Online Master of Science in Computer Science (MSCS)
Computer Science
Program Overview
Designed for critical thinkers who want to set themselves apart with a depth of technical knowledge, the University of Tennessee, Knoxville’s online Master’s in Computer Science positions you for success.
From deep learning to software engineering coursework, your program will provide you with the space to create, innovate, and learn in a format that suits your lifestyle. With the full support of the faculty, an expert roster that includes:
- White House Office of Science and Technology Policy leaders
- National Science Foundation researchers
- Award-winning scientists, and more
You’ll master the theory and practice of advanced computer science concepts to maximize your earning and creative potential. Courses are offered year-round, including during the summer, providing students with the option to accelerate their degree progress. In as few as 18 months, you can earn your MSCS from a top-ranked public engineering school—100% online and on your schedule.
Why choose the Online MSCS Program?
At its core, the online MSCS degree program is designed for students with experience in computer science or a related field. STEM professionals who want to evolve into more curious and creative problem-solvers will develop through coursework in:
- Data Engineering
- Artificial Intelligence
- Software Security
- Cyber-Physical Systems Security
- Software Engineering
- Cloud and Web Computing
With training in advanced areas of computer science, you’ll develop an agile set of skills suited for a rapidly expanding field with significant earning potential. Top computer science roles require specialized skills you may not develop with a general degree. That’s why our online MS in Computer Science has three concentrations aligned with where the field is headed. Customize your computer science education with courses grounded in Artificial Intelligence and Machine Learning, Cybersecurity, or Software Engineering. Shaped with input from professors serving at the White House on AI and software policy—and with a Turing Award-winning professor helping lead the design—each concentration maps directly to what employers need. UT leads in AI and machine learning, and by making a concentration a graduation requirement, the program signals one thing clearly: this degree is for serious professionals ready to make an immediate impact.
Admission Requirements
- Bachelor’s degree from an accredited institution
- Minimum 3.0 undergraduate cumulative GPA
- If cumulative GPA is below 3.0 but above 2.7 the student may still qualify with an exception
- Cumulative GPA of 3.3 for international students
- Copies of official transcripts from all institutions attended undergrad and grad (official transcripts upon admission)
- Resume
- Personal statement
- $60 application fee
Preferred Qualifications
Coursework or relevant work experience in:
- Programming (Java, C, C++, and/or Python)
- Data structures and algorithms
- Computer architecture
- Systems programming
- Calculus (at least 1 semester)
- Linear algebra
- Discrete mathematics
- For relevant work experience, we request three letters of recommendation from individuals who can attest to your CS acumen. We also request that you add your competency in each of the items above to an updated resume or CV.
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Program Concentrations
The following three concentrations provide students with the specialized skills needed to be competitive leaders in the field:
Cybersecurity
The Cybersecurity concentration prepares you to defend systems and break down threats at the deepest level, whether you’re new to the field or ready for advanced work. You’ll master low-level programming, software security, and both asymmetric and symmetric cryptography while learning to spot computer architecture vulnerabilities, reverse engineer code, and run penetration tests to find weaknesses before attackers do. Through hands-on practice in defensive programming, you’ll build secure software and gain skills that connect directly to today’s job market. Graduates are ready for careers such as penetration testers, computer hardware engineers, network security engineers, or cipher mathematicians.
Artificial Intelligence and Machine Learning
The Artificial Intelligence and Machine Learning concentration equips you with the skills to work at the forefront of modern computing — no prior AI experience required. You’ll explore machine learning, large language models, deep neural networks, large data engineering, reinforcement learning, and digital fingerprinting and forensics, all on a flexible online schedule built for working adults. Graduates leave ready to pursue roles such as data scientists, data engineers, high-performance computing scientists, or prompt engineers.
Software Engineering
The Software Engineering concentration prepares you to design, build, and optimize the software that powers modern applications, whether you’re new to coding or ready to specialize. You’ll learn front-end web and graphic interface design, back-end and database engineering, scripting languages, and how to create tools for specific use cases like game development. Through hands-on study of software complexity (Big-O and Big-Omega), software optimization, database optimization, and group development workflows, you’ll gain real-world skills in cloud and web engineering that connect directly to today’s job market. Graduates are ready for careers such as software engineers, front-end developers, back-end developers, or library and API developers.
Featured Courses
Theoretical and practical aspects of machine learning techniques related to pattern recognition. Statistical methods studied include Bayesian and linear classifiers, support vector machines, neural networks, and unsupervised learning. Syntactic methods include grammatical inference, string matching, and Markov chains. Ensemble methods include random forests, adaptive boosting, and classifier fusion.
Theoretical and applied aspects of artificial intelligence. Course topics include problem solving and search, knowledge representation and reasoning, decision-making under uncertainty, machine learning, and multi-agent systems.
An in-depth introduction to software security. The focus is on identifying vulnerabilities in software, exploiting vulnerabilities in software, and software development best practices for avoiding vulnerabilities during the design, implementation, testing, and deployment of software. Coursework involves hands-on experience exploiting software vulnerabilities to increase understanding, awareness, and appreciation of software vulnerabilities.
Advanced coverage of software processes and technologies that can be used on large projects to help design, manage, maintain, and test software.
