Description
The health of an IS department is partly linked to the quality and number of the employment opportunities available to graduates of that program. With the field of data science (considered here to include big data, data analytics, and business intelligence) growing quickly and significantly, providing IS graduates with a breadth and depth of data science employment opportunities is important to the IS department, the employers, and the students themselves. We present a framework to increase the recruiting efficacy for data science graduates. The framework is patterned after a similar framework that has been successfully implemented to garner local, regional, and national placements in the IS consulting field for our IS graduates. We discuss the process that the IS department, the data science employers, and the IS students should follow, as well as how these three stakeholder groups interact. We then suggest the implications for the IS department.
Recommended Citation
Firth, David; Harrington, Michael; and Triche, Jason, "A Skill Set Based Framework to Increase the Recruiting Efficacy for Data Science Graduates" (2015). AMCIS 2015 Proceedings. 5.
https://aisel.aisnet.org/amcis2015/ISEdu/GeneralPresentations/5
A Skill Set Based Framework to Increase the Recruiting Efficacy for Data Science Graduates
The health of an IS department is partly linked to the quality and number of the employment opportunities available to graduates of that program. With the field of data science (considered here to include big data, data analytics, and business intelligence) growing quickly and significantly, providing IS graduates with a breadth and depth of data science employment opportunities is important to the IS department, the employers, and the students themselves. We present a framework to increase the recruiting efficacy for data science graduates. The framework is patterned after a similar framework that has been successfully implemented to garner local, regional, and national placements in the IS consulting field for our IS graduates. We discuss the process that the IS department, the data science employers, and the IS students should follow, as well as how these three stakeholder groups interact. We then suggest the implications for the IS department.