Skip Navigation

Data Science Course Development

Collaborative Course Development

TAMIDS works with Texas A&M faculty, departments and colleges to develop new course offerings in Data Science. Please contact TAMIDS Director Dr. Nick Duffield if you are interested in working with TAMIDS to develop Data Science courses or content for your degree program. Some course modules are also adapted for non-credit bearing training and webinars. For 2020/2021 TAMIDS has established a competitive Data Science Course Development Grant Program to provide support to faculty.

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
  • Developed in part Dr. Matt Hielsberg and Dr. Andrea Bonito collaboration with the Department of Mathematics.
  • Mathematical theory and implementation with Python of modern algorithms; project based.