By Emily Grace Bolger
Information
As a graduate student in the Computing Education Research Lab (CERL) and former Teaching Assistant, I work with extraordinary educators who think critically about preparing our data science students for their next steps. The Introductory to Data Science and Computational Modeling course in our department serves about 400 undergraduate students each semester from a variety of home disciplines. The course provides them with an opportunity to learn the basics of coding in Python as well as introductory data analysis and computational modeling skills. This “Beyond” presentation will highlight the structure of the course and the concepts we feel are essential to a data science curriculum. Additionally, I will showcase a few class assignments that help students expand their knowledge using data visualizations for learning and communicating as well as thinking critically about the importance of data ethics and biases (both algorithmic and systemic). Our course has always sought to make data science relevant to students and their home disciplines (e.g. physics of bungee jumping, savings accounts), however, we have expanded to incorporate aspects of data science that we are not thinking about enough – the impact that data and modeling can have on others (e.g. voting theory, Flint water data).