Designing MOOCs to Democratize Data Science Education
Getting a job in data science has historically required an expensive education, a costly computer, having connections in the field, and physically being in the place where data science jobs are. Given these hurdles, a career in data science is inaccessible for most people in the population. We set out to minimize these barriers and enable entry into data science for non-traditional learners. To do so, we have developed Chromebook Data Science (CBDS), a set of 12 free massive open online courses (MOOCs) to teach the basics of data science. These courses can be taken by anyone who can follow instructions and who has access to the Internet. We have additionally worked to develop CBDS+, a supplement to CBDS, providing free laptops, in-person tutoring, online support, job search assistance, and payment for program completion to individuals from historically underserved populations. In this talk, I’ll discuss the design of the CBDS courses, the implementation of CBDS+, and some of the hurdles we encountered along the way.
About the Speaker
Shannon Ellis is a new Assistant Teaching Professor at UC San Diego. Before joining the Cognitive Science Department at UC San Diego, she received her Ph.D. in human genetics from the Johns Hopkins School of Medicine and was a postdoctoral fellow in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Shannon is passionate about data science, ethical data analysis, and education. One priority of her work has been working to ensure that data science education is accessible to underrepresented populations and groups who typically have not had access to such materials.