About This Program

UTA’s Master of Science degree in Data Science helps meet the growing need for application-oriented engineers who can use data science tools and techniques to solve complex problems in industries such as manufacturing, logistics, healthcare and energy. This degree has both an on-campus and an online option.

The program is unique for its breadth, offering a widely encompassing set of courses that allows students with or without a programming background to develop the necessary data science skills. It aims to instill the acumen needed to draw insights from data, to make sound decisions using data, and to effectively communicate about data driven findings and decisions.

Students will use real-world problems, methods and data in instruction materials through collaboration with community partners; problem-based, experiential learning which emphasizes hands-on coding exercises; service learning in which students learn while doing for social good; and inclusive learning to broaden student participation and strengthen student retention in data science education.

The degree requires 30 course hours and can be completed in two years. After completion of a set of core courses, students may choose electives from multiple programs to match their career interests. They may also participate in a capstone experience which can be integrated with workplace projects. Elective options offer additional flexibility to craft a degree that fits his or her specific situation.

Students will come from different backgrounds, but the most important factor for their growth and success will be a keen interest in learning the powerful ways in which data can be applied in various fields.

Competencies

1. Advanced Knowledge in Data Science: Upon completion, students will demonstrate mastery of core data science principles including data management, statistical modeling, machine learning, data mining, and data visualization. Apply mathematical, statistical, and computational frameworks to extract insights from complex, structured and unstructured data.
2. Data-Driven Problem Solving: Upon completion, students will be able to identify, formulate, and solve real-world problems using data-centric approaches. Design and implement data pipelines and workflows for large-scale data processing, analysis, and decision-making.
3. Computational and Statistical Proficiency: Upon completion, students will be able to use programming languages and tools (e.g., Python, R, SQL, Spark) to acquire, clean, analyze, and visualize data. Apply statistical inference, predictive modeling, and machine learning algorithms to draw actionable conclusions from data.
4. Data Engineering and Infrastructure: Upon completion, students will be able to design, implement, and maintain scalable data systems using cloud platforms, databases, and big data technologies. Optimize data storage, retrieval, and processing strategies for efficiency and reliability.
5. Collaboration and Interdisciplinary Work: Upon completion, students will be able to work effectively in cross-functional teams, collaborating with domain experts, stakeholders, and decision-makers. Manage projects and contribute to agile data science workflows in academic, industry, or research settings.

Admissions Criteria

Admission to the MS in Data Science (MSDS) program is based on the applicant's perceived ability to do graduate work in engineering and data science as shown by the applicant’s test scores, transcripts, and other application documents.

To begin the program, an applicant must submit a completed application and fee to the UTA Graduate Admissions Office. When all application materials have been collected by Graduate Admissions, the application is forwarded to the program for evaluation. The admission decision is then communicated to Graduate Admissions, with the final decision sent via email to the applicant.

If there is a delay in receiving materials, the application may be deferred until all required materials are available. The applicant is notified of the deferral by the Graduate Admissions Office via email.

Requirements for admission to the MSDS program include:

• An undergraduate degree, preferably in engineering or mathematics, that includes one semester of calculus and experience in programming
• A 3.0 grade point average (on a 4.0 scale) on the last two years of undergraduate coursework. In particular, performance in engineering, science and mathematics is emphasized.
• Rigorous curriculum in the bachelor's degree issued by a reputable university as assessed using a thorough transcript analysis and consideration of program quality. To assess rigor, performance across all undergraduate courses will be analyzed with specific consideration given to courses overlapping with foundation areas for Data Science (Programming, and Applied Mathematics), where grades of C or better are expected.
• GRE is not required.
• International students are required to meet requirements demonstrating proficiency in English for admission described in the University Catalog.

Notes:

• If the applicant wishes to be considered for possible funding as a Graduate Teaching Assistant (GTA) or have any teaching responsibility, the applicant must meet a higher standard of proficiency in English than required for admission. Requirements for GTAs are described in the Financial Aid section of the University Catalog. An applicant who does not achieve the stated English proficiency standards may be required to take the Graduate English Skills Program (GESP) qualifying exam upon arrival at UTA to determine the need for additional English language courses after admission.
• The only documents required of all applicants for admission are a completed application and transcripts from all institutions of higher education attended. International students may also have to submit documentation of proficiency in English. The MSDS program does not require, nor will it review letters of recommendation, statements of purpose, or any other supplemental material from applicants. 

Curriculum

Course requirements may be met using coursework from outside the MSDS program, as follows:

• The requirements for DASC 5300 and DASC 5302 can be fulfilled by appropriate undergraduate computing-related and statistics-related courses, respectively. For DASC 5301, DASC 5304, DASC 5305 and DASC 5306, external courses must be at the graduate level and cover all essential topics of the corresponding core course.
• The student must meet the 30-hour degree requirement. In lieu of each core course that is to be fulfilled by an external course, the student must take an extra elective.
• External courses must be vetted by and arrangements must be approved by an MSDS program advisor.

Program Completion

Course sequence

• Students should meet the requirements of DASC 5300, DASC 5301, DASC 5302, before or in the same semester when they are enrolled in any other course for fulfilling MSDS degree requirements.
• Students should only be enrolled in DASC 5309 or equivalent course after completing all core course requirements in the MSDS program.
• Specialization courses can be taken in any order, as long as prerequisites are satisfied.

Advising Resources

Graduate students should consult a graduate advisor as needed

Location:

Engineering Research Building 6th Floor

Email:

csegradadvising@uta.edu

Phone:

N/A

Web:

Graduate Advising