ABOUT THE PROGRAM
The Master of Science (M.S.) program in Applied Data Science (MSADS) addresses the rising demand for data science professionals in today’s dynamic technological landscape. Through its interdisciplinary approach, the program equips students with a comprehensive skillset, merging knowledge from diverse fields such as computer science, statistics, and business.
Industry careers looking for students with a MS degree in Applied Data Science include finance and insurance, healthcare and social assistance, information, manufacturing, transportation, scientific and technological services, retail trade, and more.
The MS program is a non-thesis major in Applied Data Science with a total of 30 credits hours. The program is designed for students or working professionals with a B.S. degree in a non-computing engineering field such as agricultural and biological engineering, business, biomedical engineering, civil engineering, environmental engineering, transportation engineering and possibly industrial systems engineering.
The curriculum will consist of a set of 7 core courses (21 credit hours), 2 specialization courses (6 credit hours) in a specific area, and a capstone project (3 credit hours) in the selected specialization. Throughout the curriculum you will interact with HWCOE faculty with a diverse research portfolio, carry hands-on software experiments, access to HiPerGator supercomputer resources, and participate in an interdisciplinary capstone project.
Key Features
The projected benefits of the program include versatile skillset, real-world applicability, industry relevance, adaptability to change, customization and specialization, enhanced problem-solving abilities, global perspectives.
You can customize the curriculum to your preferred specialization. The MSADS currently offers the following specialization areas:
- Artificial Intelligence (AI)
- Biomedical Engineering
- Agricultural and Biological Engineering
- Data Analytics for Industrial Systems Engineering
- Environmental Engineering
- Transportation
A MESSAGE FROM THE PROGRAM Coordinator
Welcome to the Master of Science (M.S.) in Applied Data Science at the University of Florida!
As the program coordinator, I am excited to lead a team of dedicated faculty and staff committed to your academic and professional success. The MSADS program takes an interdisciplinary approach, preparing students for success in the ever-evolving field of data science. We aim to equip our students with the tools and knowledge necessary to navigate complex datasets, extract meaningful insights, and make data-driven decisions.
One of the unique of our program is the flexibility it offers through specialized tracks. Students can tailor their education to match their interests and career goals by choosing a specialization area.
I’m excited to lead you through this educational journey and look forward to helping you achieve your academic and professional aspirations.
GENERAL ADMISSION REQUIREMENTS
The minimum admission requirements include:
- B.S. in a non-computing engineering field.
- GPA requirements as defined by the Graduate School (recommended GPA at 3.0 or higher).
- 3 Letters of Recommendations.
- 1 Statement of Purpose (SOP).
- English proficiency test, if applicable. (Check if you need an English proficient test here).
- GRE is not required.
Note to applicants: The Admissions Committee reviews packets on a rolling basis for all packets that include all items from the list above. Your application packet will be labeled as incomplete until you submit all the items listed above.
| APPLY NOW! |
DEGREE REQUIREMENTS
The MS program is a non-thesis major in Applied Data Science with at least a total of 30 credits hours beyond bachelor’s degree required. These hours include MSADS master’s degree work taken at the University of Florida or, if approved, up to 6 hours of master’s degree work earned at another approved university outside UF. Course substitutions must be petitioned and are considered on a case-by-case basis.
Requirements include completion of the following:
- MSADS core courses: 21 credits
- MSADS specialization courses: 6 credits
- MSADS capstone project: 3 credits
GRADUATE HANDBOOK
EED Graduate Handbook 2022-2024
COURSES
Course descriptions are available in the Graduate Catalog.
MSADS Core Courses (21 cr)
- EGN 6445 Mathematical Foundations for Data Science for Engineers (3 credits)
- EGN 5442 Programming for Applied Data Science (3 credits)
- CAP 5771 Introduction to Data Science (3 credits)
- EGN 6556 Mathematical Foundations for Data Science for Engineers II (3 credits)
- EEE 5776 Applied Machine Learning (3 credits)
- EEE 6778 Applied Machine Learning II (3 credits)
- LAW 6936 Artificial Intelligence, Technology, and the Law (3 credits)
MSADS Specialization Courses (6 cr)
Choose two courses in one of the following concentrations:
Artificial Intelligence (AI)
- EVN 6932 Algorithms for Imaging Spectroscopy (3 credits)
- ESI 6617 High-Dimensional Data Analytics (3 credits)
- EEE 6512 Image Processing and Computer Vision (3 credits)
Environmental
- OCP 6168 Data Analysis Techniques for Coastal Ocean (3 credits)
- ABE 6035 Advanced Remote Sensing: Science and Sensors (3 credits)
- EVN 6932 Algorithms for Imaging Spectroscopy (3 credits)
- EEE 6512 Image Processing and Computer Vision (3 credits)
Data Analytics in Industrial Systems Engineering (ISE)
- EIN 6905 Data Analytics for Systems Monitoring (3 credits)
- ESI 6617 High-Dimensional Data Analytics (3 credits)
Biomedical
- BME 6522 Biomedical Multivariate Signal Processing (3 credits)
- BME 6938 Multimodal Data Mining (3 credits)
- BME 6938 Biomedical Data Science (3 credits)
Agricultural and Biological Engineering
- EGN 5XXX AI for Automated Sensor Data Interpretation (3 credits)
- ABE 5038 Fundamentals and Applications of Biosensors (3 credits)
- ABE 6840 Data Diagnostics
- ABE 5643C Biological Systems Modeling (3 credits)
- ABE 6649C Advanced Biological Systems Modeling (3 credits)
Transportation
- EGN 5215 Machine Learning Applications in Civil Engineering (3 credits)
- TTE 6505 Discrete Choice Analysis (3 credits)
MSADS Capstone Project (3 cr)
- EGN 6933 Project in Applied Data Science (Non-Thesis Project) (3 credits)
A typical curriculum will look like the following:
| Semester | Spring | Fall |
| Year 1 |
EGN 6445 Mathematics for Data Science for Engineers |
EGN 6556 Mathematics for Data Science for Engineers II CAP 5771 Introduction to Data Science EEE 5776 Applied Machine Learning |
| Year 2 | EEE 6778 Applied Machine Learning II EGN 6933 Concentration Class I EGN 6933 Concentration Class II |
EGN 6933 Project in Applied Data Science |
Contact Information
For inquiries about the M.S. in Applied Data Science, please contact:
Catia S. Silva, Ph.D.
catiaspsilva@ece.ufl.edu | (352) 392-6502 | MALA 3122
Instructional Assistant Professor, Department of Electrical and Computer Engineering
Program Coordinator, M.S. in AI Systems, Herbert Wertheim College of Engineering
GitHub Campus Advisor
University of Florida
Gainesville, FL 32611
Webpage: faculty.eng.ufl.edu/catia-silva
GitHub: github.com/catiaspsilva
—
Pamela Simon
phs@ufl.edu | (352) 392-9672
Academic Assistant II, Department of Engineering Education
University of Florida
Gainesville, FL 32611