This is an exciting time to be a statistician. The contribution of the discipline of statistics (the science of learning from data) to scientific knowledge is widely recognized. But there are challenges as well as opportunities in this new world of data. In this talk, I will discuss a number of questions and big ideas with major implications for how we teach statistics and data science. All too often we teach two-sample comparisons when the true relationship depends on other factors. In a world of found data, what issues of design and confounding are needed to disentangle complex relationships? What theoretical foundations are needed for statisticians? Can the long list of mathematical prerequisites for this course be reconsidered? Statistics is increasingly a 'team sport.' How do we teach students to work effectively in groups and communicate their results? In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. How do we integrate these precursors to data science into our curricula—early and often? By fostering more multi-variable thinking, teaching about confounding, developing simulation-based problem solving, and building data-related skills, we can help to ensure that statisticians are fully engaged in data science and the analysis of the abundance of data now available to us.
