More than Methods: Preparing students for data-driven work outside the classroom

As data have become more prevalent in many fields, it is imperative that undergraduate students are equipped with the skills necessary for data-driven work in this modern environment. There has been significant innovation in introductory statistics and data science courses; however, there has not been as much focus on innovating subsequent courses. In this talk I will share innovations to an undergraduate regression analysis course, the second statistics course taken by many students from a variety of disciplines. I will discuss three principles that have guided the modernization of the course, along with how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. I will present pedagogical strategies and examples from in-class activities and assignments. The talk will conclude with a discussion about lessons learned and next steps for the course. Though the talk focuses on the undergraduate regression course, the principles and pedagogical strategies are applicable for courses throughout the undergraduate curriculum.