and gain an understanding of the overall investigative process. Operating system architectures and disk structures are discussed. Studies how to set up an investigator's office and laboratory, as well as what computer forensic hardware and software tools are available. Other topics covered include importance of digital evidence controls and how to process crime and incident scenes, details of data acquisition, computer forensic analysis, e-mail investigations, image file recovery, investigative report writing, and expert witness requirements. Provides a range of laboratory and hands-on assignments either in solo or in teams. With rapid growth of computer systems and digital data this area has grown in importance. Prereq: Working knowledge of windows computers, including installing and removing software. Access to a PC meeting the minimum system requirements defined in the course syllabus. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
E1 |
IND |
Arena |
EPC 206 |
S |
9:00 am – 12:00 pm |
O1 |
IND |
Navarro |
|
ARR |
12:00 am – 12:00 am |
MET CS 694 Mobile Forensics and Security
Overview of mobile forensics investigation techniques and tools. Topics include mobile forensics procedures and principles, related legal issues, mobile platform internals, bypassing passcode, rooting or jailbreaking process, logical and physical acquisition, data recovery and analysis, and reporting. Provides in-depth coverage of both iOS and Android platforms. Laboratory and hands-on exercises using current tools are provided and required. [ 4 cr. ]
MET CS 695 Cybersecurity
Fall ‘24
Undergraduate Prerequisites: (METCS625) or instructor's consent - This course introduces fundamental concepts, principles of cybersecurity and their use in the development of security mechanisms and policies. Topics include basic risk assessment and management; basic legal and ethics issues, various cyber attacks, defense methods and tools; security principles, models and components; different crypto protocols, techniques and tools, including symmetric and asymmetric encryption algorithms, hashing, public key infrastructure, and how they can be used; security threats and defense to hardware, operating systems, networks and applications in modern computing environments. Hands-on labs using current tools are provided and required. Prerequisite: METCS535 or METCS625 or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Zhang |
CGS 121 |
T |
6:00 pm – 8:45 pm |
E1 |
IND |
Zhang |
CGS 121 |
T |
6:00 pm – 8:45 pm |
MET CS 699 Data Mining
Fall ‘24
Prerequisites: MET CS 521 & MET CS 546; MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. Students learn underlying theories of data mining algorithms in the class and they practice those algorithms through assignments and a semester-long class project using R. After finishing this course, students will be able to independently perform data mining tasks to solve real-world problems. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A2 |
IND |
Lee |
HAR 322 |
R |
6:00 pm – 8:45 pm |
A3 |
IND |
Lee |
MET 101 |
W |
2:30 pm – 5:15 pm |
O2 |
IND |
Joner |
|
ARR |
12:00 am – 12:00 am |
MET CS 701 Rich Internet Application Development
Fall ‘24
Undergraduate Prerequisites: MET CS 520 or MET CS 601 and programming experience, or instructor's c onsent - The Rich Internet Application (RIA) Development course concentrates primarily on building rich client web applications in the browser for desktop and mobile devices. The course is divided into various modules covering in depth the following technologies: HTML5, AngularJS, and Ionic framework. Along with the fundamentals underlying these technologies, several applications will be showcased as case studies. Students work with these technologies starting with simple applications and then examining real world complex applications. At the end of this course, students would have mastered the latest and widely used RIA methodologies. Course Prerequisites: METCS520 (Information Structures) and METCS601 (Web Application Development), or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
O1 |
IND |
Winderbaum |
|
ARR |
12:00 am – 12:00 am |
MET CS 763 Secure Software Development
Fall ‘24
Graduate Prerequisites: MET TC 250 or MET CS 248 or MET OM 501. - Overview of techniques and tools to develop secure software. Focus on the application security. Topics include secure software development processes, threat modeling, secure requirements and architectures, vulnerability and malware analysis using static code analysis and dynamic analysis tools, vulnerabilities in C/C++ and Java programs, Crypto and secure APIs, vulnerabilities in web applications and mobile applications, and security testing. Hands-on lab and programming exercises using current tools are provided and required. Prerequisite: At least two 500- level (or above) programming-intensive computer science courses; or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
O2 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
MET CS 766 Deep Reinforcement Learning
Perquisites: MET CS 677 or instructor’s consent. - This course focuses on reinforcement learning, covering fundamental concepts and advanced techniques. It begins with an introduction to reinforcement learning and key concepts, such as exploitation versus exploration and Markov Decision Processes. As the course progresses, it delves into state transition diagrams, the Bellman equation, and solutions to the Multi-Armed Bandits problem. Students will explore challenges and methods related to control and prediction. Then, they learn tabular methods, including Monte Carlo, Dynamic Programming, Temporal Difference Learning, SARSA, and Q-Learning. Afterwards, the course also extends into reviewing neural network concepts, covering convolutional and recurrent neural networks, and moves on to approximation methods for both discrete and continuous spaces, including DQN and its variants. Policy gradient methods, actor-critic methods. Finally, ethical considerations in AI and safety issues are also discussed. [ 4 cr. ]
MET CS 767 Advanced Machine Learning and Neural Networks
Fall ‘24
Graduate Prerequisites: MET CS 521; MET CS 622, MET CS 673 or MET CS 682; MET CS 677 strongly recommended; or consent of instructor. - Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN’s, Neural Nets and Deep Learning, Recurrent Neural Nets, Rule-learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptrons, backpropagation, attention, and transformers. Each student focuses on two of these approaches and creates a term project. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Djordjevic |
CAS 324 |
R |
6:00 pm – 8:45 pm |
A2 |
IND |
Rawassizadeh |
MET 101 |
T |
9:00 am – 11:45 am |
O2 |
IND |
Braude |
|
ARR |
12:00 am – 12:00 am |
MET CS 775 Advanced Networking
Graduate Prerequisites: (METCS535) or consent of the instructor - This seminar course provides a strong foundation in networking and Internet architecture, data transfer protocols, including TCP, SCTP, QUIC, and IPv6, and a deep look at network resource allocation with an emphasis on protocol- independent hardware for Deep Packet Inspection (DPI) and congestion management. The course goes into greater depth of current topics such as: naming and addressing, synchronization, congestion management and resource allocation (routing) and how they manifest in different environments. There will be assigned readings from the professor that require considerable class participation, both in presenting material and discussing it.
Prereq: MET CS 535 OR MET CS 625,or instructor's consent required. [ 4 cr. ]
MET CS 777 Big Data Analytics
Fall ‘24
This course is an introduction to large-scale data analytics. Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This class will focus both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, we will cover the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce, Apache Spark and Flink. Students will implement data mining algorithms and execute them on real cloud systems like Amazon AWS, Google Cloud or Microsoft Azure by using educational accounts. On the data mining models side, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side.
Prerequisite: MET CS 521, MET CS 544 and MET CS 555. Or, MET CS 677. Or, Instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Alizadeh-Shabdiz |
PSY B51 |
W |
6:00 pm – 8:45 pm |
O1 |
IND |
Trajanov |
|
ARR |
12:00 am – 12:00 am |
MET CS 779 Advanced Database Management
Fall ‘24
Graduate Prerequisites: (METCS579 OR METCS669) or consent of the instructor - This course covers advanced aspects of database management including normalization and denormalization, query optimization, distributed databases, data warehousing, and big data. There is extensive coverage and hands on work with SQL, and database instance tuning. Course covers various modern database architectures including relational, key value, object relational and document store models as well as various approaches to scale out, integrate and implement database systems through replication and cloud based instances. Students learn about unstructured "big data" architectures and databases, and gain hands-on experience with Spark and MongoDB. Students complete a term project exploring an advanced database technology of their choice. Prereq: MET CS 579 or MET CS 669; or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Polnar |
FLR 121 |
R |
6:00 pm – 8:45 pm |
MET CS 781 Advanced Health Informatics
Fall ‘24
Undergraduate Prerequisites: (METCS570) - This course presents the details of information processing in hospitals, hospital information systems (HIS), and more broadly health information systems. It presents the architecture, design, and user requirements of information systems in health care environment. It focuses on Information Technology aspects of Health Informatics specifically addressing the design, development, operation, and management of HIS. The first part of this course covers the introductory concepts including information processing needs, and information management in health care environment. The second part covers detailed description of HIS including hospital process modeling, architecture, quality assessment, and applicable tools. The final part of the course covers management of HIS and related issues and extension of this topic to other health care organizations. The course will have a term project providing students a hands-on experience in design and research of HIS. Prereq: MET CS 580; or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
D'Amore |
SAR 104 |
W |
6:00 pm – 8:45 pm |
E1 |
IND |
D'Amore |
SAR 104 |
W |
6:00 pm – 8:45 pm |
MET CS 783 Enterprise Architecture
Fall ‘24
Graduate Prerequisites: (METCS682) or strategic IT experience or instructor's consent - This course builds upon the strong technical foundation of our MSCIS and MSCS curricula, by providing students with the CIO-level management perspective and skills of an enterprise architect, in the context of the technologies that implement those architectures. Current technologies and processes explored in the enterprise architecture context include blockchain, microservices, multimodal/analytic databases, DevOps, SAFe (Scaled Agile Framework), containers/Docker, and some leverage of AI techniques. We cover both the migration of legacy enterprise systems and de novo enterprise architecture development, vendor selection and management, cybersecurity in the enterprise, and complex system integration. Enterprise architecture decisions are presented in the context of the business goals and alignment that are critical for success, given globalization and the reality that "all companies are now technology companies." The course content is rich with case studies that illustrate practical application of enterprise architecture approaches and lessons learned. The course also includes a number of realistic enterprise architecture assignments and an incremental term project with components spanning the course, to provide students with hands on enterprise architecture experience. Students develop the understanding and skills needed to define and implement successful enterprise architectures that provide real strategic and concrete value to organizations, such as substantially reducing IT costs while improving performance, agility and alignment of information technology to business goals. On-campus classrooms follow a "flipped classroom" format, where significant class time is devoted to in-class group workshops. Prereq: MET CS 682. Or strategic IT experience. Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Yates |
STH 113 |
M |
6:00 pm – 8:45 pm |
MET CS 787 Adversarial Machine Learning
Prerequisites: MET CS 767 or knowledge of Neural Networks or instructor’s consent. - This course is designed to provide students with a comprehensive understanding of the inherent vulnerabilities/security issues associated with integrating machine learning into various applications, as well as the knowledge/skills to defeat those vulnerabilities. Topics include an overview of categories of attacks against machine learning models and a detailed exploration of adversarial attacks, data poisoning attacks, membership inference attacks, model stealing attacks, as well as various defense solutions against the above-mentioned attacks. Upon the completion of the course, students are expected to know the threats and vulnerabilities that machine learning models face, along with the strategies and tools used to mitigate those risks. Hands-on labs based on existing tools are provided and required. [ 4 cr. ]
MET CS 788 Generative AI
Prerequisites: MET CS 677, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or instructor’s consent. - The first part of the course covers statistical concepts required for generative artificial intelligence. We review regressions and optimization methods as well as traditional neural network architectures, including perceptron and multilayer perceptron. Next, we move to Convolutional Neural Networks and Recurrent Neural Networks and close this part with Attention and Transformers. The second part of the course focuses on generative neural networks. We start with traditional self-supervised learning algorithms (Self Organized Map and Restricted Boltzmann Machine), then explore Auto Encoder architectures and Generative Adversarial Networks and move toward architectures that construct generative models, including recent advances in NLP, including LLMs, and Retrieval Augmented Methods. Finally, we describe the Neural Radiance Field, 3D Gaussian Splatting, and text-2-image models. Prereq: MET CS 677, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or instructor’s consent. [ 4 cr. ]
MET CS 789 Cryptography
Fall ‘24
Graduate Prerequisites: (METCS248 & METCS566) or consent of the instructor - The course covers the main concepts and principles of cryptography with the main emphasis put on public key cryptography. It begins with the review of integers and a thorough coverage of the fundamentals of finite group theory followed by the RSA and ElGamal ciphers. Primitive roots in cyclic groups and the discrete log problem are discussed. Baby-step Giant-step and the Index Calculus probabilistic algorithms to compute discrete logs in cyclic groups are presented. Naor -- Reingold and Blum -- Blum -- Shub Random Number Generators as well as Fermat, Euler and Miller-Rabin primality tests are thoroughly covered. Pollard's Rho, Pollard's and Quadratic Sieve factorization algorithms are presented. The course ends with the coverage of some oblivious transfer protocols and zero-knowledge proofs. There are numerous programming assignments in the course. Prereq: MET CS 248, or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Pascoe |
CAS B06A |
M |
6:00 pm – 8:45 pm |
MET CS 790 Computer Vision in AI
Prerequisites: MET CS 566 or instructor’s consent. - Students enrolled in this course will gain comprehensive insights into fundamental and advanced concepts within the dynamic realm of computer vision. The curriculum will focus on cutting-edge applications of deep neural networks in computer vision. Through hands-on experiences and practical exercises, students will learn to leverage computer vision and machine learning techniques to solve real-world challenges. This course not only equips students with theoretical knowledge but empowers them to apply these concepts effectively, fostering a deep understanding of how computer visio can be harnessed to address complex problems in diverse industries [ 4 cr. ]
MET CS 793 Special Topics in Computer Science
Fall ‘24
Fall 2023 Topic: Generative AI
This course focuses on recent advances in generative AI. It starts by reviewing statistics and regression models related to generative models, then common deep learning methods described. Later, models for designing new content, such as images, music, or text, will be explored, including GAN, VAE, Autoregressive and Diffusion Models. MLP, CNN, RNN, and Transformer models covered in CS 767 are reviewed. Students should be fluent in Python programming and CS 555 and CS 677 [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Rawassizadeh |
CAS 201 |
R |
6:00 pm – 8:45 pm |
MET CS 799 Advanced Cryptography
Undergraduate Prerequisites: (METCS789) or instructor's consent - This course builds on the material covered in CS 789 Cryptography. It begins with the coverage of commutative rings, finite fields, rings of polynomials, and finding of the greatest common divisor in the ring of polynomials. Irreducible polynomials are discussed. Field extensions and fields Fᴩ [x]/P are thoroughly covered. The main emphasis is put on elliptic curves over Fᴩ and F₂ and the ElGamal cipher on elliptic curves is presented. Block ciphers DES and double and triple DES are introduced. AES and WHIRLPOOL block ciphers and modes of operation are covered. The course continues with the introduction of message integrity and message authentication. In the last part of the course cryptographic hash functions SHA-512 and WHIRLPOOL as well as various digital signatures are introduced. Finally, entity authentication and key management issues are discussed. Prereq: MET CS 789; or instructor's consent. [ 4 cr. ]
Thesis Option
(8 credits)
The thesis option is to be completed within 12 months. This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a 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).
Computer Science Faculty
Tuition & Financial Assistance
Competitive Tuition
Our part-time rates are substantially lower than those of the traditional, full-time residential programs yet provide access to the same high-quality BU education.
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How Much Does This Program Cost?
BU MET Programs offer the flexibility of part-time or full-time study. Tuition, fees, and total program cost are determined by enrollment status. If you enroll in 1–2 courses (4–8 credits) in a semester, you are charged the part-time per-credit rate. If you enroll in 3–4 courses (12–16 credits) in a semester, you are charged the full-time semester rate.
MS in Computer Science (On Campus)
Enrollment Status |
Part Time |
Full Time |
Courses per Semester |
2 courses (8 credits) |
4 courses (16 credits) |
3 courses (12 credits) |
Time to Degree |
5 semesters (20 months) |
3 semesters (12-16 months)*** |
4 semesters (16-20 months)*** |
Tuition* |
$550-$975 per credit** |
$33,335 per semester |
$33,335 per semester |
Fees per Semester* |
$60 |
$478 |
$478 |
Total Degree Cost* |
$30,800– $34,200 |
$75,486 |
$105,399 |
*Based on 2024–2025 Boston University tuition & fee rates.
**Cost per credit is determined by course number (100–599 = $550/credit, 600–999 = $975/credit).
***Summer semester enrollment is not required for international students to maintain F-1 visa status. Enrollment in summer semester coursework will expedite completion of program and reduce total program cost.
International students seeking an F-1 visa for on-campus study must enroll full time and demonstrate availability of funds to cover the Estimated Cost of Graduate Study; those who wish to study online may enroll part-time but are not eligible for a visa. Learn more about International Student Tuition & Fees.
Questions? Please contact us to hear from an Admissions Advisor who can help you determine the best enrollment pathway. For information regarding financial aid, visit BU MET’s Financial Aid page.
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Please visit the BU MET admissions page for details on how to apply, financial assistance, tuition and fees, requirements for international students, and more.
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