Skip to main content
Home / Online Master’s in Computer Science (MSCS)

BECOME A SOFTWARE DEVELOPMENT LEADER

ONLINE MASTER’S IN COMPUTER SCIENCE (MSCS)

TELL US ABOUT YOURSELF.

Let us help you find your program fit. Please note, a bachelor degree is required for this program.

YOU’RE ALMOST DONE!

By submitting this form, I agree to be contacted via email, phone, or text to learn more about the programs at Stevens Institute of Technology. Since this program is 100% online, Stevens Institute of Technology does not offer US visas to attend.

ONLINE MASTER’S IN COMPUTER SCIENCE PROGRAM

99% of Stevens MSCS graduates in the Class of 2023 accepted job offers within three months of graduating1

You don’t need a computer science background to earn a Stevens MSCS

Pivot to a computer science career with a Stevens online master’s in computer science

Gain technical depth with in-demand focus areas in AI and machine learning, business intelligence and analytics, or software development

1Based on data from 2023 full-time program graduates.

Apply Now

Ranked No. 1 in New Jersey for “Best Online Master’s in Computer Information Technology Programs” in 2024 by U.S. News & World Report.
REQUEST INFORMATION

ONLINE MASTER’S IN COMPUTER SCIENCE OVERVIEW

The Stevens Institute of Technology Master’s of Science in Computer Science (MSCS) offers a curriculum aligned with high-demand areas such as software development, web programming, mobile systems and applications, cloud computing, human-computer interaction and enterprise software design. Computer science students take courses that foster technical proficiency in the leading industry tools, including:

Enterprise software design and engineering

Mobile application development and cloud computing

Agile development methods

Algorithm design and testing

Machine learning in support of providing software development leaders and high-quality coders

QUICK FACTS

TERM START DATE

FALL 2024: September 9, 2024

OVERVIEW

  • 30 Credit Hours
  • 10 Courses
  • 100% Online
  • 2 Years or Less Completion Time*

*Total time to complete the program may vary based on the number of credits taken each semester.

99%

EMPLOYMENT

99% of MSCS graduates in the Class of 2023 accepted job offers within three months of graduating.1

No. 1

IN N.J.

No. 1 in New Jersey for Best Online Master’s in Computer Information Technology Programs by U.S. News & World Report (2024).

7x

WINNER

Winner of the U.S. Distance Learning Association’s 21st Century Award for Best Practices in Distance Learning.

No. 13

IN THE NATION

Stevens was named one of the Top 20 U.S. Private Schools for Best Career Placement by The Princeton Review (2022).

No. 15

FOR BEST VALUE

Ranked No. 15 among “Best Value Colleges” by Payscale (2024).2

1 Based on data from 2023 full-time program graduates.
2 Based on the cost of a four-year bachelor’s degree program.

COMPUTER SCIENCE CAREER OUTLOOK

An MSCS qualifies students for advanced, well-compensated positions as computer science managers and individual contributors specializing in areas like software engineering, machine learning and database management. The U.S. Bureau of Labor Statistics (BLS) forecasts much faster than average growth for such computer science roles as: 

  • Software developers (25% growth in employment by 2032)
  • Computer research scientists (23% growth in employment by 2032)1

The exceptional skill set MSCS graduates possess helps them secure premiere tech jobs as computer scientists at companies such as Google and Meta, which can pay around $300,000.2 

Many remote work opportunities are available in computer science, but emerging tech hubs and traditional business hubs are also significant employers in the field. For instance, more than 119,000 software developers are employed in the New York City metropolitan area, where Stevens is located. Other popular cities, including San Francisco, San Jose and Seattle, are home to prominent businesses that hire many developers and offer average annual salaries between $164,000 and $199,000.3

Our diverse curriculum and specialized offerings equip students with the sought-after skills needed to thrive in their chosen fields. This degree prepares graduates with skills in computer science, Python, Amazon Web Services, automation and software engineering and opens doors to other organizations outside of tech, such as Bank of America and JPMorgan Chase, both of which have employed Stevens MSCS alumni.

1 U.S. Bureau of Labor Statistics, 2024.
2 Glassdoor, April 2024.
3 U.S. Bureau of Labor Statistics, 2024.

JOBS IN COMPUTER SCIENCE

Job Title
Employed
Median Annual Earnings
Job Title Computer and Information Systems Manager
Employed 557,400
Median Annual Earnings $169,510
Job Title Computer and Information Research Scientist
Employed 36,500
Median Annual Earnings $136,620
Job Title Computer Network Architect
Employed 180,200
Median Annual Earnings $126,900
Job Title Software Developer
Employed 1,795,300
Median Annual Earnings $132,270
Job Title Information Security Analyst
Employed 168,900
Median Annual Earnings $112,000
Job Title Computer Systems Analyst
Employed 531,400
Median Annual Earnings $103,800
Job Title Software Tester
Employed 65,000
Median Annual Earnings $101,800
Job Title Web Developer
Employed 216,700
Median Annual Earnings $84,960

Source: U.S. Bureau of Labor Statistics, 2024.

ONLINE MASTER’S IN COMPUTER SCIENCE COURSEWORK

Below are the Traditional and Advanced course sequences for the online master’s in computer science program. Students will engage in coursework on the following topics to develop skills as software development leaders and high-quality coders. The MSCS program consists of 30 credit hours, with 10 courses, and is 100% online.

TERM 1

This course introduces the Java programming language to students with little programming experience. It begins by covering fundamental concepts such as program structure and Java syntax and continues with data types, object-oriented programming, abstract classes and interfaces, control flow, exception handling, recursion and event-driven programming. Students will write, compile and execute programs operating on arrays of data or strings, including programs with graphical user interfaces. Note: This is a foundational course to be taken by students who did not have the relevant background.

This course introduces the most commonly used data structures, as well as sorting algorithms, using the Java programming language. Data structures that are introduced and compared include arrays, lists, stacks, queues, trees, priority queues, and maps, such as hash tables. Relevant principles including encapsulation, interfaces and testing are presented along with the data structures, while asymptotic complexity analysis is also introduced. Students implement and test programs that use the above data structures in conjunction with algorithms such as insertion, selection, merge, and quick sort.

Note: This is a foundational course to be taken by students who did not have the relevant background.

TERM 2

This course will provide focus on more complex data structures, and algorithm design and analysis, using one or more modern imperative language(s). Topics include advanced and/or balanced search trees, hashing, further asymptotic complexity analysis, standard algorithm design techniques, graph algorithms, complex sort algorithms and other “classic” algorithms that serve as examples of design techniques.

In this course, students will learn to develop complex system-level software in the C programming language while gaining an intimate understanding of the UNIX family of operating systems and their programming environment. Topics include the user/kernel interface, fundamental concepts of UNIX, user authentication, basic and advanced input/output (I/O), file systems, signals, process relationships and interprocess communication. Fundamental concepts of software development and maintenance on UNIX systems will also be covered.

TERM 3

This course will provide an intensive introduction to material on computer organization and assembly language programming. Topics include structure of stored program computers; linking and loading; assembly language programming, with an emphasis on translation of high-level language constructs; data representation and arithmetic algorithms; basics of logic design; processor design; data path; hardwired control and microprogrammed control. Students will be given assembly language programming assignments on a regular basis.

This course will provide an introduction to the design and querying of relational databases. Topics include relational schemas; keys and foreign key references; relational algebra (as an introduction to SQL); SQL in depth; Entity-Relationship (ER) database design; translating from ER models to relational schemas and from relational schemas to ER models; functional dependencies; and normalization.

TERM 4

This course will focus on the use and internals of modern operating systems. Primary topics include the process concept; concurrency and how to program with threads; memory management techniques, including virtual memory and shared libraries; file system data structures and input/output (I/O).

This course will provide students with a first approach to internet programming. It will give the basic knowledge on how the internet works and how to create advanced web sites by the use of script languages, after learning the basics of HTML. In this course, the student will learn how to create a complex global site through the creation of individual working modules, providing them the interpersonal skills required in business settings such as team building and cooperation.

TERM 5

Personal computing is now mobile and cloud-based. Disconnected mobile computing challenges many of the assumptions underlying much of today’s distributed systems. “Cloud computing” provides a powerful background computing facility for mobile devices, but also raises important issues of trust and privacy. Many of these issues arise in critical yet sensitive domains such as electronic healthcare delivery. Mobile computing applications are location-aware or context-aware; the privacy implications of these applications are profound. Mobile, and increasingly location aware, gaming systems are now one of the largest sectors of the world entertainment industry. The purpose of this course is to review the fundamentals of mobile systems and applications, and how they relate to services in the cloud. The course will review material from wireless communication, distributed systems, and security and privacy, as they pertain to the systems being studied. The course will involve programming mobile apps using a popular mobile computing platform, such as Android or iPhone, to get hands-on experience with the concepts being discussed in the class.

This is an introductory course for machine learning theory, algorithms and applications. The content aims to provide students with the knowledge to understand key elements of how to design algorithms/systems that automatically learn, improve and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms and various application problems. Students will be provided opportunities to simulate their algorithms in a programming language and apply them to solve real-world problems. Cross-listed with: EE 695.

TERM 1

This course will provide focus on more complex data structures, and algorithm design and analysis, using one or more modern imperative language(s). Topics include advanced and/or balanced search trees, hashing, further asymptotic complexity analysis, standard algorithm design techniques, graph algorithms, complex sort algorithms and other “classic” algorithms that serve as examples of design techniques.

The objective of this course is to give students a basic grounding in designing and implementing distributed and cloud systems, including issues in the implementation of backend services in the cloud itself. What are global consensus and Paxos, and what is their application in building cloud systems? What are the advantages and disadvantages of using distributed NoSQL stores such as Cassandra instead of relational stores such as MySQL? What are strong and weak consistency, what are the “CAP Theorem” and the “CALM Theorem” and what are their implications for building highly available services? What is a blockchain, such as the Bitcoin blockchain, and how does it relate to issues in coordinating distributed systems? What are the roles of REST, Websockets and stream processing in cloud applications? This course will combine hands-on experience in developing cloud services, with a firm grounding in the tools and principles for building distributed and cloud applications, including advanced architectures such as peer-to-peer and publish-subscribe. Besides cloud services, we will also be looking at cloud support for batch processing, such as the Hadoop framework, and its use with NoSQL data stores, such as Cassandra.

TERM 2

This course will provide an intensive introduction to material on computer organization and assembly language programming. Topics include structure of stored program computers; linking and loading; assembly language programming, with an emphasis on translation of high-level language constructs; data representation and arithmetic algorithms; basics of logic design; processor design; data path; hardwired control and microprogrammed control. Students will be given assembly language programming assignments on a regular basis.

This course will provide an introduction to the design and querying of relational databases. Topics include relational schemas, keys and foreign key references, relational algebra (as an introduction to SQL), SQL in depth, Entity-Relationship (ER) database design, translating from ER models to relational schemas and from relational schemas to ER models, functional dependencies and normalization.

TERM 3

This course will focus on the use and internals of modern operating systems. Primary topics include the process concept; concurrency and how to program with threads; memory management techniques, including virtual memory and shared libraries; file system data structures and input/output (I/O).

This course will provide students with a first approach to internet programming. It will give the basic knowledge on how the internet works and how to create advanced web sites by the use of script languages, after learning the basics of HTML. In this course, the student will learn how to create a complex global site through the creation of individual working modules, providing them the interpersonal skills required in business settings such as team building and cooperation.

TERM 4

Personal computing is now mobile and cloud-based. Disconnected mobile computing challenges many of the assumptions underlying much of today’s distributed systems. “Cloud computing” provides a powerful background computing facility for mobile devices, but also raises important issues of trust and privacy. Many of these issues arise in critical yet sensitive domains such as electronic healthcare delivery. Mobile computing applications are location-aware or context-aware; the privacy implications of these applications are profound. Mobile, and increasingly location aware, gaming systems are now one of the largest sectors of the world entertainment industry. The purpose of this course is to review the fundamentals of mobile systems and applications, and how they relate to services in the cloud. The course will review material from wireless communication, distributed systems, and security and privacy, as they pertain to the systems being studied. The course will involve programming mobile apps using a popular mobile computing platform, such as Android or iPhone, to get hands-on experience with the concepts being discussed in the class.

This is an introductory course for machine learning theory, algorithms and applications. The content aims to provide students with the knowledge to understand key elements of how to design algorithms/systems that automatically learn, improve and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms and various application problems. Students will be provided opportunities to simulate their algorithms in a programming language and apply them to solve real-world problems. Cross-listed with: EE 695.

TERM 5

In software problem areas that require exploratory development efforts, those with complex requirements and high levels of change, agile software development practices are highly effective when deployed in a collaborative, people-centered organizational culture. This course examines agile methods, including Extreme Programming (XP), Scrum, Lean, Crystal, Dynamic Systems Development Method and Feature-Driven Development to understand how rapid realization of software occurs most effectively. The ability of agile development teams to rapidly develop high quality, customer-valued software is examined and contrasted with teams following more traditional methodologies that emphasize planning and documentation. Students will learn agile development principles and techniques covering the entire software development process from problem conception through development, testing and deployment, and will be able to effectively participate in and manage agile software developments as a result of their successfully completing this course. Case studies and software development projects are used throughout.

This is an introduction to Human Computer Interaction (HCI). It covers basic concepts, principles, and frameworks in HCI; models of interaction; and design guidelines and methodologies. The course includes extensive readings and reports, as well as work on projects involving interface design and development.

Depending on your background and your postgraduate aspirations, you may have the option to choose additional courses from three in-demand areas of focus to develop additional professional expertise: AI and machine learning, business intelligence and analytics, and software development. A sample of available courses is below.

AI & MACHINE LEARNING

This course will give students a rigorous introduction to the foundations of machine learning, including but not limited to frequently used tools in linear algebra, calculus, probability and widely applied methods such as linear regression and support vector machines. In addition, this course provides hands-on training on implementing these algorithms via Python from scratch. Students will be trained to use popular Python libraries such as Numpy, Scipy and Matplotlib.

In this course, we will talk about the foundational principles that drive machine learning applications and practice implementing machine learning algorithms. Specific topics include supervised learning, unsupervised learning, neural networks and graphical models. The main goal of the course is to equip you with the tools to tackle new ML problems you might encounter in life.

Deep learning (DL) is a family of the most powerful and popular machine learning (ML) methods and has wide real-world applications such as face recognition, machine translation, self-driving cars, recommender systems, and playing the Go game, etc. This course is designed for students either with or without an ML background. The course will cover fundamental ML, computer vision, and natural language problems and DL tools for solving the problems. The students will be able to use DL methods for solving real-world ML problems. The homework is mostly implementation and programming using the Python language and popular DL frameworks such as TensorFlow and Keras. Knowledge and skills in Python programming and linear algebra are strictly required. Probability theory, statistics, and numerical analysis are recommended but not required. Knowledge in machine learning and artificial intelligence is helpful but unnecessary.

Natural language processing (NLP) is one of the most important technologies in the era of information. Comprehending human language is also a crucial and challenging part of artificial intelligence. People communicate almost everything in language: conferences, emails, customer service, language translation, web searches, reports and so on. There is a large variety of underlying tasks and machine learning models behind NLP applications. Recently, deep learning approaches have achieved high performance in many different NLP tasks. Instead of traditional and task-specific feature engineering, deep learning can solve tasks with single end-to-end models. The course provides an introduction to machine learning research applied to NLP. We will cover topics including word vector representations, neural networks, recurrent neural networks, convolutional neural networks, semi-supervised models and reinforcement learning for NLP, as well as some attention-based models.

BUSINESS INTELLIGENCE & ANALYTICS

Many managerial decisions — regardless of their functional orientation — are increasingly based on analysis using quantitative models from the discipline of management science. Management science tools, techniques and concepts (e.g., data, models and software programs) have dramatically changed the way businesses operate in manufacturing, service operations, marketing, transportation and finance. Business Analytics explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation, and optimization models.

Applied Analytics is a capstone course for the analytic-focused MBA program. It is intended to integrate all previously taken courses in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skills taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as a way to build models that can focus attention on parts of the system that can be improved through intervention. The early part of the course uses synthetic data and empirical data readily available for analysis. The second part of the course encourages students to state and solve their own problem, gathering their own data as a part of the analytic process

This course explores the area of cognitive computing and its implications for today’s world of big data analytics and evidence-based decision-making. Topics covered as part of this seminar include cognitive computing design principles, natural language processing, knowledge representation, advanced analytics, as well as IBM’s Watson DeepQA and Google’s TensorFlow deep learning architectures. Students will have an opportunity to build cognitive applications as well as explore how knowledge-based artificial intelligence and deep learning are impacting the field of data science.

The field of Big Data is emerging as one of the transformative business processes of recent times. It utilizes classic techniques from business intelligence and analysis (BI&A) along with new tools and processes to deal with the volume, velocity, and variety associated with big data. As they enter the workforce, a significant percentage of BIA students will be directly involved with big data as technologists, managers, or users. This course will build on their understanding of the basic concepts of BI&A to provide them with the background to succeed in the evolving data-centric world, not only from the point of view of the technologies required but also in terms of management, governance, and organization. Students taking the course will be expected to have some background in areas such as multivariate statistics, data mining, data management, and programming.

In this course, students will learn through hands-on experience how to extract data from the web and analyze web-scale data using distributed computing. Students will learn different analysis methods that are widely used across the range of internet companies, from start-ups to online giants like Amazon or Google. At the end of the course, students will apply these methods to answer a real scientific question or to create a useful web application.

SOFTWARE DEVELOPMENT

Personal computing is now mobile and cloud-based. Disconnected mobile computing challenges many of the assumptions underlying much of today’s distributed systems. Cloud computing provides a powerful background computing facility for mobile devices but also raises important issues of trust and privacy. Many of these issues arise in critical yet sensitive domains such as electronic healthcare delivery. Mobile computing applications are location-aware or context-aware; the privacy implications of these applications are profound. Mobile and, increasingly, location-aware gaming systems are now one of the largest sectors of the world entertainment industry. The purpose of this course is to review the fundamentals of mobile systems and applications and how they relate to services in the cloud. The course will review material from wireless communication, distributed systems, and security and privacy as they pertain to the systems being studied. The course will involve programming mobile apps using a popular mobile computing platform, such as Android or iPhone, to get hands-on experience with the concepts being discussed in the class. Programming experience with Java or C# is required.

This course will provide students with an introduction to internet programming. It will cover the basic knowledge of how the internet works and how to create advanced websites using script languages after learning the basics of HTML. The course will teach students how to build a complex global site through the creation of individual working modules, helping them develop the skills required in any business, such as proper teamwork and coordination between groups.

This course focuses on teaching students the newest technologies available in web programming. Topics include advanced client-side programming, responsive design, NoSQL databases, JQuery, AJAX, website security, and the latest frameworks. Students will be given the opportunity to suggest topics they would like to explore at the end of the semester. The course is very hands-on, where everything taught will be practiced through in-class exercises.

Theory of object-oriented design, classes, interfaces, inheritance hierarchy, and correctness; abstract data types, encapsulation, formal specification with preconditions, postconditions and invariants, and proofs of correctness; object-oriented software, objects and classes, genericity, inheritance, polymorphism, and overloading; single and multiple inheritance, programming by contract, subclassing as subcontract, specification, and verification; programming language examples include C+ +, Java, Smalltalk, and Eiffel.

This course covers the computing background for large-scale enterprise computing, including the outsourcing of computing to the cloud. The course includes developing and deploying web and microservice applications in the cloud for both client-facing and B2B applications. The course also considers cloud support for enterprise integration and Internet of Things, and NoSQL data stores such as CosmosDB. Finally, the course considers virtualization and its role in the cloud, including security in virtualization. Cloud computing: SaaS and PaaS (e.g., Azure App Service). Web applications in the cloud: ASP.NET MVC. Enterprise Web services: gRPC and Web API. Serverless applications and microservices. Gathering and processing data using NoSQL data stores, e.g., CosmosDB. Enterprise blockchain: Azure Confidential Ledger and Quorum Blockchain Service. Virtualization as the basis for scalable enterprise and cloud computing: Xen, KVM, z/VM. Secure virtualization, e.g., Security Enhanced Linux (SELinux). Programming experience with Java or C# is required.

This course covers the issues in designing and engineering large enterprise and cloud-based software systems. Such systems are distributed and require increasingly complex inter-enterprise as well as intra-enterprise coordination. Technologies such as Web Services and cloud computing provide platforms for building such systems, and architectures such as microservices and cloud-native applications, event-driven architecture (EDA), domain-driven design (DDD), representational state transfer (REST), command query responsibility segregation (CQRS), serverless and blockchain are idioms for structuring such systems. Data modeling includes E-R designs, XML and JSON Schemas, NoSQL data models, semantic data modeling (OWL), and object-relational mapping (ORM). Process modeling includes BPMN, Workflow and Petri nets. The course includes hands-on application of the concepts with tools such as Jakarta EE and Eclipse MicroProfile, Docker, Kubernetes and Kafka, and Hyperledger Fabric. Knowledge of Java or C# is required.

In software problem areas that require exploratory development efforts, those with complex requirements and high levels of change, agile software development practices are highly effective when deployed in a collaborative, people-centered organizational culture. This course examines agile methods, including Extreme Programming (XP), Scrum, Lean, Crystal, Dynamic Systems Development Method, and Feature-Driven Development to understand how rapid realization of software occurs most effectively. The ability of agile development teams to rapidly develop high-quality, customer-valued software is examined and contrasted with teams following more traditional methodologies that emphasize planning and documentation. Students will learn agile development principles and techniques covering the entire software development process from problem conception through development, testing and deployment, and will be able to effectively participate in and manage agile software developments as a result of their successfully completing this course. Case studies and software development projects are used throughout.

Introduction to the design and querying of relational databases. Topics include relational schemas, keys and foreign key references, relational algebra (as an introduction to SQL), SQL in depth, Entity-Relationship (ER) database design, translating from ER models to relational schemas and from relational schemas to ER models, functional dependencies and normalization.

Courses may be subject to availability. Some have prerequisites. Please reach out to an enrollment advisor if you are a prospective student and would like to learn more.

EARN YOUR COMPUTER SCIENCE MASTER’S ONLINE AT STEVENS

The Stevens Online MSCS program offers both traditional and advanced course sequences where students are taught by our renowned faculty and engage in coursework to develop skills as software development leaders and high-quality coders. 

Demonstrated expertise in computer science is not a prerequisite to apply to the program — and many Stevens online students leverage their MSCS to pivot to computer science careers. The wide range of experience within the Stevens online MSCS student body, paired with the MSCS cohort model, which encourages strong peer-to-peer relationships, enables students to learn from each other and grow. 

Since computer science is a rapidly evolving field, Stevens updates its online MSCS coursework frequently to keep pace with innovations in computer technology. Stevens MSCS students acquire job-ready skills, whether they are coming to computer science from another STEM field or have years of software development experience. The exceptional quality and value of the Stevens MSCS program is reflected in the fact that 99% of our computer science graduates accept jobs within three months of graduation.

FREQUENTLY ASKED QUESTIONS

MSCS graduates qualify for some of the best computer science jobs available. In an era where artificial intelligence may change the future of work, computer science graduates are leading these efforts. MSCS holders enjoy high salaries, thoughtful and engaging challenges, and benefits like remote work. They also have unparalleled job security as more and more organizations seek digital solutions to their challenges.

Computer science professionals can work in almost any industry — from emerging and innovative tech startups to old-school financial institutions, healthcare systems and automotive companies.

Yes. In addition to the fact that expert computer science professionals are in demand in almost every industry, computer science jobs are some of the highest-paying careers available. On average, a professional with a master’s degree in computer science can boost their lifetime earnings by over $900,000, easily covering their upfront costs for a degree.1 Demand for tech professionals with advanced coding skills also motivates some companies to offer tuition reimbursement, helping promising employees pay for an advanced degree.

1 Source: “Is Grad School Worth It? A Comprehensive Return on Investment Analysis” from FREOPP.org

Yes, computer science typically requires a strong foundation in mathematics. While no specific courses are required to apply, a bachelor’s degree with a minimum GPA of 3.0 from an accredited institution is required. Learn more about the admission requirements.

As a seven-time winner of the 21st Century Award for Best Practices in Distance Learning, Stevens Institute of Technology offers an exceptional student experience that mirrors the quality of our on-campus programs. With relevant courses, renowned faculty and personalized support, our programs ensure a rewarding academic journey. Benefit from a diverse cohort, direct faculty interaction and flexible study options tailored to your needs, providing you with an enriching and engaging learning environment that parallels the on-campus experience.

Financial aid and scholarship options for the online computer science program are available. Some employers offer tuition reimbursement to staff, and there may be additional financial aid opportunities available to students to support their education and prepare them for future leadership roles within their organizations.

ONLINE MSCS ACCREDITATION

Stevens Institute of Technology has been continually accredited by the Middle States Commission on Higher Education (MSCHE) since 1927. Stevens is accredited until 2027 and the next self-study evaluation is scheduled to take place during 2026-2027.

MSCS ALUMNI HAVE GONE ON TO BE EMPLOYED AT ORGANIZATIONS SUCH AS:

AMAZON

META

IBM

BANK OF AMERICA

GOOGLE

JPMORGAN

FACULTY

Our faculty includes National Science Foundation (NSF) CAREER winners as well as researchers who consult for companies such as Microsoft, IBM, Google, Bell Labs and other top industry firms.

A professional headshot of Stevens faculty member, Shudong Hao

Shudong Hao

Teaching Assistant Professor and Associate Chair for Graduate Studies in the Department of Computer Science
Reza Peyrovian

Reza Peyrovian

Senior Lecturer
Patrick Hill

Patrick Hill

Lecturer
Dominic Duggan

Dominic Duggan

Associate Professor
Samuel Kim

Samuel Kim

Teaching Professor

PROGRAM ADMISSION REQUIREMENTS

BACHELOR’S DEGREE

Minimum GPA of 3.0 from an accredited institution. Degree required to begin the program; completion not required at time of application.

TWO LETTERS OF RECOMMENDATION

Faculty members and/or professional colleagues.

STATEMENT OF PURPOSE

Optional, but strongly recommended.

ACADEMIC TRANSCRIPTS

Applicants must submit transcripts from all undergraduate and graduate institutions where credit was earned. You may submit unofficial transcripts during the application process. After admission, you will be required to submit official transcripts.

TOEFL/IELTS/DUOLINGO SCORES

Required for international students.

RESUME

Optional, but strongly recommended.

Key Dates & Deadlines

Term
Early Submit
Priority Submit
Final Submit
Start of Classes
Fall 2024
May 21, 2024
Deposit Waiver* and Application Fee Waiver Available.
June 25, 2024
Application Fee Waiver Available and Early Application Review.
August 6, 2024
September 9, 2024

*Applicants who apply by the early submit deadline and are admitted may be eligible for a $250 deposit waiver. Applicants who receive education assistance from employers or other tuition discounts are not eligible. Other eligibility conditions may apply.

TUITION*

$1,930

Per Credit (30 Credits)

$60

Application Fee

Fee waivers available

$250

Enrollment Deposit

Financial Aid

*Tuition based on fall 2023 rates effective September 2023. Tuition and fees are subject to change annually. Additional program fees may apply.

UPCOMING EVENTS

Attendees will receive an application fee waiver.

Stevens Online Computer Science Master’s: The Future of Your Career in Technology
Wednesday July 24, 2024
07:00 PM ET
The StevensOnline Experience: Current Student Perspectives
Thursday August 1, 2024
07:00 PM ET

“When I looked at the StevensOnline MSCS program, I liked that it gave students more opportunities to explore different areas of computer science. We started with Java and data structure and built from there. I think it’s nice that the program teaches foundational skills you can use in many branches of computer science.”

Rojona Feliciano ’22

A headshot of Rojona Feliciano, an MSCS student at Stevens

Request Information

By submitting this form, I agree to be contacted via email, phone, or text to learn more about the programs at Stevens Institute of Technology. Since this program is 100% online, Stevens Institute of Technology does not offer US visas to attend.