Developed by Dr. Senarath Dharmasena

Students will learn theory and applications in causal inference, machine learning, and data science pertaining to modeling various complex economic systems. This course uses a high-impact, problem-based, critical-thinking approach to teaching and learning, which is supportive of generating transformative knowledge among the learning community.

Developed by Dr. Jun Wang, Associate Professor, Department of Agricultural Leadership, Education, and Communications

This course delves into the intersection of positive youth development (PYD) and artificial intelligence (AI), exploring how data science and machine learning (ML) techniques can be leveraged to support and enhance the well-being and potential of young people.

Developed by Dr. Zhihong Xu, Department of Agricultural Leadership, Education, and Communication

This new proposed course aims to empower students in the Food, Agriculture, Natural Resources, and Human (FANH) sciences with big data science knowledge and skills. The proposed course will emphasize the understanding and application of big data in FANH sciences, utilizing practical examples and case studies from datasets in this field to provide students with hands-on experience.

Developed by Robert Hardin, Associate Professor, Department of Biological and Agricultural Engineering

This course provides an overview of data science tools for analyzing agricultural and natural resources data, focusing on deep learning models and addressing challenges such as data quality, spatiotemporal nature, high dimensionality, and limited size, enabling students to evaluate data sources and implement suitable methods.

Developed by Dr. Charles Patrick and Dr. Franck Diaz-Garelli, Department of Biomedical Engineering

Exploration of applications of data analytics, machine learning, and deep learning in health sciences and biomedical data; topics include theoretical foundations, algorithms and methods of deriving valuable insights from data, predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling.

Developed by Dr. Ashrant Aryal

The main objective of this course is to facilitate the rapid learning of data science concepts and tools to Construction Science students and develop a programmatic way of thinking and problem-solving. This course is taught in Python.

Developed by Dr. Ann McNamara.

Visual representation and design of data and information; 3D visualization, infographics, data narratives, principles of visual data encoding and interaction techniques.

Developed by Dr. Jian Tao in collaboration with TEES, Texas A&M HPRC and the Texas A&M Department of Electrical & Computer Engineering.

Computational practice of data science through a sequence of interactive modules that provide an integrated hands-on approach to its methods, tools, and applications, and supporting technologies including high performance and cloud computing platforms.

Developed by Dr. Xiaoning Qian, Professor, Electrical & Computer Engineering

This course introduces artificial intelligence and machine learning (AI/ML) methods in scientific discovery. In the past decades, we have witnessed many success stories of AI/ML methods from different disciplines, such as biology, engineering, social science, etc.

Developed by Dr. Nick Duffield in collaboration with Dr. Yang Shen in Department of Electrical & Computer Engineering and with the Department of Statistics.

Application of data science methods including machine learning to research problems; team project-based training for project management, interdisciplinary collaboration and communication skills. 

Developed by Ulisses Braga-Neto, Professor, Electrical & Computer Engineering

This course introduces the foundations of Scientific Machine Learning (SciML), a rapidly developing area that brings together the fields of Machine Learning and Scientific Computation. After an introduction to the field, we review the basics of Machine Learning and classical ODE and PDE discretization methods, and then discuss automatic differentiation, PDE-constrained deep neural networks, PDE-constrained Gaussian Process, and Operator Learning, and their application in forward prediction, inverse modeling, and uncertainty quantification.

Developed by Dr. Fernando A. Luco Echeverría, Economics

Integration of fundamental economic concepts, such as the study of how economic incentives impact decision making, with skills and tools from data science that are necessary to work with the vast amounts of data that economic agents generate daily.

Developed by Dr. Seung Won Yoon, School of Educational Administration & Human Resource Development

This proposed course aims to empower our graduate and upper-level undergraduate students with the skills and knowledge to analyze major workforce planning, development, and management issues by performing predictive modeling on topics of absenteeism, turnover, retention, and many more.

Developed by Congrui Jin, Department of Engineering Technology & Industrial Distribution

This course is developed for undergraduates with zero experience in machine learning or R programming. It takes a learn-by-example approach, emphasizing the practical application of machine learning in material manufacturing rather than the complexity of the mathematics involved.

Developed by Dr. Heng Cai

GIS data modeling; introductory and advanced spatial SQL (structured query language); spatial database management system (DBMS) server setup, management and maintenance; spatial DBMS design, implementation, tuning, performance analysis and indexing; connecting spatial data services and warehouses to GIS software.

Developed by Dr. Jian Tao and Dr. Jianhua Huang in collaboration with Dr. Tiandong Wang and Dr. Huiyan Sang in the Department of Statistics.

Discussion of basic concepts and methods used in data science with an emphasis on applications in energy; topics include concepts of probability theory, probability distributions, statistical data modeling and inference, linear regression and predictive models, dimension reduction, introduction to machine learning and statistical modeling of dependent data.

Developed by Dr. Hui Liu

This course is to reinforce and extend the mathematical skills that students bring to graduate studies in the marine sciences. It will explore mathematical approaches and solutions that cut across environmental disciplines, and it will introduce quantitative techniques designed specifically to meet the needs of environmental sciences and related fields. The goal is to provide students with the tools and confidence they need to apply quantitative methods in their own research. The mathematical programming language R is used to solve problems in class, to complete homework sets, and to process and analyze online data.

Developed by Dr. Irfan Khan, Department of Marine Engineering Technology, Texas A&M – Galveston

Examination of cybersecurity fundamentals and the concept of network security; analysis of the cybersecurity conditions in the maritime domain and the countermeasures deployed against these cyberattacks by using data science tools; survey of data science programming and approaches adopted in the cybersecurity domain to eliminate malware and perform network traffic analysis; development and utilization of data analytic tools for assessing the cybersecurity risks of the maritime sector.

Developed by Dr. Jian Tao and Dr. Jianhua Huang in collaboration with Dr. Tiandong Wang and Dr. Huiyan Sang in the Department of Statistics.

Discussion of basic concepts and methods used in data science with an emphasis on applications in energy; topics include concepts of probability theory, probability distributions, statistical data modeling and inference, linear regression and predictive models, dimension reduction, introduction to machine learning and statistical modeling of dependent data. 

Developed by Dr. Simon Foucart.

Rigorous introduction to several subfields of data science; machine learning; optimal recovery; compressive sensing; optimization; neural networks.

Developed by Dr. Hongbin Wang, School of Medicine

This course is intended to provide a general introduction to foundations and select topics in BMI.

Developed by Dr. Shuang Zhang, Department of Oceanography

Selected topics in an identified area of Oceanography.

Developed by Dr. Jianhua Huang with Dr. Nick Duffield in collaboration with the Department of Statistics.

Application of data analytic methods and technologies in domain-based problems with real-world data; use of relevant machine learning platforms and open source tools; organization of project activities to meet goals; written and oral communication skills and methods for effective collaboration in teams with members drawn from varied technical disciplines.

Developed by Dr. Jesus Arroyo

This course provides an overview of recent advances in statistical and computational methodologies for the analysis of network data, including models, methods, theory, and applications.

Developed by Dr. Xinyue Ye, Department of Landscape Architecture and Urban Planning

This course will focus on theoretical and substantive themes within network and flower in the cities. Basic knowledge of network elements as well as tools for spatial network analysis and visualization will be covered in this course, followed by several case studies.

Developed by Dr. Suryansh Kumar, School of Performance, Visualization, and Fine Arts

This course offers a broad overview of how deep learning techniques are applied to reconstruct, generate, and manipulate 3D data, encompassing both theoretical concepts and practical applications, from basic data handling to advanced model architectures and their use in cutting-edge technologies.

Developed by Dr. Xin Li, College of Performance, Visualization, & Fine Arts

This course will give a comprehensive introduction to deep learning-based generative techniques and models for synthesizing and manipulating visual data. The course is aimed at graduate students in Visualization, as well as other interested students in data science, computer science, and related engineering programs.

Developed by Dr. Jian Tao, College of Performance, Visualization, and Fine Arts

Comprehensive introduction to digital twins and the technologies to make them possible; focus on tools, techniques, and interfaces of digital twins, alongside applied Internet of Things (IOT) methodologies.