By Tori Ellison, Julie Deeke
Information
How can we encourage students to think critically and invite them to “be a part of the conversation” when it comes to the research norms surrounding data science? We’ve developed and published online content for an undergraduate second course in data science that is framed upon building a deep awareness of the myriad of different decisions that can be made when completing a beginning-to-end data science analysis. Through engaging real-world examples and datasets, we explore how these different decisions can lead to different answers to the same research question. With this in mind, we frame and embed our course with discussions and exercises which encourage students to think critically about the research norms that surround how we answer questions with data. We introduce the first section of this text as an example of how educators might frame a data science or statistics course in such a way. We introduce targeted questions that the students answer in projects and peer-evaluation surveys that encourage these critical thinking skills as well as assess them. This content is free and accessible to all students and includes units on data wrangling, linear regression, feature selection including cross-validation, logistic regression, simulating sampling distributions, and statistical inference. We have taught approximately 600 undergraduate students at a large, public institution over the last year using this website as our course resource.