STOR Colloquium: Iain Carmichael, UNC-Chapel Hill
Title: Data science and the undergraduate curriculum
Last semester we developed and taught a new course titled “Introduction to Data Science” for the undergraduate analytics major at UNC (see https://idc9.github.io/stor390/). The core topics of the class were: programming in R (working with data, visualization, functions/loops/conditionals), data analysis (exploratory, predictive and inferential), acquiring data (APIs, web scraping), communication (e.g. literate programming, effective visualization), and some additional topics (text data and data ethics/inequality). This course differed from existing courses in a number of ways including: more code than math, focus on real data/questions, a final project, and open source course material. We present the overall goals of the class, the teaching methods, design choices and our own takeaways from the class. Drawing on our own experiences and a survey of the literature on advancing the undergraduate statistics curriculum we discuss future directions for both this course and the rest of the curriculum.
Link to presentation slides:
Refreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall