Additional Pathways for Texas A&M Undergraduates
QuickAdmit Pathway to MSDS
The MSDS QuickAdmit offers an expedited application review for current Texas A&M seniors and recent graduates (within the last two years) seeking entry into the CSCE or ECEN tracks of the MS in Data Science.
Eligibility:
Eligibility is restricted to students at Texas A&M’s College Station campus and its branch campuses (Galveston, McAllen, Qatar) and to programs in the College of Engineering or College of Arts and Sciences.
Applicants must have a minimum 3.0 GPA. The review is based on the undergraduate transcript in Howdy.
NOTE: The MATH and STAT tracks of the MSDS do not offer QuickAdmit; students applying to those tracks must use the regular application.
Application Deadlines & Fees
For current and former Texas A&M University students applying through the “Quick Admit” process, the Priority Deadline is March 1, 2026 (11:59 PM EST). Applications submitted by this date receive full consideration.
- International applicants (non-U.S. citizens or permanent residents) must apply by March 1.
- Domestic applications (US Citizens and Permanent Residents) submitted after March 1 are reviewed on a rolling basis until August 1 (2026) or until the program is full, whichever comes first.
Application Fee
Applicants will be charged the full GraduateCAS application fee, which will be refunded to applicants who remain enrolled in the program beyond the census date of the first semester.
Students eligible for QuickAdmit who have already applied through the regular pathway should not submit a QuickAdmit application. However, the fee they were charged for their application to regular admission be refunded if they remain enrolled in the program beyond the census date of their first semester
International Student Application Requirements
Proof of English proficiency can be demonstrated by completing all years of a Bachelor’s degree or higher at a United States-accredited university. Other proof of English proficiency and test requirements can be found on the Office of Admissions website here: https://admissions.tamu.edu/international.graduate
Automatic Admission within QuickAdmit
Applicants with a GPA of 3.25 or higher from CSCE or ECEN undergraduate degree programs who apply to the allowed MSDS departmental tracks (detailed below) will be automatically admitted into the program, provided sufficient capacity remains.
Undergraduate Major Allowed MSDS tracks for Auto-Admit Electrical Engineering ECEN Computer Engineering ECEN or CSCE Computer Science or Computing CSCE Data Engineering ECEN
All other Quick Admit applicants will undergo review:
- Applicants with a GPA below 3.25
- Applicants from other majors in the eligible Colleges
- Applicants from the majors listed above, but not applying to the allowed MSDS tracks
Want to Apply Now?
Learn how to apply for QuickAdmit and other admission requirements by visiting the Admissions & FAQ page.
College of Engineering Fast Track
Undergraduate students in the College of Engineering Fast Track program can get a head start on their MS in Data Science degree while still pursuing their bachelor’s.
How It Works:
- Take graduate-level courses in your final year in your bachelor’s
- Earn graduate credit while fulfilling undergraduate requirements through credit-by-exam
- Earn up to 9 graduate-level credit hours (three elective courses).
- Apply these credits to any MS in Data Science track after admission, subject to the Curricular Requirement to take at least three electives in the track department.
- Finish your MSDS degree in just two semesters—a full academic year.
Eligible Electives:
Currently only the Computer Science and Engineering (CSCE) department offers electives in the MS Data Science that are eligible for Fast Track. These can be taken by undergraduates in the Computer Science, Computing and Computer Engineering programs.
To learn more about requirements and how to enroll, please see the CSCE Fast Track Program details.
| MSDS Electives | Paired Course |
|---|---|
| CSCE 608 Database Systems | CSCE 310 Database Systems |
| CSCE 625 Artificial Intelligence | CSCE 420 Artificial Intelligence |
| CSCE 633 Machine Learning | CSCE 421 Machine Learning |
| CSCE 670 Information Storage and Retrieval | CSCE 470 Information Storage and Retrieval |
| CSCE 671 Computer-Human Interaction | CSCE 436 Computer-Human Interaction |
| CSCE 679 / VIZA 676 Data Visualization | CSCE 447 / VIST 476 Data Visualization |
| CSCE 704 / CYBR 604 Data Analytics for Cybersecurity | CSCE 439 Data Analytics for Cybersecurity |
| CSCE 735 Parallel Computing | CSCE 435 Parallel Computing |
Other Departments:
The Electrical and Computer Engineering (ECE) department does not offer any MS Data Science electives in the College of Engineering Fast Track. The Mathematics (MATH) and Statistics (STAT) departments do not offer MS Data Science electives for dual credit in the program.
Undergraduate Majors at Texas A&M
We encourage applicants to play to their strengths by applying to the track that most closely aligns with the disciplinary background of their bachelor’s degree. Because admitted students are required to take three or more electives from their track department, admissions staff assess applicants’ preparation to succeed in those electives.
This is not a comprehensive list. Students from other degree programs with a solid foundation in computing, mathematics, statistics, and other core data science skills might be competitive for the MSDS program. See the Admissions & FAQ section for more information.
MSDS Track Departments
BS in Computer Engineering (CSCE)
BS in Computer Engineering (ECEN)
Each of the MSDS Track departments offers an undergraduate degree that provides a strong foundation in mathematics, statistics, programming, and data-centric computing. We encourage undergraduate students in these degree programs to apply to their corresponding MSDS track.
Bachelor of Business Administration in Management Information Systems
The Management Information Systems program offers training in key technical skills, including database management, data warehousing, analytics, probability and statistics, and programming, to meet the challenges of the MSDS curriculum.
Bachelor of Science in Data Engineering
A Bachelor’s in Data Engineering from Texas A&M’s Department of Industrial & Systems Engineering builds fluency in acquiring, cleaning, integrating, and storing data; performing mining and manipulation; and communicating results through visualization; all skills that are essential for graduate-level work in analytics and machine learning. By training students to turn raw data into reliable, contextually relevant information using quantitative and computational tools, the program aligns directly with MSDS expectations for data literacy, coding proficiency, and methodological rigor.
The Department of Industrial & Systems Engineering also offers:
- Data Engineering Undergraduate Certificate to equip students with fundamental and advanced technical skills for data-driven decision-making.
- Data Center Operations Engineering Certificate for running the operations, designing the infrastructure, and managing the resources in large-scale data centers.
Bachelor of Science in Industrial Engineering
Undergraduate students in the Industrial Engineering major learn key data science skills, including stochastic modeling, quality engineering, operations research, optimization, simulation, and foundational programming.
Bachelor of Science in Petroleum Engineering
An undergraduate degree in Petroleum Engineering from the Harold Vance Department of Petroleum Engineering offers a rigorous grounding in mathematics, physics, and computing, an emphasis on designing experiments, and practice analyzing complex subsurface and sensor datasets, which build the data literacy and problem-solving skills needed for an MSDS student.
TAMIDS also supports the Undergraduate Certificate in Data Analytics for the Petroleum Industry. This interdisciplinary certificate trains undergraduate students at Texas A&M University to become more competitive in the petroleum industry job market. Students will receive education in concepts, computation, and case-based learning to prepare them for careers in data analytics and machine learning.



