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Masters

Applied Data Science

University of Florida
Gainesville , United States
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Masters
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About Program

Program Overview

University of Florida

The University of Florida offers a Master of Science degree with a major in Applied Data Science. This degree is designed for engineering students and working professional engineers with a B.S. degree in a non-computing engineering field.


Program Overview

The Master of Science with a major in Applied Data Science degree will provide students with a working knowledge of techniques and software commonly used in Data Science. The degree is designed for engineering students and working professional engineers with a B.S. degree in a non-computing engineering field, that is, for engineering students and professional engineers who have engineering degrees other than Computer Science or Computer Engineering and possibly Industrial and Systems Engineering.


Degrees Offered

  • Master of Science

Program Courses

Core/Required Courses:

  • CAP 5771: Introduction to Data Science (3 credits)
  • COT 5615: Mathematics for Intelligent Systems (3 credits)
  • EEE 5776: Applied Machine Learning (3 credits)
  • EEE 6778: Applied Machine Learning II (3 credits)
  • EGN 6446: Mathematical Foundations for Applied Data Science (3 credits)
  • EGN 5442: Programming for Applied Data Science (3 credits)
  • EGN 6933: Special Topics (1-3 credits)
  • LAW 6930: Selected Legal Probs (1-4 credits)

Example Specialization Courses:

  • ABE 5038: Recent Developments and Applications in Biosensors (3 credits)
  • ABE 5643C: Biological Systems Modeling (3 credits)
  • ABE 6035: Advanced Remote Sensing: Science and Sensors (3 credits)
  • ABE 6649C: Advanced Biological Systems Modeling (3 credits)
  • ABE 6840: Data Diagnostics (3 credits)
  • BME 6522: Biomedical Multivariate Signal Processing (3 credits)
  • BME 6938: Special Topics in Biomedical Engineering (1-4 credits)
  • EIN 6905: Special Problems (1-6 credits)
  • OCP 6168: Data Analysis Techniques for Coastal and Ocean Engineers (3 credits)
  • TTE 6505: Discrete Choice Analysis (3 credits)

Engineering Education Departmental Courses:

  • EGN 6913: Engineering Graduate Research (0-3 credits)
  • EGN 6933: Special Topics (1-3 credits)
  • EGS 6012: Research Methods in Engineering Education (3 credits)
  • EGS 6020: Research Design in Engineering Education (3 credits)
  • EGS 6050: Foundations in Engineering Education (3 credits)
  • EGS 6051: Instructional Design in Engineering Education (3 credits)
  • EGS 6054: Cognition, Learning, and Pedagogy in Engineering Education (3 credits)
  • EGS 6056: Learning and Teaching in Engineering (1 credit)
  • EGS 6085: Advanced Engineering Educational Technology (3 credits)
  • EGS 6930: Engineering Education Seminar (1 credit)
  • EGS 6940: Foundations of Research to Practice in Engineering Education (1 credit)
  • EGS 6949: Research to Practice Experience in Engineering Education (1-3 credits)
  • EGS 6971: Research for Master’s Thesis (1-12 credits)
  • EGS 7979: Advanced Research (1-12 credits)
  • EGS 7980: Research for Doctoral Dissertation (1-12 credits)

College of Engineering Courses:

  • CAP 5771: Introduction to Data Science (3 credits)
  • EEE 5354L: Semiconductor Device Fabrication Laboratory (3 credits)
  • EEE 5776: Applied Machine Learning (3 credits)
  • EEE 6778: Applied Machine Learning II (3 credits)
  • EGN 5215: Machine Learning Applications in Civil Engineering (3 credits)
  • EGN 5216: Machine Learning for Artificial Intelligence Systems (3 credits)
  • EGN 5442: Programming for Applied Data Science (3 credits)
  • EGN 5447: Mathematical Foundations for Data Science for Engineers I (3 credits)
  • EGN 6216: Artificial Intelligence Systems (3 credits)
  • EGN 6217: Applied Deep Learning (3 credits)
  • EGN 6446: Mathematical Foundations for Applied Data Science (3 credits)
  • EGN 6640: Entrepreneurship for Engineers (3 credits)
  • EGN 6642: Engineering Innovation (3 credits)
  • EGN 6937: Engineering Fellowship Preparation (0-1 credit)
  • EGN 6951: Integrated Product and Process Design G1 (3 credits)
  • EGS 6039: Engineering Leadership (3 credits)
  • EGS 6101: Divergent Thinking (3 credits)
  • EGS 6216: AI Ethics for Technology Leaders (3 credits)
  • EGS 6512: Managing Engineering with Integrity (3 credits)
  • EGS 6626: Fundamentals of Engineering Project Management (3 credits)
  • EGS 6628: Advanced Practices in Engineering Project Management (3 credits)
  • EGS 6629: Agile Project Management for Engineers and Scientists (3 credits)
  • EGS 6681: Advanced Engineering Leadership (3 credits)
  • ESI 6900: Principles of Engineering Practice (1-4 credits)

Student Learning Outcomes

SLO 1: Knowledge

To analyze, design, implement, and evaluate Data Science systems solution to meet a given set of system requirements.


SLO 2: Skills

To recognize professional responsibilities and make informed decisions when developing Data Science systems based on legal, ethical, and policy principles.


SLO 3: Professional Behavior

To function effectively as a member of a team engaged to develop a Data Science systems solution.


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