PROGRAM INFORMATION
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. The degree will prepare engineering students to work as data scientists in industry and can also be used by engineering students working towards a Ph.D. degree in non-computing focused engineering degree programs.
applied data SCIENCE PROGRAM COURSES
Core/Required Courses:
Code | Title | Credits |
---|---|---|
CAP 5771 | Introduction to Data Science | 3 |
COT 5615 | Mathematics for Intelligent Systems | 3 |
EEE 5776 | Applied Machine Learning | 3 |
EEE 6778 | Applied Machine Learning II | 3 |
EGN 6446 | Mathematical Foundations for Applied Data Science | 3 |
EGN 5442 | Programming for Applied Data Science | 3 |
EGN 6933 | Special Topics | 1-3 |
LAW 6930 | Selected Legal Probs | 1-4 |
Example Specialization Courses
Code | Title | Credits |
---|---|---|
ABE 5038 | Recent Developments and Applications in Biosensors | 3 |
ABE 5643C | Biological Systems Modeling | 3 |
ABE 6035 | Advanced Remote Sensing: Science and Sensors | 3 |
ABE 6649C | Advanced Biological Systems Modeling | 3 |
ABE 6840 | Data Diagnostics | 3 |
BME 6522 | Biomedical Multivariate Signal Processing | 3 |
BME 6938 | Special Topics in Biomedical Engineering | 1-4 |
BME 6938 | Special Topics in Biomedical Engineering | 1-4 |
EIN 6905 | Special Problems | 1-6 |
OCP 6168 | Data Analysis Techniques for Coastal and Ocean Engineers | 3 |
TTE 6505 | Discrete Choice Analysis | 3 |
ENGINEERING EDUCATION departmental COURSES
College of Engineering Courses
Code | Title | Credits |
---|---|---|
EEE 5354L | Semiconductor Device Fabrication Laboratory | 3 |
EEE 5776 | Applied Machine Learning | 3 |
EEE 6778 | Applied Machine Learning II | 3 |
EGN 5215 | Machine Learning Applications in Civil Engineering | 3 |
EGN 5216 | Machine Learning for Artificial Intelligence Systems | 3 |
EGN 5442 | Programming for Applied Data Science | 3 |
EGN 6216 | Artificial Intelligence Systems | 3 |
EGN 6217 | Applied Deep Learning | 3 |
EGN 6446 | Mathematical Foundations for Applied Data Science | 3 |
EGN 6640 | Entrepreneurship for Engineers | 3 |
EGN 6642 | Engineering Innovation | 3 |
EGN 6913 | Engineering Graduate Research | 0-3 |
EGN 6933 | Special Topics | 1-3 |
EGN 6937 | Engineering Fellowship Preparation | 0-1 |
EGS 6012 | Research Methods in Engineering Education | 3 |
EGS 6020 | Research Design in Engineering Education | 3 |
EGS 6039 | Engineering Leadership | 3 |
EGS 6050 | Foundations in Engineering Education | 3 |
EGS 6051 | Instructional Design in Engineering Education | 3 |
EGS 6054 | Cognition, Learning, and Pedagogy in Engineering Education | 3 |
EGS 6056 | Learning and Teaching in Engineering | 1 |
EGS 6085 | Advanced Engineering Educational Technology | 3 |
EGS 6101 | Divergent Thinking | 3 |
EGS 6626 | Fundamentals of Engineering Project Management | 3 |
EGS 6628 | Advanced Practices in Engineering Project Management | 3 |
EGS 6629 | Agile Project Management for Engineers and Scientists | 3 |
EGS 6681 | Advanced Engineering Leadership | 3 |
EGS 6930 | Engineering Education Seminar | 1 |
EGS 6940 | Foundations of Research to Practice in Engineering Education | 1 |
EGS 6949 | Research to Practice Experience in Engineering Education | 1-3 |
EGS 6971 | Research for Master’s Thesis | 1-12 |
EGS 7979 | Advanced Research | 1-12 |
EGS 7980 | Research for Doctoral Dissertation | 1-12 |
ESI 6900 | Principles of Engineering Practice | 1-4 |
Applied data SCIENCE
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.