Master of Science in Applied Data Science
About This Program
The Master of Science in Applied Data Science with a concentration in statistics will train students in statistical methodologies, data science, big data analytics, and machine learning. A primary objective of this track is to prepare students for work in industry through an emphasis on cutting-edge methods commonly used to solve real-life problems. The curriculum will focus on applied statistics and data science (contrasted with theoretical statistics) and is designed for hands-on experience through in-class learning and opportunities for research project internships in different settings. Students will increase their breadth of knowledge of statistical research, machine learning, deep learning and big data analytics, and will be proficient in various programming languages at a suitable level for data analysis. The program includes a 3-semester credit hour (SCH) capstone project or professional internship that will allow students to experience the entire process of analyzing a given problem, starting with problem conception all the way to product delivery. The program is designed for students with a wide range of backgrounds, including STEM degrees and those with non-technical backgrounds, such as business majors. 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 design experiments.
- Upon completion, students will understand the computational foundations of big data analytics, apply machine-learning and deep learning to a wide range of learning tasks, and work effectively across diverse disciplines.
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 |
| General Applied Data Science Specialization | ||
| ASDS 5304 | APPLIED MULTIVARIATE STATISTICAL ANALYSIS | 3 |
| ASDS 5306 | APPLIED TIME SERIES ANALYSIS IN DATA ANALYTICS | 3 |
| Select one from the following: | 3 | |
| ADVANCE REGRESSION ANALYSIS | ||
| MACHINE LEARNING WITH APPLICATIONS | ||
| Select one from the following: | 3 | |
| DATA MINING WITH INFORMATION VISUALIZATION | ||
| STATISTICAL AND SCIENTIFIC COMPUTING II | ||
| Applied 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.