New advances in data science have paved the way for the development of transformational technologies that touch upon many aspects of daily lives, including health, transportation, and security, among others. Consequently, there is a critical need for today's students to gain an increased understanding of data science, building the workforce needed to continue data science innovations in service of society. The Sensing for Data Science (SensDS) project responds to this need through the design and development of an educational platform and instructional materials focused on the use of Cyber-Physical Human Systems for hands-on data science education. This interdisciplinary project takes place in urban and rural high schools in Alabama and uses gesture-driven radio frequency sensors to introduce 260 students and their teachers to data science concepts and processes. Specifically, the project will develop a web-based, interactive, machine learning platform, tailored to hybrid learning for grades 9-12, which will engage students with sensing + machine learning + control technology as they interact with virtual games and robots. The project involves activities that make visible how data moves across platforms in a system and then uses tools to make those patterns meaningful for students. SensDS follows a design-learn-play methodology for translating abstract data science concepts into a more easily understandable, tangible domain, enabling students to intuitively relate their move