Data science drives innovation across industries, from healthcare and agriculture to finance and transportation, enabling solutions to complex global challenges. Providing learning opportunities in data science, artificial intelligence, and machine learning (DS/AI/ML) is essential to prepare the workforce for the rapidly evolving technological landscape.

Collaborative Course Development

TAMIDS works with Texas A&M faculty, departments and colleges to develop new course offerings in data science, empowering students and professionals with critical skills such as data analysis, predictive modeling, algorithmic thinking, ethical and responsible AI use, and more.

Some course modules are also adapted for non-credit-bearing training and webinars.

Please contact Drew Casey, if you are interested in working with TAMIDS to develop data science courses or content for your degree program.

Course Development Grant Program

Since 2020, TAMIDS has held a competitive grant program to provide faculty support to develop new courses in Data Science (including Artificial Intelligence and Machine Learning) or to revamp existing courses to include Data Science components.

New award cycles will be announced each Spring.

TAMIDS, in association with the Texas A&M Center for Teaching Excellence (CTE), solicits proposals to support the development of for-credit courses entailing at least three SCH for graduate and undergraduate Texas A&M students, training programs for Texas A&M communities, or external educational outreach that entails a similar participant effort.

Grant Guidelines

The 2024 Cycle Deadline has passed. 2024 Request for Proposals

Possible proposed deliverables include but are not limited to, class notes, videos, labs, homework, and exam problem sets. The funds are intended to support time for the proposer to design and develop the course curriculum and deliverables and can also support associated faculty, researchers, or students engaged in the proposed development activities. The funding does not support the delivery of the proposed program when completed.

Since course development strengthens translation from related research activities, TAMIDS particularly encourages submissions related to one or more of the following:

Rapidly growing and high-impact areas in AI, such as Generative AI and Large Language Models.
Priority areas identified in national-level reports and initiatives, such as the US Presidential Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
Major strategic initiatives within Texas A&M involving opportunities for Data Science, Machine Learning, and AI.
Broadening knowledge of Data Science, Machine Learning, and AI in new Texas A&M communities.

Support

TAMIDS provides up to $15,000 in unrestricted funds deposited in the lead proposer’s research account for accepted proposals. The funds will be deposited in two installments, the first $10,000 at the start of the project, and the remaining $5,000 when the proposed deliverables are completed and any new course submitted to the Curricular Approval Request System (CARS), by December 31, the year following the grant award. TAMIDS has developed resources for credit-bearing courses and non-credit-bearing training in Data Science and can assist awardees who wish to adapt or utilize these resources within their courses. Details concerning these resources are available at https://tamids.tamu.edu.

Eligibility

Individual and team proposals are allowed. The lead proposer must be a full-time Tenured / Tenure Track or Instructional Track faculty of any level with a primary affiliation in Texas A&M University (TAMU), including associated campuses, Texas A&M Engineering Experiment Station (TEES), Texas A&M Engineering Extension Services (TEEX), Texas A&M AgriLife Research, Texas A&M AgriLife Extension, or Texas A&M Transportation Institute (TTI). Collaboration outside these organizations is permitted, but funds will only be available to personnel within these organizations.

Limitations: Each person may be a proposer (lead or otherwise) for only one proposal per review cycle.

Information on Previous Awards

2021 | 2022 | 2023 | 2024

List of Courses Developed and Supported

ECEN 725 / CSCE 725 / STAT 683: Data Science Capstone

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.

STAT 483: Interdisciplinary Data Analytics Practicum

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.

ECEN 360 / CSCE 305 / STAT 315: Computational Data Science

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.

ICPE 689: Data Science Fundamentals for Energy

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.

MATH 679: Mathematical Algorithms and their Implementations