Submit your information to be among the first to know when applications open for the M.S. AI and Entrepreneurship program.
In today’s rapidly evolving market, a traditional business degree isn’t enough. Our Master of Science in AI and Entrepreneurship bridges the gap between advanced technology and strategic leadership. We empower the next generation of innovators to build, scale and lead AI-driven ventures.
At Meharry, you will learn to harness AI to reshape industries, with a specific focus on revolutionizing health care operations and patient care. We guide you through the entire venture creation process: identifying a problem, building the AI solution, and navigating the path to commercial success.
Join a community dedicated to innovation and health care for all, and graduate with the portfolio and pitch deck you need to succeed.
Students are eligible for scholarships.
The M.S. AI and Entrepreneurship program is organized around five integrated pillars:
The AI Venture Startup Studio is a two-semester capstone course where students build and launch a working AI venture. The process spans problem selection and customer discovery to MVP development and venture pitching.
Graduates of the program will be able to:
MSAE 590 (6 hours): Capstone
The AI Venture Startup Studio is a two-semester capstone course where students build and launch a working AI venture—from problem selection and customer discovery through MVP (Minimum Viable Product) development and venture pitching.
MSAE 510: Lean Startup & Business Models for AI
3 credit hours
Introduction to the application of the lean startup methodology. An assembly of methodologies to build and validate business models for AI-driven products and services. The identification of market needs, designing MVPs for an AI-powered service, and conducting data-driven experiments to validate business models.
MSAE 520: AI Product Management & Innovation
3 credit hours.
This hands-on, interdisciplinary course equips students with the skills, frameworks and mindset to lead the creation of AI-powered products from initial concept to market launch. Students will navigate the full AI product lifecycle—covering discovery, design, development, launch, growth and maintenance—with a focus on practical product management techniques. Emphasizing entrepreneurial thinking, the course trains students to identify high-impact market opportunities and drive innovation. Through lectures, case studies, guest talks and applied projects, students will learn to balance technical feasibility, user needs and business value to deliver AI solutions that thrive in competitive markets.
MSAE 530: Agentic AI and Multi-Agent Systems
3 credit hours.
This course introduces foundational concepts of Agent AI and Multi-Agent Systems (MAS) through an entrepreneurial lens. Students will learn how autonomous agents solve problems, collaborate and drive innovation in business. Emphasis is placed on real-world applications, ethical implications and low-code tools to ensure accessibility for non-technical learners. No prior programming experience is required.
MSDS 510 Computer Programming Foundations for Data Science
3 credit hours
introduction to computer programming for data science using Python, R, and SAS.
There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
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.
MSDS 530 Statistical Inference and Modeling
3 credit hours
Regression Models and Analysis of Variance (SAS, R). Confidence Interval; Parameter Estimation, Fitting Distributions; Testing Hypothesis, Goodness of Fit; Summarizing Data; Comparing Two Samples; ANOVA; Categorical Data; Least Squares Method.
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 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 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.
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 575 Ethics in Data Science
3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 530 or MSBD 530.
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.
MSDS 730 Deep Learning
3 credit hours.
Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision-making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had a significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing and decision-making (reinforcement learning) will be demonstrated. Through in-depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch.
You will engage in hands-on research projects throughout the program, culminating with AI Venture Startup Studo. This two-semester capstone course challenges students to build and launch a working AI venture—from problem selection and customer discovery through MVP (Minimum Viable Product) development and venture pitching.
The M.S. in AI and Entrepreneurship program prepares graduates for roles that sit at the intersection of AI development, product innovation, and venture creation, including:
Applicants should meet the following requirements:
If you meet one of the above requirements, you may begin the admissions process.
Application timeline
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.
Fall 2026 priority application deadline
Application Steps
Step 1: Complete an application.
The application includes:
Step 2: Submit materials
Step 3: Interview
Complete an on-campus or virtual interview with the Admissions Committee.
International Applicants International applicants must hold a degree comparable to a regionally accredited U.S. bachelor’s or master’s degree. Transcripts from international institutions must be verified for U.S. equivalency by one of the following organizations:
Verification decisions must be transmitted directly to Meharry Medical College electronically.
English Proficiency Adequate proficiency in spoken and written English is essential for success. Please review Meharry’s F-1 English Language Policy and the college’s English proficiency requirements.
Get Prepared
Interested in this program but need to improve your programming or statistics skills? Contact us at sacsadmissions@mmc.edu to learn about preparatory courses.
Te’Onnika Hollowell
Student Recruitment and Admissions Specialist
teonnika.hollowell@mmc.edu
