Master of Science in Applied Data Science (Mathematics Teaching)
About This Program
The Master of Science in Applied Data Science with an emphasis in Mathematics Teaching prepares students to analyze and interpret educational and learning data using modern computational and statistical tools. Through coursework in areas such as quantitative assessment, learning analytics, curriculum evaluation, educational measurement, and data-informed instructional design, students gain the knowledge necessary to apply data science techniques to challenges in mathematics education. This emphasis enables students to leverage classroom, assessment, and student-performance datasets to identify learning patterns, improve instructional strategies, evaluate curriculum effectiveness, and generate meaningful insights that support evidence-based teaching. A 3-semester credit hour (SCH) project or professional internship provides hands-on experience applying analytical and computational methods to real-world educational settings. 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 apply statistical methodologies to real-world data from diverse applications, with an emphasis on those within the field of mathematics education.
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 |
| Mathematics Teaching Specialization | ||
| Select four from the following: | 12 | |
| CONCEPTS AND TECHNIQUES IN NUMBER THEORY | ||
| CONCEPTS AND TECHNIQUES IN CALCULUS | ||
| DISCRETE MATHEMATICS FOR PROBLEM SOLVING | ||
| MODERN GEOMETRY | ||
| CONCEPTS AND TECHNIQUES IN ALGEBRA | ||
| MATHEMATICS-SPECIFIC TECHNOLOGIES | ||
| HISTORICAL APPROACH TO REAL ANALYSIS | ||
| CONCEPTS AND TECHNIQUES IN PROBLEM SOLVING | ||
| 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.