Presenters: Tracy Teal, Kari L. Jordan, and SherAaron Hurt, The Carpentries

AbstractTraining for data skills is more critical now than ever before. In the past decade, we've seen the creation of certification and graduate programs for data science, as well as a plethora of interactive, self-paced online learning platforms. Today's learners are often learning on the job and need the flexibility of short, or self-paced learning experiences. Research results, however, stress the importance of guided instruction and learner-instructor interaction. We've taken a distinctive approach to this problem, combining the power of guided instruction with the flexibility of short, focused learning experiences. Two-day, interactive, hands-on coding workshops train researchers to work with data, and have impacted over 27,500 researchers, ranging from biologists to physicists to engineers and economists. Researchers have benefited from evidence-based teaching approaches to learning data organization (spreadsheets), cleaning (OpenRefine), management (SQL), analysis and visualization (R and Python). This talk focuses on implications and growth opportunities for incorporating data science curriculum at the university level, from the perspective of The Carpentries. We explore tips and best-practices in data science curriculum development including assessment strategies, accessibility, and equity and inclusion.