Elevate your career with our advanced MS in Computer Science program. With expertise in artificial intelligence, software development, and high-performance and quantum computing, you will be ready for future, innovative careers in tech.
Why Choose Our MS Computer Science Degree?
Students are eligible for scholarships up to 50% of tuition.
We integrate AI/ML, cybersecurity and advanced computing to prepare you for future computer science roles. Our courses cover:
14 courses, 42 graduate credits
Students will gain a common background in bioinformatics through thirteen core courses. The degree culminates with a comprehensive real-life, industry-type capstone, oriented toward the student’s domain of interest.
MSDS 575 Ethics in Data Science
3 credit hours
Analysis of ethical issues, algorithmic challenges, and policy decisions (and social
implications of these decisions) that arise when addressing real-world problems through the lens of data science, and the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in analysis.
MSCS 540 Theoretical Foundations of Artificial Intelligence
3 credit hours
This course delves into the theoretical underpinnings of machine learning. It covers foundational concepts, theoretical results, and proofs that inform modern machine learning algorithms. Students will explore learning theory, statistical learning theory, and algorithmic principles that form the basis for developing and analyzing machine learning models.
MSDS 520 Math and Statistical Foundations for Data Science
3 credit hours
Techniques for building and interpreting mathematical models of real-world phenomena in and across multiple disciplines, including linear algebra, discrete mathematics, probability, and calculus, with an emphasis on applications in data science and data engineering. Introduction to statistical methods that are used to solve data problems. Topics include sampling and experimental design, group comparisons, parametric statistical models, multivariate data visualization, multiple linear regression, and classification. Students will obtain hands on experience in implementing a range of commonly used statistical methods on numerous real-world datasets.
MSCS 565 Advanced Computer Systems
3 credit hours
This course offers an in-depth exploration of modern computer systems, focusing on advanced topics in computer architecture, operating systems, networking, and quantum computing. The course begins with a review of foundational computer systems concepts and progresses to advanced pipelining, superscalar architectures, distributed computing, and system-level design. Students will engage with cutting-edge topics such as Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and the principles and challenges of quantum computing. Through a combination of theoretical instruction and practical assignments, students will develop a comprehensive understanding of advanced computer systems and their applications in real-world scenarios.
MSCS 580 Advanced Analysis of Algorithms
3 credit hours
This course provides an in-depth exploration of advanced topics in algorithm analysis. Students will study a range of sophisticated techniques and their applications, focusing on both theoretical and practical aspects. Emphasis will be placed on problem-solving, algorithm design, and performance analysis, with topics drawn from current research and emerging trends in the field.
MSDS 535 Further Mainstream Program Languages for Data Science
3 credit hours
This course covers other useful mainstream programming languages for data science, beyond Python, R, SQL, and SAS. These “other” potential programming languages supplement the ability to crunch numbers and equip the data scientist with good all-round programming skills. Programming languages covered will vary depending on industry popularity. While some of the programming languages may not be covered in detail, examples include Java, Scala, Julia, MATLAB, JavaScript, TensorFlow, Go, Spark.
MSDS 550 Computational Machine Learning
3 credit hours
Introduction to machine learning with business applications. Survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification.
MSCS 575 High Performance Computing
3 credit hours
This course provides a comprehensive introduction to High-Performance Computing (HPC) and its applications in various fields. Students will explore the fundamental concepts of HPC, including distributed and parallel computing, HPC architecture, and operating systems. The course covers key optimization techniques, libraries, and tools essential for developing and managing HPC systems. Additionally, students will gain insights into specialized topics such as System on Chip (SoC) design, neuromorphic computing, and CUDA programming. Through hands-on projects and practical exercises, students will learn to harness the power of HPC for solving complex computational problems.
MSDS 560 Natural Language Processing
3 credit hours
(Python, SAS, R). A comprehensive review of text analytics and natural language processing with a focus on recent developments in computational linguistics and machine learning. Students work with unstructured and semi-structured text from online sources, document collections, and databases. Using methods of artificial intelligence and machine learning, students learn how to parse text into numeric vectors and to convert higher dimensional vectors into lower dimensional vectors for subsequent analysis and modeling. Applications include speech recognition, semantic processing, text classification, relevant search, recommendation systems, sentiment analysis, and topic modeling. This is a project-based course with extensive programming assignments.
MSDS 700 Fundamentals of Database Management Systems
3 credit hours.
Introduction to database concepts and the relational database model. Topics include ER Model, Relational Model, Relational Algebra, SQL, normalization, Indexing, Normal Forms, design methodology, DBMS functions, Security, Transaction Management, data-base administration, and other database management approaches such as client/server databases, object-oriented databases, and data warehouses. Strong emphasis on database system design and application development.
MSDS 655 AI in Cybersecurity
3 credit hours.
What is artificial intelligence (AI)? What does it mean for cybersecurity? And how AI can be integrated to achieve the goals of cybersecurity? This course designed to answer the above questions. In this course, a mix of key AI technologies will be introduced to support the understanding of the decision-making process when cybersecurity is concerned. The course will address key AI technologies in an attempt to help in understanding their role in cybersecurity. AI deficiently will complement and strengthen the cybersecurity practices and will improve their applications in enhancing our security.
MSCS 530 Principles of Programming Languages
3 credit hours
This course explores the principles and concepts underlying programming languages. It provides a comprehensive understanding of language design, semantics, and implementation. Topics include syntax and semantics, type systems, language paradigms, and the practical aspects of programming language implementation. The course also covers recent trends and innovations in programming languages.
MSDS 545 Computational Software Engineering
3 credit hours
Introduction to systems development for computational science. Design, develop, and deploy a set of software components to produce a scalable, reliable, and reproducible experimental system for scientific investigation. Use a variety of approaches to software development team organization, and select techniques that are appropriate in different circumstances
MSDS 590 Capstone
3 credit hours
Comprehensive real-life industry-type capstone, oriented toward the student’s domain of interest. Projects will include: formulation of a question to be answered by the data; collection, cleaning and processing of data; choosing and applying a suitable model and/or analytic method to the problem; and communicating the results to a non-technical audience.
Career Opportunities and Outcomes for MS Computer Science Graduates
Our MS in Computer Science program prepares you for lucrative and high-impact roles in the rapidly evolving tech industry.
Possible research projects in our MS Computer Science program
You will enhance your MS Computer Science classroom experience with hands-on, projects that will prepare you for an impactful career. Possible projects include:
You will also benefit from access to a high-performance, supercomputer network and real-world data.
If you meet one of the above requirements, you may begin the admissions process.
Applicants can apply to begin our programs in the Fall Semester, starting in August. While we accept students on a rolling basis, the following dates serve as application deadlines the semester. Please contact the Office of Enrollment Management about applying after these deadlines.
May 30, 2026: Priority deadline for application. All materials outlined in Step 2 of the applicant process are due by June 13, 2026.
Step 1
Complete an application.
That application will include the following:
Step 2
Step 3
On-campus (or virtual) interview with the School’s Admissions Committee.
International applicants must hold a degree comparable to a regionally accredited US baccalaureate or master’s degree. Applicants submitting transcripts from international colleges and universities are required to have them verified for US degree program equivalency before being considered for admission. Verification from the following organizations is acceptable:
The decision of the verifying organization must be transmitted directly to Meharry Medical College in electronic form.
Adequate proficiency of spoken and written English is essential to success in graduate study, and medical residency training at Meharry Medical College.
Please review Meharry’s F-1 English Language Policy and the College’s English proficiency requirements policy.
Are you interested in the M.S. Data Science or M.S. Biomedical Data Science program but need to improve your programming or statistics skillset? Contact us at sacsenrollment@mmc.edu or complete the request information form to learn about courses you can take to prepare you for either program.
Jayla Stallworth, PHR
Student Recruitment and Admissions Specialist
jayla.stallworth@mmc.edu
