Moving Toward Big Data Using Long-Term Projects, Capstones, and Culminating Experiences

• Activity Number: 97
• Type: Invited
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Education
• Author(s): Julie Marie Legler*+ and Paul Roback
• Companies: St. Olaf College and St. Olaf College
• Keywords: undergraduate research ; data science

Abstract

Many colleges incorporate undergraduate research into their statistics curriculum either as a research course such as St. Olaf's Center for Interdisciplinary Research (CIR) or as Capstone Experiences or as Consulting Courses, for example.  All of these experiences involve elements of data science.  Building blocks acquired from the first and second courses make dealing with open-ended research possible, adding  key skills not yet mastered. Here, two examples of past projects from the CIR are  re-viewed from a data science perspective. A project investigating outpatient clinics' skimming of hospitals' patients presents students with serious data acquisition challenges. A second project compares the styles of translations of texts.   Initially the St. Olaf CIR consisted of teams of statistics students working with domain experts from other disciplines.  Data science may lead to another re-envisioning of the program where the team consists of a data wiz, algorithm jocks, statistics-savvy students, and students from a domain area.  A data science approach suggests that we need to explicitly think of these needs when approaching almost any of our undergraduate projects.