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Teaming Up to Teach Data Science

By Alla Baranovsky, Math faculty; Brian Cook, Social Studies Faculty; Cloricia (Pat) Townsend, Computer Science & Engineering Department Head

As the faculty and staff at Dana Hall were celebrating the completion of another productive academic year in June of 2022, we learned the news that the Edward E. Ford Foundation had awarded the School the grant we applied for earlier that year. The grant will support the creation of an interdisciplinary Data Science course in the Upper School. As the author of the original idea, I (Alla) remember the emotional swirl of excitement to work on this incredibly important project and the anxiety about getting all of the coordination between departments just right. 

Since the course will be co-taught by instructors from three departments (Social Studies, Mathematics, and Computer Science), we formed a team of three educators to develop it during the 2022-23 academic year. Each of us brings to this endeavor our unique perspective and experiences of working with students in our disciplines. 

I — Alla Baranovsky — am in my second full year of teaching at Dana Hall. I came to Dana after earning my PhD from Harvard University and teaching quantitative methods like Introduction to Data Science and Causal Inference in college for several years. While teaching at college, I learned that knowledge of data science is an expectation for just about every college student in just about every major. This is the case for several good reasons! First, empirical thinkers are rigorous thinkers who use evidence to answer questions. In this day and age, when misinformation competes with factual information for attention and disseminates in rapid cascades on social media, empirical thinking is an increasingly important skill to support in our students. Second, data is absolutely everywhere in our lives, which is why it is important to educate our students to become competent consumers of large quantities of data, as well as responsible owners of their personal data.

After I —Pat Townsend — joined the Dana Hall faculty in 2017, I continued to notice the growing interest in computer science among our students and their desire to use the skills that they have learned throughout their years at Dana. I am thrilled to be working with Brian and Alla on building a curriculum that allows the students to apply their programming skills to advance their study of computer science by learning R, which is a programming language for statistical computing and graphics. My earlier career of 15 years in the field of electrical engineering has taught me that statistical analysis and clear communication of results add great value to projects in the real world. Therefore, I will not only be teaching students about conducting analyses in R, but I will also assist them in constructing a Shiny page to publish their results to be visible by their teachers, parents, and even outside audiences.

As a member of the Social Studies department since 2013, I — Brian Cook — am excited to join in this collaboration. The ability to evaluate data is a key skill of the social scientist; indeed, a common refrain in my Economics classroom is “we know what we know because we can measure it.” But I am also passionate about this collaboration because it will allow the Dana Hall community to explore methods and models for building interdisciplinary learning opportunities "from the ground up." Interdisciplinary thinking will be embedded in the "DNA" of this course, and I hope that the work of this collaboration will be able to provide inspiration and guidance for further development of interdisciplinary curriculum. The central focus of my professional development in my career has been curriculum design and pedagogy, so I am grateful for the opportunity to do this deep dive in Data Science.

The three of us meet three times per week to work on the course. We are currently working on a list of learning goals by department, a course overview project, installing and training everyone to use R and RStudio, as well as completing a sample project from start to finish to give to our future students and the administration as an example. And despite the relative heterogeneity in our backgrounds and teaching experiences, we are united by a passion for data and empirical thinking, as well as collaborative approaches to teaching.