Curriculum
This is a 30-hour program that consists of ten (10) courses. Students from all tracks will take the same set of four (4) core courses, which develop students with a strong foundation of data science in mathematics, statistics, computing skills, and machine learning.
Students will choose six (6) elective courses from the list of courses offered by five different departments.
Students typically take all four (4) core courses in the first semester and then take three (3) elective courses in each of the two subsequent semesters. Students are required to take three (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).
Courses Offered for Current Academic Year
MSDS Core Courses
These core courses are required for all MSDS students:
- MATH 677 Mathematical Foundations of Data Science (3 Lecture Credits Hours)—Linear systems; least squares problems; eigenvalue decomposition; singular value decomposition; Perron–Frobenius theory; dynamic programming; convex optimization; gradient descent; linear programming; semidefinite programming; compressive sensing.
- STAT 650 Statistical Foundation of Data Science (3 Lecture Credits Hours)—Introduction to both probability and statistics with emphasis on applications in data science; topics include basic probability concepts, sample space, conditional probability, random variables, as well as statistical inference.
- STAT 624 Databases and Computational Tools Used in Big Data (3 Lecture Credits Hours)—Survey of common tools used by statisticians for high-performance computing and big data type problems; shell scripting; HPC clusters; code optimization and vectorization; parallelizing applications using numerical libraries; open MP, MPI, and parallel R; data management and revision control using Git; exploration of SQL, survey NOSQL databases; introduction to Python.
- STAT 639/ECEN 758/CSCE 676 Data Mining and Analysis (3 Lecture Credits Hours)—A broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, clustering, and classification; applications to scientific and online data.
Computer Science and Engineering
Schedule subject to change. Always check Howdy for the most up-to-date information.
CSCE course descriptions are available online at TAMU’s Graduate and Professional Catalog.
| Course Name | Semester Offered (Number Year/Semester) |
|---|---|
| CSCE 608: Database Systems | Odd/Spring |
| CSCE 625: Artificial Intelligence | Every Fall and Spring |
| CSCE 633: Machine Learning | Every Fall and Spring |
| CSCE 636: Deep Learning | Every Fall and Spring |
| CSCE 638: Natural Language Processing: Foundations and Techniques | Every Spring |
| CSCE 642: Deep Reinforcement Learning | Every Fall |
| CSCE 666:Pattern Analysis | Fall 2022 |
| CSCE 670: Information Storage and Retrieval | Odd/Spring |
| CSCE 678 / ECEN 757 Distributed Systems and Cloud Computing | Spring 2022 |
| CSCE 679 / VIZA 676: Data Visualization | Every Fall |
| CSCE 704 / CYBR 604 Data Analytics for Cybersecurity | Every Fall |
| CSCE 735 Parallel Computing | Every Spring, Summer, & Fall |
Electrical and Computer Engineering
Schedule subject to change. Always check Howdy for the most up-to-date information.
ECEN course descriptions are available online at TAMU’s Graduate and Professional Catalog.
| Course Name | Semester Offered (Number Year/Semester) |
|---|---|
| ECEN 642: Digital Image Processing & Computer Vision | Every Spring |
| ECEN 689: Scientific Machine Learning | Spring and Fall |
| ECEN 644: Discrete Time Systems | |
| ECEN 649: Pattern Recognition | Even/Fall |
| ECEN 663: Data Compression with Applications to Speech and Video | |
| ECEN 732: Online Decision Making and Learning | Fall 2023 |
| ECEN 740: Machine Learning Engineering | Every Spring |
| ECEN 743: Reinforcement Learning | Every Spring |
| ECEN 748: Data Stream Algorithms and Applications | Even/Spring |
| ECEN 757 / CSCE 678: Distributed Systems & Cloud Computing | Every Spring |
| ECEN 760: Introduction to Probabilistic Graphical Models | |
| ECEN 765: Machine Learning with Networks | |
| ECEN 766: Algorithms in Structural Bioinformatics | Every Spring |
| ECEN 769: Materials Informatics | Fall 2024 |
Mathematics
Schedule subject to change. Always check Howdy for the most up-to-date information.
Math course descriptions are available online at TAMU’s Graduate and Professional Catalog.
| Course Name | Semester Offered (Number Year/Semester) |
|---|---|
| MATH 609: Numerical Analysis | Every Fall |
| MATH 613: Graph Theory | Alternating Fall Semesters |
| MATH 649: Principles of Deep Learning | Fall 2024 |
| MATH 678: Introduction to Topological Data Analysis | Alternating Spring Semesters |
| MATH 679: Mathematical Algorithms and Their Implementations | Spring and Fall |
| MATH 680: Topics in Mathematical Data Science | Spring 2025 |
Statistics
Schedule subject to change. Always check Howdy for the most up-to-date information.
Statistics course descriptions are available online at TAMU’s Graduate and Professional Catalog.
| Course Name | Semester Offered (Number Year/Semester) |
|---|---|
| STAT 608: Regression Analytics | Every Fall and Spring |
| STAT 616: Statistical Aspects of Machine Learning I | Every Fall |
| STAT 618: Statistical Aspects of Machine Learning II | Spring 2013 |
| STAT 626: Methods in Time Series Analysis | Summer |
| STAT 636: Applied Multivariate Analysis and Statistical Learning | Every Fall |
| STAT 638: Applied Bayesian Analysis | Every Fall |
| STAT 645: Applied Biostatistics and Data Analysis | Every Fall |
| STAT 647: Applied Spatial Statistics | Every Fall |
| STAT 654: Statistical Computing with R and Python | Every Spring |
| STAT 656: Applied Analytics | Every Fall and Spring |
| STAT 659: Applied Categorical Data Analysis | Every Fall |
Industrial and Systems Engineering
Schedule subject to change. Always check Howdy for the most up-to-date information.
ISEN course descriptions are available online at TAMU’s Graduate and Professional Catalog.
Course Name | Semester Offered (Number Year/Semester) |
|---|---|
| ISEN 613: Engineering Data Analysis | |
| ISEN 6189: Analysis & Prediction |