All Items 1 Collection 1 The Octagon 1 Contributor 11 Baumer, Benjamin (Department of Mathematics and Statistics) 1 Hall-Holt, Olaf (Department of Mathematics, Statistics, and Computer Science) 1 Hardin, Johanna (Mathematics Department, Pomona College) 1 Hoerl, Roger (Department of Mathematics, Union College) 1 Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College) 1 Murrell, Paul (Department of Statistics) 1 Nolan, Deborah (Department of Statistics, University of California, Berkeley) 1 Peng, Roger D. (Department of Biostatistics) 1 Roback, Paul (Department of Mathematics, Statistics, and Computer Science) 1 Temple Lang, Duncan (Department of Statistics) 1 Ward, Mark D. (Department of Statistics) 1 show more 6 show fewer Topic 1 Statistics--Study and Teaching 1 Part Of 1 The Amherst College Octagon 1 Genre 1 Articles 1 Subject 1 Statistics--Study and Teaching 1 Data science in statistics curricula: Preparing students to "think with data" Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College) A growing number of students are completing undergraduate degrees in statistics and entering the work force as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this paper is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science. Data science in statistics curricula: Preparing students to "think with data"