Master of Science Data Science and Analytics with a Concentration in Full Stack (MS)
This program will provide students with a foundation to use state-of-the-art programming tools and software packages, and includes the flexibility to explore the application of tools and methods across disciplines. Students will learn how to use data for identifying trends and patterns, solving problems, communicating results, and recommending optimal solutions.
Coursework for the full stack concentration provides the flexibility for students to take courses on a wide variety of Data Science topics, and includes electives from a variety of subject areas. Graduates will be able to enter data science, analytical, and statistical fields. This program is available on-campus and online.
Full Stack
In this concentration, students will prepare to enter rapidly emerging fields related to data science and analytics. The coursework addresses relevant data analytics topics such as text analytics, visualization, algorithms and data structures, and information retrieval. Students will learn computational data analysis, data visualization, and natural language processing. Students will select four courses in consultation with the faculty advisor.
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.
Students with previously completed work at a regionally-accredited institution may submit a request for a maximum of 12 elective graduate credit hours to be transferred into the program. If approved by the admission committee, it will be added to the transcript.
Curriculum Requirements
The program requires 30 credit hours. A capstone project is required.
Data Science & Analytics Core
| Code | Title | Credit Hours |
|---|---|---|
| Core Requirements | ||
| DASC/CS 620 | Introduction to Data Science and Analytics | 3 |
| CS 624 | Data Analytics and Big Data | 3 |
| CS 625 | Data Visualization | 3 |
| STAT 603 | Probability Models for Data Science and Analytics | 3 |
| DASC 605 | Advanced Statistical Concepts in Data Science | 3 |
| Total Credit Hours for Concentration | 12 | |
| Capstone Course | 3 | |
| Total Credit Hours | 30 | |
Full Stack Concentration
| Code | Title | Credit Hours |
|---|---|---|
| Select four of the following: | 12 | |
| Programming for Data Science | ||
| Deep Learning Fundamentals and Applications | ||
| Data-Driven Computational Imaging | ||
| Fundamentals of Interpretable Machine Learning and Explainable AI | ||
| AI for Health Sciences | ||
| Generative AI | ||
| Data Visualization and Exploration | ||
| Advanced Business Analytics/Big Data Applications | ||
| Introduction to Machine Learning | ||
or ECE 607 | Machine Learning I | |
| Web Science | ||
| Database Concepts | ||
or IT 650 | Database Management Systems | |
| Data Analytics for Cybersecurity | ||
| Human Computer Interaction | ||
| Natural Language Processing | ||
| Advanced Digital Forensics | ||
| Advanced Cryptography | ||
| AI Security and Privacy | ||
| Computer Vision | ||
| Geospatial Data Analysis | ||
| Spatial Statistics and Modeling | ||
| Introductory Computational Physics | ||
or PHYS 711 | Computational Physics | |
| Total Credit Hours | 12 | |