Master of Science in Applied Data Science (Physics)
About This Program
The Master of Science in Applied Data Science with an emphasis in Physics prepares students to analyze and interpret large-scale physical data using modern computational tools. Through coursework in areas such as artificial intelligence in dynamic system control, astrophysics, biophysics, computational physics, experimental physics and design, instrumentation, particle physics, space physics, and statistical physics, students gain the knowledge necessary to apply data science techniques to complex questions in the physical sciences. This emphasis enables students to leverage massive datasets and catalogs generated by some of the largest data collection instruments and missions in the world (such as the high energy particle detectors in Fermilab and CERN, NASA’s astronomy and space missions, as well as “-omics” datasets in biophysical research) to uncover patterns, test hypotheses, and generate meaningful physical insights. A 3-semester credit hour (SCH) capstone project or professional internship provides hands-on experience applying analytical methods to real-world physical challenges. The program can be completed in 18 months (full-time students may complete it in 12 months).
Competencies
- Upon completion, students will be able to apply statistical methodologies to real-world data from diverse applications.
- Upon completion, students will be able to understand computational aspects of big data analytics.
- Upon completion, students will be able to work across a variety of disciplines, understanding modern physical science data collection tools, procedures, cleaning, feature engineering, and analytical approaches. They will learn to use high-performance computing and AI methods to address complex physical problems, develop and apply models to predict physical phenomena and simulate physical systems, and visualize complex datasets to identify patterns and communicate findings effectively.
Admissions Criteria
All applicants for the Applied Data Science MS must meet UT Arlington’s graduate admission requirements. The program does not consider GRE scores in evaluating candidates for admission.
To be considered for admission, applicants must demonstrate undergraduate preparation equivalent to a baccalaureate degree in natural, physical, or social sciences, technology, engineering, mathematics, business, or related fields. To apply for a specific track, applicants must demonstrate sufficient background in that concentration with a relevant bachelor’s degree.
Applicants must submit two letters of recommendation from evaluators who can assess the candidate’s potential for academic success.
Unconditional Admission
Applicants who demonstrate a GPA of 3.0 and strong support from references will be considered for unconditional admission. Applicants with a GPA of 2.7 or higher (but less than 3.0) who demonstrate relevant work experience and/or certification may be offered unconditional admission upon review.
All admitted students must complete the program’s self-paced, non-credit online review, “Math Foundation of Machine Learning,” prior to enrolling in classes.
Curriculum
| Foundations | ||
| ASDS 5301 | STATISTICAL THEORY AND APPLICATIONS | 3 |
| ASDS 5302 | PRINCIPLE OF DATA SCIENCE | 3 |
| ASDS 5303 | STATISTICAL AND SCIENTIFIC COMPUTING I | 3 |
| ASDS 6306 | INTERNSHIP/CAPSTONE RESEARCH PROJECT | 3 |
| Physics Specialization | ||
| PHYS 5319 | MATHEMATICAL METHODS IN PHYSICS III | 3 |
| Select three from the following: | 9 | |
| CHAOS AND NONLINEAR DYNAMICS | ||
| CLASSICAL MECHANICS | ||
| QUANTUM MECHANICS I | ||
| QUANTUM MECHANICS II | ||
| ELECTROMAGNETIC THEORY I | ||
| STATISTICAL MECHANICS | ||
| MATHEMATICAL METHODS IN PHYSICS I | ||
| MATHEMATICAL METHODS IN PHYSICS II | ||
| ELECTROMAGNETIC THEORY II | ||
| SOLID STATE I | ||
| INTRODUCTION TO ELEMENTARY PARTICLES I | ||
| Data Science Electives | ||
| Select 6 hours from the following: | 6 | |
| DEEP LEARNING AND ARTIFICIAL NEURAL NETWORKS | ||
| SPECIAL TOPICS | ||
| ADVANCE REGRESSION ANALYSIS | ||
| MACHINE LEARNING WITH APPLICATIONS | ||
| DATA MINING WITH INFORMATION VISUALIZATION | ||
| OPTIMIZATION AND BIG DATA ANALYTICS | ||
| STATISTICAL AND SCIENTIFIC COMPUTING II | ||
| INDEPENDENT STUDY | ||
| Total Hours | 30 | |
Advising Resources
Undergraduate and Graduate Advising
Location:
Life Science Building Room 206A and 206B
Email:
data.advising@uta.edu
Phone:
817-272-1512
Web:
Speak to an advisor in the Division of Data Science or schedule an appointment.