  As data have become more prevalent in academia, industry, and daily life, it
is imperative that undergraduate students are equipped with the skills needed
to analyze data in the modern environment. In recent years there has been a lot
of work innovating introductory statistics courses and developing introductory
data science courses; however, there has been less work beyond the first
course. This paper describes innovations to Regression Analysis taught at Duke
University, a course focused on application that serves a diverse undergraduate
student population of statistics and data science majors along with non-majors.
Three principles guiding the modernization of the course are presented with
details about how these principles align with the necessary skills of practice
outlined in recent statistics and data science curriculum guidelines. The paper
includes pedagogical strategies, motivated by the innovations in introductory
courses, that make it feasible to implement skills for the practice of modern
statistics and data science alongside fundamental statistical concepts. The
paper concludes with the impact of these changes, challenges, and next steps
for the course. Portions of in-class activities and assignments are included in
the paper, with full sample assignments and resources for finding data in the
supplemental materials.

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