Vanderbilt Master's in Data Science

Orientation in August. Launch and level-set. Capstone phase: teams and topics are assigned.

Start together, start prepared. You begin the first project tasks with your team, and skill-refresh courses in programming and probability get everyone to the same starting line; a summer assessment lets you opt out if you are already there.

Courses

• DS 5210 Intro to Programming (1 credit)
• DS 5110 Intro to Probability and Statistics (1 credit)
• Capstone project assignments, bootcamp and orientation

Mod 0 courses can be waived by summer assessment. They do not add to tuition.

August to October. Statistics, analysis, programming. Capstone phase: scope the problem statement, data sources and success metrics.

Build the toolkit you will use all year. Core courses in statistical inference, exploratory analysis and programming sit beside a business foundations course that teaches you to define a project, connect it to what an organization needs and say so clearly.

Courses, 8 credits

• DS 6110 Probability and Statistical Inference (2 credits)
• DS 6120 Exploratory Data Analysis (2 credits)
• DS 6210 Principles in Programming and Simulation (2 credits)
• DS 6410 Business Foundations for Data Scientists (2 credits)

October to December. Databases, machine learning, responsible practice. Capstone phase: ethics and privacy, sensitive data, responsibilities, governance.

Lay a solid and responsible data foundation while your capstone moves from concept to a concrete plan. Databases and SQL, machine learning and data governance broaden the technical toolkit; the first generative AI course puts transformer models in your hands.

Courses, 8 credits

• DS 6220 Data Management Systems (2 credits)
• DS 6310 Machine Learning (2 credits)
• DS 6420 Data Governance and Ethical Leadership in Data Science (2 credits)
• DS 6340 Generative AI Models in Theory and Practice (2 credits)

January to March. Build, code, iterate. Capstone phase: build it, with GitHub, agile practice and mid-course corrections.

You are writing the code for your capstone while learning to scale to big data and to implement the algorithms of data science from the bottom up. A teamwork course runs the project like a workplace. Your first elective opens here.

Courses, 8 credits

• DS 6230 Big Data Scaling (2 credits)
• DS 6320 Data Science Algorithms (2 credits)
• DS 6430 Data Science Teamwork in Practice (2 credits)
• Elective (2 credits)

March to May. Deliver, tell the story, go deeper. Capstone phase: deliver it, a formal presentation to stakeholders.

You finish the capstone and learn to present it to stakeholders and leadership. Required Deep Learning and a second elective sharpen your edge. The mod ends with your formal capstone presentation and graduation in May.

Courses, 6 credits

• DS 6330 Deep Learning (2 credits)
• DS 6440 Data Storytelling and Communication (2 credits)
• Elective (2 credits)
• DS 6670 Capstone Development (0 credits, recorded on your transcript)

Electives. Pre-approved options include a generative AI track (Context-Augmented Generative AI, Advanced Generative AI, Natural Language Processing), Time Series Analysis, NoSQL for modern data applications, and Owen courses that share our calendar. An internship or research practicum can count for elective credit with approval.