Physics Concentration
The purpose of this concentration is to provide students with a thorough understanding of the methods and technologies to handle big data and to instill physics problem-solving skills rooted in big data solutions. It prepares them to become professionals trained in advanced data analytics, with the ability to transform large streams of multiple data sources into understandable and actionable information for the purpose of making decisions. The coursework enables the students to achieve a comprehensive list of tasks including collecting, storing, processing, and analyzing data, reporting statistics and patterns, drawing conclusions and insights, and making actionable recommendations.
Admission
The requirements for admission to the Master of Science in Data Science and Analytics are as follows:
- A baccalaureate degree in computer science, electrical and/or computer engineering, mathematics, statistics, information system & technology, or a related field from a regionally-accredited institution or an equivalent institution outside the U.S.; students holding a bachelor's degree in an unrelated field will need competency in topics related to basic statistics and computer science.
- Current scores on the Test of English as a Foreign Language (TOEFL) of at least 230 on the computer-based TOEFL or 79 on the TOEFL iBT, or IELTS 6.5 overall.
Curriculum Requirements
The program requires 30 credit hours. A capstone project or thesis is required.
Physics Concentration
Course List
| Code |
Title |
Credit Hours |
| Introduction to Data Science and Analytics | |
| Data Analytics and Big Data | |
| Data Visualization | |
| Probability Models for Data Science and Analytics | |
| Advanced Statistical Concepts in Data Science | |
| Fundamentals of Interpretable Machine Learning and Explainable AI |
| Intermediate Quantum Mechanics | |
| Quantum Mechanics I |
| Classical Mechanics | |
| Classical Electrodynamics I | |
* | |
| Data Science Capstone Project (3 credits) | |
| Thesis Research (6 credits) ** | |
| Total Credit Hours | 30 |
Elective Options
Course List
| Code |
Title |
Credit Hours |
| |
| DASC 600 | Programming for Data Science | 3 |
| PHYS 513 | Methods of Experimental Physics | 3 |
| PHYS 515 | Introduction to Nuclear Particle Physics | 3 |
| PHYS 517 | Introduction to Particle Accelerator Physics | 3 |
| PHYS 520 | Introductory Computational Physics | 3 |
| or PHYS 711 | Computational Physics |
| PHYS 595 | Special Topics in Physics | 1-3 |
| PHYS 696 | Special Topics in Accelerator Physics | 3 |
| PHYS 755 | Experimental and Computational Techniques in Accelerator Physics | 3 |
| PHYS 795 | Special Topics in Physics | 1-3 |
| PHYS 804 | Classical Electrodynamics II | 3 |
| PHYS 871 | Introduction to Quantum Field Theory I | 3 |
