In the age of data science, traditional statistical methods are crucial, but they are increasingly
combined with computational tools and predictive modeling techniques. This talk highlights Duke
University's large introductory data science course, which provides students with a strong foundation in
exploratory data analysis encompassing data importing, visualization, transformation, and
summarization, as well as statistical inference and descriptive and predictive modeling techniques using
the R programming language. The course emphasizes real-world applications, ethical considerations, and
the importance of reproducibility in data analysis. By integrating classical statistical theory with modern
computational approaches, the course equips students to succeed in a data-driven world. We will share
the pedagogical strategies, challenges, and successes in preparing students for careers in data science.