Training and Professional Development

CompTIA Data+ Certification

Data professionals are in high demand, and this course will prepare you to pass the CompTIA Data+ certification exam with confidence! This is a self-paced asynchronous course where you can come and go as your schedule allows. Stand out to employers and master core concepts and environments!

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 program designed to equip participants with diverse skills that support the professional practice of data science. It is intended for organizations seeking to help their workforce develop both knowledge and hands-on experience applying computational methods in data science.

The workshop consists of ten interactive, three-hour modules that integrate methodological instruction with practical application to real data through hands-on programming and computation. Participants use open-source tools and libraries to build proficiency and deepen their understanding of essential methods as well as state-of-the-art topics. The program may be delivered remotely in synchronous, asynchronous, or mixed formats, or offered on-site, depending on organizational needs.

Participants should have familiarity with Linux or similar operating system environments, experience with Python or comparable programming languages, and introductory knowledge of data analysis and statistics. The modules are delivered by Texas A&M faculty experts and were developed and field-tested through webinars, boot camps, and tutorials. TAMIDS may also collaborate with organizations by arrangement to develop domain-specific application content for customized workshop offerings.

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 formats.
• Apply NumPy and SciPy packages for numerical and statistical computation
• 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 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.

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For more information, please reach out to Dr. Nick Duffield at [email protected]

Customizable and Foundational Opportunities

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 [email protected].

Custom Professional Education: TAMIDS believes in creatively enriching professionals in the 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 [email protected].