MSDSMaster's

Master of Science in Data Science (MSDS)

Transform raw data into strategic intelligence that drives decisions.

9–12 months36 Credits
Program Snapshot

36Total credit hours

Data Science Courses + Master's Thesis

Degree awarded
Master of Science in Data Science
Duration
9–12 months full-timeup to 18 months part-time

The MSDS program equips students with the technical and analytical skills required to transform data into actionable insights. Students develop expertise in data analytics, machine learning, artificial intelligence, statistical computing, and data visualization.

Program Goals

What the MSDS is designed to achieve

Five overarching goals shape every course, assessment, and project in the program.

  1. 01

    Develop graduates who demonstrate advanced mastery of core data science disciplines, including programming in Python and R, artificial intelligence, machine learning, database systems, exploratory data analysis, and data visualization.

  2. 02

    Equip students with the theoretical foundations and applied research skills to design and execute original scholarly investigations in data science and its interdisciplinary applications across business, healthcare, and technology.

  3. 03

    Foster intellectual rigor, ethical responsibility, and a commitment to the responsible collection, processing, and deployment of data and data-driven models.

  4. 04

    Build research competency enabling graduates to pursue doctoral programs or contribute to advanced research and data science environments in academia, government, and industry.

  5. 05

    Align graduate competencies with Florida's priority data and technology workforce sectors, including healthcare informatics, financial technology, defense analytics, and cloud-based enterprise data systems.

Program Learning Outcomes

What you'll be able to do

Upon successful completion of the MSDS program, graduates will be able to:

  • PLO 1

    Apply advanced theoretical and applied knowledge in core data science domains, including programming, artificial intelligence, machine learning, database systems, data analysis, and data visualization.

  • PLO 2

    Design and implement data-driven solutions to complex real-world problems using Python, R, and industry-standard data science tools and frameworks.

  • PLO 3

    Design and execute independent research, including formulating a research question, conducting a systematic literature review, selecting appropriate methodologies, and producing original scholarly contributions.

  • PLO 4

    Evaluate ethical implications of data science technologies and research practices, applying frameworks related to data privacy, algorithmic fairness, and responsible AI deployment.

  • PLO 5

    Apply data science knowledge to real-world problems, including healthcare informatics, financial technology, defense, and cloud-based enterprise systems.

Program Structure

Your path to the degree

Two stages, 36 credit hours. Build advanced mastery across the major pillars of data science — including a dedicated research gateway course — then make an original contribution to the discipline through your master's thesis.

Stage 1 · 30 credit hours

Data science courses

All MSDS students are required to complete the following ten courses. These courses establish the theoretical and applied foundation across the major pillars of data science and include one dedicated research course (RES 900) that serves as the mandatory gateway to the thesis sequence. A minimum grade of B is required in each course.

10 courses · 30 credit hours

Stage 2 · 6 credit hours

Master's Thesis

DAS 7076 credit hours

Master's Thesis

The Master's Thesis is a mandatory degree requirement for all MSDS students and is the culminating scholarly experience of the program.

  1. Step 1Formulate a research question
  2. Step 2Conduct a systematic literature review
  3. Step 3Select appropriate methodologies
  4. Step 4Produce an original contribution
  5. Step 5Document and present your findings

The thesis demonstrates the student's capacity for independent research, original contribution to the discipline, and professional scholarly communication.

Skills You'll Gain

Capabilities you'll carry into the boardroom

  • Python and R programming for data analysis and modeling
  • Machine learning model design, evaluation, and tuning
  • Artificial intelligence foundations, reasoning, and NLP
  • Database systems and big data management
  • Exploratory data analysis and feature engineering
  • Statistical computing and reproducible research
  • Data visualization, dashboards, and data storytelling
  • Research methods and academic writing for data science
FAQs

Frequently asked questions