Computer Science MS
Join our results-focusedMS program and develop the credentials employers want in one of the world's top markets for tech talent.
Our program trains students of diverse backgrounds — computer science majors and those pivoting into the field — through a common core curriculum andspecializations in applied computer science, cybersecurity, game and computational media, or data science. Students may receive funding as research assistants working with professorsor in other university roles.
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At a Glance
- Mode of Study
On campus - Time to Complete
18 mos. or your own pace - Course Scheduling
Variable, mostly afternoons and evenings - Requirements
30 credits with capstone
- Tuition and Funding
$2000 per credit. All applicants automatically considered for merit scholarships; visitTuition and Funding. - Admissions
Start in fall or spring; no GRE scores or tech degree required: Admission and Requirements.
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Current Researchareas include computational science and signalprocessing; cybersecurity, data science, AI, and machine learning; neuroscience, visual/tactile perception, and DNA sequencing; game design, illusions, and social simulations.
DC Career Launch
The DC tech sector of federal and state agencies, NGOs, higher ed, andcontractors/consultanciesoffers max career choice —with triplethe national concentration of computer science jobs and starting salaries for MS graduates over $100K.
Responsible AI
AU profs Nathalie Japkowitz and Zois Boukouvalas release new work on robust evaluation methods, November 2024 on Cambridge UP:
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Specialize Your Studies
The MS programoffers students 4specializations:
- Applied Computer Science
- Cybersecurity
- Data Science
- Game and Computational Media
Students choose their specialization while completing a 5-course common core. Startingin their penultimate semester, they begin work on an expansivecapstone project with theoretical or practicaloptions: Internship and Project,Project, or Thesis.
Beyond the capstone, students may work with facultyprojects andlabs, including the AU Game Center and Game Studio (for persuasive play and interactive media); Design and Build Lab (prototyping and digital fabrication); Data Science Lab (machine learning development and application); Lab for the Study of Sensation, Perception, Reality, and Illusion; andComputational Material Perception Laboratory (applying human perception principles to improve machine perception). Our studentsintern with a range of local, national, and international firms, often on campus or arranged through AU alum networks.
Recent student projects and internships include
Projects
- Prediction of Breast Cancer Recurrence with Machine Learning
- Music as a Memory Enhancer: A Music Therapy Web Application for Dementia Patients
- Pocket Legal: Exploring Open-Access API Content & Features to Create a Legal Literacy Dashboard
- Adopt A Pixel Project: Developing Useful Products to Boost Effective Learning for NASA GLOBE (Global Learning and Observations to Benefit the Environment)
Internships
- DealRoom: Machine Learning Intern building machine-learning prototype on AWS Sagemaker for a model that will analyze and rationalize trends in the M&A landscape for the recent 100 years.
- Workday: Technology Analyst assisting with enterprise-level implementation of finance, budget, and HR software applications.
- Reep Technologies: Machine Learning Intern testing machine learning results quality and developing protocols.
Curriculum
Students progress through three phases of the program for the required 30 credits:
- 5 core courses: 621 Design/Organization of Programming Languages; 634Database Management Systems; 650Software Engineering; 665 Operating Systems;and 668 Artificial Intelligence.
- 6-creditCapstone with three options: Internship and Project, Project, or Thesis
- 9 elective credits in their chosen specialization:
Applied Computer Science
- /
Cybersecurity
Data Science
Game and Computational Media
Visit our Admission and Course Requirementspage for complete curricularand admissiondetails, and guidance for international students.
Spotlight: Yanet Yilma
“The community at AU is tight knit. It’s easy to contact professors, conduct independent studies, and participate in faculty research. Professors within the program explore many different areas of studies, so it is easy to learn from them and obtain internship and career opportunities through their advice and recommendations. ”
—Yanet Yilma, MS Computer Science alumna ’23
Career Outlook
The Bureau of Labor Statistics reports a median annual salary over$131,000for Computer Scientists with starting salaries for MS graduates exceeding $100,000 nationwide as of 2021 (typically a 30-50% higher than starting salaries with bachelor's degrees).The DC metro area has triple the nationalconcentration of jobsand job growth projected above the 21% national rate over the next decade.
In DC and surrounding areas, you'll be able to take advantage of the concentration of federal and state agencies, private contractors and consultancies, business, NGOs — and AU's active alumni network — to find career choices you want. Recent graduates have gone on to a range of positions at firms including
- Adobe Systems Incorporated
- Amazon Web Services, Inc.
- Applied Research in Acoustics LLC
- Association for Women in Science
- Boeing
- CyberData Technologies, Inc.
- GEICO
- Guidehouse Defense & Security
- ICANN
- Lockheed Martin
- Pew Research Center
- USAir Force
- United Technologies
- Walmart Labs
- Williams-Sonoma, Inc.
What You'll Walk Away With
- Programming skills: proficiency in at least one of Java, Python, or C++, in addition to R, SQL, JavaScript, Tidyverse, or Julia, and a strong understanding of programming principles, algorithms and data structures.
- Software development skills: design, develop, and implement software systems, including requirements analysis, design, implementation, testing, database management, and deployment.
- Communication and teamwork skills: practical experience with technical documentation, including testing, security, and maintenance planning; competence with position or research papers and case studies; ability to collaborate with both technical and non-technical colleagues.
- Cybersecurity: ability to stay up to date on evolving risk management, cyber-attack, and digital forensic trends and techniques; advanced understanding of network security protocols, technologies, tools, and best practices; demonstrable expertise with computer vision, crypto algorithms, and legal or ethical standards.
- Data Science: create ethical, data-driven solutions for real problems in diverse fields; collect, clean, and organize large amounts of data from open data sites, through APIs, via web scraping or from SQL-based databases; develop reproducible analyses
Ready to dive in?
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