Teaching Data Science

roundtableLocation: Ballroom C+D+E
This is a roundtable in the Cyberlearning 2017 Roundtable session.

What We Learned at the Data Science Education Technology Conference

William Finzer
Data Science Education Technology Conference Photos
In February, 2017, the Cyberlearning-funded CODAP project at the Concord Consortium hosted a two-day conference for about 100 educators, curriculum developers, and software developers to explore the emerging field of data science education and think together about needs for technology to support it in classrooms from middle school through college. At this round table we’ll briefly present a few of the most interesting outcomes and findings from the conference and discuss their implications together. Here are some of the more evocative session titles from the conference: * Illustrations of Data Science Integration in Subject Matter Teaching * Data Technology Integration with Online Curricula * Innovations Needed to Support Data Intensive Curriculum Development * Using Simulations and Modeling Environments as Data Sources * Connecting Technologies * The Future of Data Intensive Learning * Community Building around Data Science Education Technology.
Project: Homepage, NSF Award #1530578 – Data Science Games – Student Immersion in Data Science Using Games for Learning in the Common Online Data Analysis Platform

An innovative model of teaching data science using competition-based learning

Huzefa Rangwala
We seek to research and develop a new competition-based paradigm for teaching data science to students from computing and non-computing backgrounds to meet the expectations of training the next-generation data scientist. Our proposed data analytics course implementation for graduate students in Computer Science and STEM disciplines proposes the following key innovations: (i) Competition-Style Learning: Use competition-based framework to introduce different data analytics related concepts. In particular this was implemented and tested in Fall 2016 and results of this attempt will be presented at this forum for feedback and refinement. (ii) Personalized: Use learning analytics to understand student learning styles and knowledge competencies towards helping students learn better and (iii) Domain-Specific: Enhance developed approach to cater to different domains (E.g., biology and bioengineering) by developing domain-specific assessments and contents.
Project: NSF Award #1447489 – BIGDATA: IA: DKA: Collaborative Research: Learning Data Analytics: Providing Actionable Insights to Increase College Student Success