2014/01/14 by Nicholas J. Horton, Horton, Nicholas J, Benjamin S. Baumer +3
Computer Science · Mathematics · #62-07 #Computation (stat.CO) #Computers and Society (cs.CY) #Data Analysis with R #Data Visualization and Analytics #FOS: Computer and information sciences #Other Statistics (stat.OT) #Statistics Education and Methodologies
paper · pdf · doi:10.48550/arxiv.1401.3269
openalex publication_date 2014/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Statistics students need to develop the capacity to make sense of the\nstaggering amount of information collected in our increasingly data-centered\nworld. Data science is an important part of modern statistics, but our\nintroductory and second statistics courses often neglect this fact. This paper\ndiscusses ways to provide a practical foundation for students to learn to\n"compute with data" as defined by Nolan and Temple Lang (2010), as well as\ndevelop "data habits of mind" (Finzer, 2013). We describe how introductory and\nsecond courses can integrate two key precursors to data science: the use of\nreproducible analysis tools and access to large databases. By introducing\nstudents to commonplace tools for data management, visualization, and\nreproducible analysis in data science and applying these to real-world\nscenarios, we prepare them to think statistically in the era of big data.\n