Towards Open-World Scenarios: Teaching the Social Side of Data Science

摘要

This article reflects on current challenges we encounter in teaching data science to graduate students. A common critique of data science classes is that examples are static and student group work is embedded in an 'artificial' and 'academic' context. We look at how we can make teaching data science classes more relevant to real-world problems. Student engagement with real problems--and not just 'real-world data sets'--has the potential to stimulate learning, exchange, and serendipity on all sides, and on different levels: noticing unexpected things in the data, developing surprising skills, finding new ways to communicate, and, lastly, in the development of new strategies for teaching, learning and practice.