For further information contact: Christi Retzer, Program Coordinator II- email@example.com
*We are not currently offering a Spring intake*
The Master of Science in Data Science degree is an on-campus interdisciplinary program offered by the Departments of Computer Science and Engineering, Electrical and Computer Engineering, Mathematics, and Statistics within the University’s Colleges of Engineering and Science, and administered jointly with the Texas A&M Institute of Data Science.
Each of the four academic departments offers a track in the program. Students will be admitted to an individual track which they will maintain for the duration of their study. The multidisciplinary curriculum (see below) provides students with a solid foundation in mathematics, statistics, computer science, and machine learning through core courses, after which students are able to chose from electives courses provided by the different participating departments.
The MS in Data Science program prepares a student for a variety of career options associated with data science; including consulting agencies, financial services firms, government agencies, healthcare and pharmaceutical companies, marketing services, private commercial corporations, and technology companies.
Admissions & Application
Prospective students apply online through the Texas A&M University GraduateCAS. Fall 2023 application will open September 15th- March 1st. Late applications will not be accepted. https://texasam2023.liaisoncas.com/. *We are not currently offering a Spring intake*
Create an Account to get started at the GraduateCAS. Please select “Fall 2023 MS in Data Science” from the Add Program list. Complete all 4 quadrants, Personal Information, Academic History, Supporting Information and Program Materials, as instructed below.
While we are sensitive to the expenses incurred during higher education pursuit, we do not award application fee waivers. These fees are used to support the university and uphold our exceptional standards of education.
*Carefully review all information on the application before submitting. Certain errors can result in your application being withdrawn*
*Offer letters will be sent out as the committee makes their decisions. This communication will be sent to the email address you provided on your application. Please note, depending on the number of applications received the review process may take some time to complete. We appreciate your patience and understanding.
Eligibility Requirements: Applicants will need to have certain course work prior to enrolling in the program:
- Math: calculus and linear algebra
- Statistics: college level introduction to statistics
- Some programming experience in at least one of languages: R, Python, C++.
Applicants with a bachelor’s degree in mathematics, statistics, computer science, electrical engineering, industrial engineering or similar fields should have the sufficient background.
- Statement of Purpose: Applicants should submit a statement of purpose to describe the reasons for pursuing graduate study, academic and professional interests and goals, and experiences preparing for graduate study. Submit it in the Program Materials quadrant of GraduateCAS under Documents.
- CV/Resume: The CV/resume should outline the work history, educational background, relevant publications and patents, and research experience, and also include a section about the relevant courses taken and associated grades. Submit it in the Supporting Information quadrant of GraduateCAS under Documents.
- Transcript: Submit a non-official transcript in the Academic History quadrant of GraduateCAS. Official transcripts are required only if you are admitted and intend to enroll.
- 3 Recommendation Letters: Provide the contact information of your referees in the Program Materials quadrant of GraduateCAS under Recommendations. An email request will automatically be sent to each referee on your behalf.
- GRE (optional): You can self-report GRE scores in the Academic History quadrant of GraduateCAS under Standardized Tests. The GRE requirement is waived for all applicants for Fall 2023 admissions.
- TOEFL and other language requirements: International applicants must have a satisfactory score on the TOEFL or IELTS exams. For the English language proficiency requirement by the university, see International Graduate – Admissions (tamu.edu). You can self-report standardized test scores or report tests planned to take in the Academic History quadrant of GraduateCAS under Standardized Tests. Official scores must be sent directly from the testing service, institution code for Texas A&M University is 6003.
- Other supporting documents (optional): If you have publications or other documents that may show your qualification to the program, you may submit them as additional materials in the Program Materials quadrant of GraduateCAS under Documents.
- Application fee: The application fee is nonrefundable and must be paid at the time of submission.
Tuition and Fees
The chart below outlines the estimated cost of attendance. This does not include living expenses. Detailed information regarding tuition & fees at Texas A&M University is available from Student Business Service.
|In-State||Out of State|
|Tuition and fees||$13,878||$30,275|
|Program fee total||$12,400||$12,400|
This is a 30-hour program which will consist of a student completing 10 courses. Students from all tracks will take the same set of 4 core courses, which develop students with a strong foundation of data science in mathematics, statistics, computing skills, and machine learning.
Students will choose 6 elective courses from the list of 30 courses offered by five different departments.
Students typically take 4 core courses in the first semester, and then take 3 elective courses in each of the two subsequent semesters. Students are required to take 3 elective courses from the department of the track into which they are admitted, and can take the remainder of their elective courses from any of the departments listed below (including the track into which they were admitted if they wish).
- MATH 677 Mathematical Foundations for Data Science
- STAT 650 Statistical Foundations for Data Science
- STAT 624 Databases and Computational Tools Used in Big Data
- ECEN 758 / STAT 639 / CSCE 676 Data Mining and Analysis
Elective courses offered by Computer Science and Engineering:
- CSCE 608 Database Systems
- CSCE 625 Artificial Intelligence
- CSCE 633 Machine Learning
- CSCE 636 Deep Learning
- CSCE 638 Natural Language Processing: Foundations and Techniques
- CSCE 666 Pattern Analysis
- CSCE 670 Information Storage and Retrieval
- CSCE 671 Computer-Human Interaction
- CSCE 679 Data Visualization
- CSCE 735 Parallel Computing
Elective courses offered by Department of Electrical and Computer Engineering
- ECEN 642 Digital Image Processing & Computer Vision
- ECEN 644 Discretetime Systems
- ECEN 649 Pattern Recognition
- ECEN 663 Data Compression With Applications to Speech and Video
- ECEN 689 Online Decision Making and Learning
- ECEN 740 Machine Learning Engineering
- ECEN 743 Reinforcement Learning
- ECEN 748 Data Stream Algorithms and Applications
- ECEN 760 Introduction to Probabilistic Graphical Models
- ECEN 765 Machine Learning with Networks
- ECEN 766 Algorithms in Structural Bioinformatics
- ECEN 769 Materials Informatics
- ECEN 725 / CSCE 725 / STAT 683 Data Science Capstone
Elective courses offered by Department of Mathematics:
- MATH 609 Numerical Analysis
- MATH 613 Graph Theory
- MATH 664 Topics in Mathematical Data Science
- MATH 678 Introduction to Topological Data Analysis
- MATH 679 Mathematical Algorithms and Their Implementations
- MATH 680 Compressive Sensing
- MATH 689 Special Topics in Deep Learning: Theory and Application
Elective courses offered by Department of Statistics:
- STAT 608 Regression Analytics
- STAT 616 Statistical Aspects of Machine Learning I
- STAT 618 Statistical Aspects of Machine Learning II
- STAT 626 Methods in Time Series Analysis
- STAT 636 Applied Multivariate Analysis and Statistical Learning
- STAT 638 Applied Bayesian Analysis
- STAT 645 Applied Biostatistics and Data Analysis
- STAT 646 Statistical Bioinformatics
- STAT 647 Applied Spatial Statistics
- STAT 654 Statistical Computing with R and Python
- STAT 656 Applied Analytics
- STAT 659 Applied Categorical Data Analysis
Elective courses offered by Department of Industrial and System Engineering:
- ISEN 613 Engineering Data Analysis
- ISEN 619 Analysis & Prediction
Questions and Answers
Q. Is the MS Data Science a STEM program?
A. Yes. It is STEM eligible.
Q. Does the program accept 3 year Bachelor’s degrees from international universities?
A. You can check to see if specific international BS degrees are be considered equivalent to a 4 year BS degree by consulting the list of International Students – Admissions (tamu.edu) provided by the The Office of Admissions. If you do not see your country listed, you will need to contact the university Admissions Office directly at firstname.lastname@example.org.
Q. Are there funding opportunities available to international students?
A. Yes. Funding opportunities may be found on the Graduate and Professional Studies webpage https://grad.tamu.edu/
Q. Is there a word limit on the statement of purpose?
A. No. There is not a minimum or maximum word limit. The average SOP is 1- 1 1/2 pages.
Q. When can I expect a decision to be made regarding my application?
A. The application review process will begin close to the March 1st deadline.
Q. Can I receive an application fee waiver?
A. While we are sensitive to the expenses incurred during higher education pursuit, we do not award application fee waivers. These fees are used to support the university and uphold our exceptional standards of education.
Q. What English proficiency exams are accepted?
A. Citizens from non-English-speaking countries are required to submit proof of English proficiency to be eligible for review. International Graduate – Admissions (tamu.edu)
Contact and Further Information
Christi Retzer, Program Coordinator II, TAMIDS, email@example.com