Data Science Professional Education Development

Foundational Courses: TAMIDS “Foundations in Computational Data Science” and “Data Stewardship” credentialing programs are a great starting point to build on with Professional Education offerings. These courses are offered at low/no cost to TAMU students and a small program fee for Industry learners. These programs lay the foundation with the ability for your expertise to build targeted courses in addition to these foundations.  If interested in adding a complementary course to these programs please contact Dr. Nick Duffield at

Custom Professional Education: TAMIDS believes in creatively enriching professionals in Industry by collaborating with faculty subject matter expert partners to build custom professional education programs/events.  If you are interested in creating an event for your professional audience, please contact Dr. Nick Duffield at

Professional Education Workshops in Data Science Foundations and Computational Practice

The Texas A&M Institute of Data Science (TAMIDS) workshop on Data Science Foundations and Computational Practice is a week-long intensive workshop that equips participants with diverse skills to enable their professional practice of data science.

Target Audience: organizations seeking to help their workforce develop knowledge and experience applying computational methods of data science.

Format: a sequence of ten interactive, three-hour modules that integrate methodological exposition with application to data through hands-on programming and computation. Participants will use open source tools and libraries to develop proficiency and understanding of the essential methods of current data science and state-of-the-art topics.

Delivery: the workshop may be delivered remotely in synchronous, asynchronous, mixed modes, or on-site.

Prerequisites: familiarity with Linux or similar operating-system environments, experience with Python or similar programming language, introductory experience with data analysis and statistics.

Instructors: Texas A&M faculty experts deliver the program modules, which were developed and field-tested in data science webinars, boot camps, and tutorials.

Customization: TAMIDS may work by arrangement with other organizations to develop domain-application content for customized workshop events.

Learning Outcomes

Upon the completion of the course, each participant should be able to:

  • Create and manage an open source software environment for data science projects.
  • Use open source tools to read, update, and write JSON, CSV, XML, and other structured data formats.
  • Apply NumPy and SciPy packages for numerical and statistical computation on data.
  • Gain insight into data through analysis and visualization using pandas and matplotlib open source libraries.
  • Apply common supervised and unsupervised machine learning methods; identify pitfalls such as over-fitting.
  • Design and develop non-trivial programs for data science in Python using libraries and frameworks for machine learning and distributed computation, including scikit-learn and Spark.
  • Use reinforcement learning to optimize control of artificial intelligence systems in the absence of a specific reward model.
  • Model complex and high-dimensional data by learning latent low-dimensional representations.
  • Integrate deep-learning frameworks such as TensorFlow and PyTorch into data analytics workflows.
  • Develop models and perform feature selection using automated machine learning.

Click here to view this as a PDF

For more information, please reach out to Dr. Nick Duffield at