Online M.S.

in Computer Science

Expand your career opportunities with an online master’s degree in computer science.

Academics

Online Options
Academic Program
Courses

The online M.S. in Computer Science is a flexible, fully online master’s program for busy professionals who want to solve big problems through computing. The program combines the academic rigor and high quality of our top-ranked in-person degree with the flexibility of an online program, allowing students to fit classes into their busy schedules.

Online Options

100% online

All courses are fully online, and many can be completed asynchronously. Most courses include opportunities for synchronous interaction with faculty.

Begin online, finish in-person

Jumpstart your degree by taking four courses online, then come to campus for face-to-face courses and an in-person campus experience. This hybrid option may be of interest to international students seeking internships and other experiential learning opportunities in the United States.

Try a class

Take up to two online courses before applying to our program. If you later join the program, you can transfer the credits earned towards your degree. Learn how »

Academic Program

The online M.S. in Computer Science has the same degree requirements as the in-person program:

Four core requirements (at least one from each of these three areas: theory, systems, artificial intelligence)
A total of 30 credits in graduate-level computer science or related fields
Must include 12 credits in 600-level classes

See the Course Requirements page for details.

Courses

The online M.S. in Computer Science is designed to take two and a half to four years to complete, depending on how many courses you take per semester. Our course offerings for the 2021-2022 academic year are listed below. New online courses will be added every year to ensure that students have access to our most popular master's level courses and can complete their degree requirements. The course schedule is subject to change.

Asynchronous course lectures will be recorded and available on-demand. Synchronous course lectures will be held at designated times.

Fall 2022

COMPSCI 514 Algorithms for Data Science

With the advent of social networks, ubiquitous sensors, and large-scale computational science, data scientists must deal with data that is massive in size, arrives at blinding speeds, and often must be processed within interactive or quasi-interactive time frames. This course studies the mathematical foundations of big data processing, developing algorithms and learning how to analyze them. We explore methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. Undergraduate Prerequisites: COMPSCI 240 and COMPSCI 311

COMPSCI 560 Intro to Computer and Network Security - SYNCHRONOUS

This course provides an introduction to the principles and practice of computer and network security with a focus on both fundamental principles and practical applications through hands-on approach. Many of the principles are taught through examples. The key topics of this course are a brief introduction to computer networking; applied cryptography; protecting users, data, and services; network security, and common threats and defense strategies. Students will complete a number of practical lab assignments as well as auto-graded quizzes/assignments.

COMPSCI 563 Internet Law and Policy - SYNCHRONOUS

This course examines the social norms, market factors, technology and law that influence the Internet. Five content areas within this framework make up the scope of the course: Internet governance, communications, privacy, security, and intellectual property. Students will be expected to do graduate level reading and analysis, including primary case law (although there is no prerequisite to have taken a law course), contribute to a robust classroom discourse and write a substantial research paper focused on a topic within the scope.

COMPSCI 589 Machine Learning - ASYNCHRONOUS

This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results. The course will also explore the use of machine learning methods across different computing contexts including desktop, cluster, and cloud computing. The course will include programming assignments, a midterm exam, and a final project. Python is the required programming language for the course. Prerequisites: COMPSCI 383 and MATH 235.

COMPSCI 653 Computer Networking

The goals of this course are to teach advanced fundamental principles underlying computer network systems. The course will cover topics in the following categories: 1) routing and transport protocols, 2) resource management, 3) datacenter network design, 4) software defined networking 5) wireless networks, and 6) network security. Prerequisites: Introductory (undergraduate level) courses in computer networks (e.g., COMPSCI 453), and algorithms (e.g., COMPSCI 311). Some familiarity with probability, optimization theory, and operating systems will be helpful.

COMPSCI 677 Distributed and Operating Systems

An in-depth examination of principles of distributed systems and advanced operating systems. Topics include client-server programming, distributed scheduling, virtualization and cloud computing, distributed storage, IoT. Familiarity with an undergraduate course on operating systems (COMPSCI 377 or equivalent) is helpful.