Advance your career with our flexible online M.S. in Data Science program. You will learn valuable skills like machine learning, data collection, statistical inference and data visualization and how to communicate your findings. Hands-on projects will challenge you to apply those concepts, giving you practical experience that will impress potential employers. You will also learn from an HBCU committed to ensuring future algorithms and computing solutions benefit everyone.
Why Choose Our Online M.S. Data Science Program?
Students are eligible for scholarships up to 50% of tuition.
Our professional program will prepare you to analyze big data and communicate your findings to influence decisions in any industry. In interactive classes, you will learn every aspect of data science. The curriculum includes programming languages, data infrastructure, data collection, data engineering, machine learning, statistical inference and data visualization. In the comprehensive capstone course, you will apply your data science education to a real-world business problem in a domain of your interest.
The MS Data Science curriculum encompasses all important aspects of data science, including:
14 courses, 42 graduate credits
Students will gain a common background in data science through thirteen core courses. The degree culminates with a comprehensive real-life, industry-type capstone, oriented toward the student’s domain of interest.
Courses are offered on a Fall, Spring and Summer Semester schedule. The Fall and Spring Semester includes three concurrent courses. Each class is held once per week, for three hours. The Summer Semester has one course. It will take two years to complete the program.
MSDS 510 Computer Programming Foundations for Data Science
3 credit hours, Spring, Summer. Pre-requisite(s): None.
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 515 Data Conscientiousness
3 credit hours, Spring & Summer. Pre-requisite(s): None.
Using Excel, JavaScript, Python, SAS, SQL, and R to develop Data Conscientiousness: ability to immediately recognize the issues involved in data organization that will need to be addressed to tackle a specific problem. Developing skills in all of the preprocessing, scrubbing, cleaning tools (“search and rescue” operations), data imputation and handling of missing values, checking for adherence to data standards, and all of the rest of the time-consuming and dirty work of data projects. Linking structured and unstructured data sources and recognizing how to reshape data to get it into a computer-friendly format (i.e., rows and columns) required by analytical and statistical methods. A gentle introduction to statistics to enable understanding of the statistical difference between observations and variables, along with knowledge of the different scales of measurement so as not to end up with nonsensical analytical results.
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 Mathematical & Statistical Foundations for Data Science
3 credit hours, Fall, Spring. Pre-requisite(s): Undergraduate Calculus or Elementary Statistics.
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 525 Data Management Foundations for Data Science
3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 510, 515.
The concepts and structures used to store, analyze, manage, and present (visualize) information and navigation using Python, SQL, SAS, and QGIS. Topics will include information analysis and organizational methods, and metadata concepts and applications. Students will be assisted to identify disparate data sources needed to perform analysis for a given real-world problem. Typically, data from a single source will not be adequate to perform the required analysis. Students will pull data from the disparate data sources and import it into SAS and use several SAS procedures to detect invalid data; format, validate, clean the data; and impute the data if it is missing. This will prepare the data for statistical analysis and decision modeling in SAS.
MSDS 530 Statistical Inference and Modeling
3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 510, (MSDS 520 or MSBD 520).
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 Programming Languages for Data Science
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 525.
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, Fall, Summer. Pre-requisite(s): (MSDS 530 or MSBD 530), (MSDS 535 or MSBD 540).
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 555 Big Data Management and Analytics
3 credit hours, Fall, Summer. Pre-requisite(s): MSDS 530, 535.
(SAS, Python, SQL, MapReduce/Hadoop). An overview of modern data science: the practice of obtaining, storing, modeling, manipulating, analyzing, and interpreting data. Emerging Big data processing frameworks. NoSQL storage solutions. Memory resident databases and graph databases. Ability to initiate and design highly scalable systems that can accept, store, and analyze large volumes of unstructured data in batch mode and/or real time. Organization, administration, and governance of large volumes of both structured and unstructured data.
MSDS 560 Natural Language Processing
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 550.
(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 565 Predictive Modeling and Analytics
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 550 or MSBD 550.
Tools and techniques for building statistical or machine learning models to make predictions based on data. NLP and Text Analytics, Time Series, Experimentation and Optimization.
MSDS 570 Visualization and Unstructured Data Analysis
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 520 or MSBD 520.
This course provides a comprehensive exploration of data visualization as a critical tool for interpreting complex data, facilitating insights, and supporting data-driven decision-making. Students will learn data visualization tools and technologies (including Matplotlib, Seaborn, Plotly) essential to analyze massive disparate amounts of information and make data-driven decisions. They will develop the skills necessary to transform raw data into compelling visuals that communicate insights effectively and responsibly.
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 580 Research Methods
3 credit hours, Spring, Summer. Pre-requisite(s): MSDS 565, 575.
The research process investigating information needs, creation, organization, flow, retrieval, and use. Stages include: research definition, question, objectives, data collection and management, data analysis and data interpretation. Techniques include: observation, interviews, questionnaires, and transaction-log analysis.
MSDS 590 Capstone
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 580.
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.
Our students have pursued meaningful research topic such as:
Your research projects will be powered by a high-performance, supercomputer network and access to real-world data, including Meharry’s robust dataset.
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
