Speaker
- Rachel Levy (North Carolina State University)
Details
In mathematics, rigor is both a practice and a value. However, the term isn't exclusive to our field. How in the growing field of data science can we define rigor? What are the implications for the classroom? In what ways are our definitions of rigor influenced by the discipline where we practice it? Can we find analogies between definitions of rigor in pure and applied arenas? In what ways can we expect rigor from developers and practitioners of data science, in the classroom as well as the workplace?
