What draws students to statistics? For some, the answer is mathematics, and for those a course in probability theory might be an attractive entry point. For others, their first exposure to statistics might be an applied introductory statistics course that focuses on methodology. This talk presents an alternative focus for a gateway to the discipline of statistics, the statistics major, and broadly to quantitative studies: data science. The course is intended for an audience of students with little to no computing or statistical background, and focuses on data wrangling, exploratory data analysis, data visualization, and effective communication. Unlike most traditional introductory statistics courses, the course approaches statistics from a model-based perspective and introduces simulation-based and Bayesian inference later in the course. A heavy emphasis is placed on reproducibility, version control, and collaboration. We will discuss in detail the course structure, logistics, pedagogical considerations, and student feedback as well as give examples from the case studies used in the course.