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Over the past decade, data science courses have been growing more popular across university campuses. These courses often involve a mix of programming and statistics and are taught by instructors from diverse backgrounds. In our experiences launching a data science program at a large public U.S. university over the past four years, we noticed one central tension within many such courses: instructors must finely balance how much computing versus statistics to teach in the limited available time. In this experience report, we provide a detailed firsthand reflection on how we have personally balanced these two major topic areas within several offerings of a large introductory data science course that we taught and wrote an accompanying textbook for; our course has served several thousand students over the past four years. We present three case studies from our experiences to illustrate how computer science and statistics instructors approach data science differently on topics ranging from algorithmic depth to modeling to data acquisition. We then draw connections to deeper tradeoffs in data science to help guide instructors who design interdisciplinary courses. We conclude by suggesting ways that instructors can incorporate both computer science and statistics perspectives to improve data science teaching. 

Explorable explanations (a.k.a. 'explorables') enable readers to learn concepts in domains such as math, physics, and the social sciences by interacting with live visualizations. Despite their popularity, there is currently a high barrier to creating explorables since one must be adept at UI and visualization programming. To learn about these challenges, we interviewed 6 educators who were interested in explorables but lacked the skills to create them from scratch. These interviews gave us design insights to lower some of these implementation barriers. We used these insights to create a live programming system called Data Theater that enables programmers to prototype explorables by writing their simulation logic in Python and mapping Python values to visualization elements using a declarative JSON grammar. To demonstrate the capabilities of Data Theater, we used it to recreate two of Bret Victor's original physics simulation explorables and found that our approach can lower the barriers to prototyping explorables.