Stephanie Hicks, Assistant Professor
Department of Biostatistics
Johns Hopkins Bloomberg School of Public Health
Making data science accessible world-wide in the Johns Hopkins Data Science Lab
Abstract
In this talk, I will introduce the Johns Hopkins Data Science Lab: who we are, what are our goals, and the types of projects we are working on to make data science accessible world-wide. Then, I will discuss projects that I have focused on related to data science education. Despite unprecedented and growing interest in data science on campuses, there are few courses and course materials that provide meaningful opportunity for students to learn about real-world challenges. Most courses provide unrealistically clean data sets that fit the assumptions of the methods in an unrealistic way. The result is that students are left unable to effectively analyze data and solve real-world challenges outside of the classroom. To address this problem, I am leveraging the idea from Nolan and Speed in 1999, who argued the solution to this problem is to teach courses through in-depth case studies derived from interesting scientific questions with nontrivial solutions that leave room for different analyses of the data. I will share a set of general principles and offer a detailed guide derived from my successful experience developing and teaching graduate-level, introductory data science courses centered entirely on case studies. Furthermore, I will present the Open Case Studies educational resource of case studies that educators can use in the classroom to teach students how to effectively derive knowledge from data derived from real-world challenges.
DATE: Wednesday, February 27, 2019
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu