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.

September 23, 2024