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: https://texasam2021.liaisoncas.com/applicant-ux/#/login
Create an Account to get started at the GraduateCAS. Please select “Fall 2021 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.
Application Deadline for Fall 2021: March 31
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 2021 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 https://ogaps.tamu.edu/English-Language-Proficiency-Requirements. 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.
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
Elective courses offered by Department of Electrical and Computer Engineering
- ECEN 649 Pattern Recognition
- ECEN 760 Introduction to Probabilistic Graphical Models
- ECEN 765 Machine Learning with Networks
- 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 689 Special Topics in Deep Learning: Theory and Application
Elective courses offered by Department of Statistics:
- STAT 616 Statistical Aspects of Machine Learning I
- STAT 618 Statistical Aspects of Machine Learning II
- STAT 627 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 659 Applied Categorical Data Analysis
Elective courses offered by Department of Industrial and System Engineering:
- ISEN 613 Engineering Data Analysis
- ISEN 619 Analysis & Prediction