The Department of Mathematics at The University of Texas at Arlington is offering a new master's degree program in applied statistics and data science that will equip students for careers in a fast-growing job field.
The Master of Science in Applied Statistics and Data Science (M.S. in ASDS) program will start in the fall 2023 semester and can be completed in three semesters (18 months). It is designed for students from a wide range of backgrounds, including degrees in STEM and non-technical fields, such as business.
"This new degree program will be a great asset for students who want to enter the rapidly growing field of data science, and it will give them added versatility by equipping them with a solid foundation in applied statistics," College of Science Dean Morteza Khaledi said. "As we did with our bachelor's degree program in data science, we are helping lead the way in educating the workforce of the next generation, which will need a high percentage of data science proficiency."
Numerous studies show the number of jobs requiring data science skills increasing at a much higher rate than almost any other field. This is due to the massive amounts of data generated by businesses and organizations and the need for skilled workers to analyze and interpret it. The U.S. Bureau of Labor Statistics predicts that statistician and data scientist jobs will experience 36% growth by 2031.
"The ability to receive advanced training in statistics helps to set the M.S. in ASDS program apart from other master's degrees in data science," said Shan Sun-Mitchell, professor of statistics. "Our program will train students in statistical methodologies, data science, big data analytics and machine learning to prepare work-ready students for statistics and data science positions in multiple disciplines and industries. The program is project-based and is designed to help students learn how to interpret and analyze data, with a minimal prerequisite of mathematical and statistical knowledge and programming languages."
The M.S. in ASDS curriculum is designed to provide hands-on experience through in-class learning and a summer internship or a capstone research project. 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.
"Integrating data science and statistics will equip students with a broader range of skills, giving them a competitive edge over graduates from programs that focus solely on one or the other," said Li Wang, associate professor of mathematics and computer science and engineering.
The idea for the program was conceived by Minerva Cordero, UTA interim vice provost for faculty affairs and professor of mathematics, and Sun-Mitchell, professor of statistics in the Department of Mathematics. They were joined in their efforts by Keaton Hamm, Pedro Maia, Suvra Pal, Wang, and Dengdeng Yu, all faculty members in the Department of Mathematics. Their collaboration led to the development and approval of the degree proposal by the Texas Higher Education Coordinating Board in the summer of 2022. The program is being launched through the joint leadership of Sun-Mitchell, program director, and Sherry Wang, director of the Center for Data Science Research and Education.
The degree program consists of 30 total hours, including six required courses, three elective courses, and one research capstone project course or a summer internship. It is presented in a cohort style to guarantee the most interactions and community-building between students and faculty.
"As we move toward a new era of big data, graduates with expertise in applied statistics and data science are being hotly pursued by high-tech industry, banks, national defense, marketing, and health care organizations," said Jianzhong Su, professor and chair of the UTA Department of Mathematics. "This unique program creates an excellent career pathway for students to attain a level of competency in applied statistics and data science and lead the next generation of the STEM workforce."
Admission requirements for the program include the following: • Undergraduate preparation equivalent to a baccalaureate degree in natural, physical, or social sciences; technology; engineering; mathematics; business; or related fields. • Completion of a linear algebra course. Applicants may gain provisional admittance to the program without it, but will be required to take the course during the summer prior to starting the program. • At least a 3.0 undergraduate GPA on a 4.0 scale. • Two favorable letters of recommendation from people familiar with the applicant's academic work and/or professional work. • GRE scores are suggested but not required.
The first cohort of M.S. in ASDS students will take three core courses during the fall 2023 semester: ASDS 5301 - Statistical Theory and Applications, ASDS 5302 - Principles of Data Science, and ASDS 5303 - Statistical and Scientific Computing I.
