Program Highlights
- 18 months (total of 30 credits) cohort-style program
- Open to students with both STEM and non-technical backgrounds, such as business majors
- Minimal prerequisite of mathematical/statistical knowledge and programming languages
- Broaden knowledge in Applied Statistics and Data Science with hands-on experience through in-class learning and summer Internship or capstone research project
- Train and improve various programming language skills at proficiency level for data analytics
- Prepare work-ready students for statistics/data science in multiple disciplines and industries
Program Overview
The Master of Science in Applied Statistics and Data Science (ASDS) in the Department of Mathematics 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 summer internship or a capstone project course.
All students must complete 6 required courses, 3 elective courses, and 1 research capstone project course or a summer 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
Summer Internship or Research Capstone Project
ASDS 6306 Summer Internship or Capstone Research Project
Prerequisite
- Linear Algebra
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.