Master in Applied Statistics and Data Science

Program Overview

The Master of Science in Applied Statistics and Data Science (ASDS) in the Division of Data Science is designed for students with a wide range of backgrounds, including degrees in STEM fields, and those with non-technical backgrounds, such as business majors. The program trains students in statistical methodologies, data science, big data analytics, and machine learning to prepare work-ready students for statistics/data science positions in multiple disciplines and industries. 

The ASDS curriculum focuses on applied statistics and data science and is designed for hands-on experience through in-class learning and opportunities for research project internships in different settings. Students will increase their knowledge of statistical research, machine learning, and big data analytics and be proficient in various programming languages at a suitable level for data analytics.

Coursework

The total proposed length to complete the Master of Science in ASDS is 18 months (3 semesters). The program is in a cohort style to guarantee the most interactions and community-building between students and faculty. The requirements for the Master of Science in ASDS are 27 hours of graduate courses from the Department of Mathematics and a 3-hour capstone project or an internship.

All students must complete 6 required courses, 3 elective courses, and 1 capstone research project or an internship.

Required Courses (6)

ASDS 5301 Statistical Theory and Applications  

ASDS 5302 Principles of Data Science

ASDS 5303 Statistical and Scientific Computing I  

ASDS 6301 Advanced Regression Analysis 

ASDS 6302 Machine Learning with Applications

ASDS 6303 Data Mining with Information Visualization 

Elective Courses (3 out of 5)

ASDS 5304 Applied Multivariate Statistical Analysis

ASDS 5305 Deep Learning and Artificial Neural Networks  

ASDS 5306 Applied Time Series Analysis in Data Analytics  

ASDS 6304 Optimization and Big Data Analytics

ASDS 6305 Statistical and Scientific Computing II 

ASDS 6306 Internship or Capstone Research Project

Admission Requirements:

See UTA Admissions (note that during the application process, you should choose ASDSMSNT as the major code for this program).

Undergraduate preparation equivalent to a baccalaureate degree in natural, physical, or social sciences, technology, engineering, mathematics, business, or related fields

At least a 3.0 undergraduate GPA on a 4.0 scale

GRE scores are suggested but not required

Two favorable letters of recommendation from people familiar with the applicant’s academic work and/or professional work

Applicants may gain provisional admittance to the program without a Linear Algebra course but will be required to take one during the summer prior to starting the program.

Applicants who do not meet the requirements for admission may be considered after further review. We will consider other factors such as professional experience and prior success in related courses to not disadvantage qualified candidates.

Fall and Spring admissions

For regular admissions deadlines please go here. 

For further information and application forms, visit the website of the Office of Graduate Studies.