The global engineering workforce needs to have the ability to analyze ‘big’ data sets critically to understand how and when to apply data sets in various settings and also understand how data can be interpreted to reveal a deeper contextual understanding about risks involved from multiple perspectives. In order to be able to provide aspiring engineering professionals with the complex skills necessary to use a variety of data sets, undergraduates require exposure to fundamental skills, challenging problems based in real world settings, and exposure to undergraduates with different disciplinary perspectives. This paper will provide an overview of a university-wide effort to facilitate development of the fundamental data analytics skills through transdisciplinary workplace relevant learning opportunities that bring engineering students together with the sciences, social sciences, humanities, and arts. Discussion will focus on the transdisciplinary faculty design effort and how industry professionals were involved in the construction, implementation, and support of the effort. Discussion will also focus on the curriculum offered through the Data and Decisions Minor as well as the opportunity to earn a Digital Tech Credential that is co-approved by a consortium of employers and institutions in the Capital Collaborative of Leaders in Academia and Business (CoLAB).  The information presented in this paper provides an opportunity for universities and industry professionals to consider creative partnerships that can better prepare future leaders of the engineering workforce. Engineering educators and future employers can use the findings presented in this paper to consider how to implement similar approaches at other institutions.

Keywords
Data analytics, complex problem solving, transdisciplinary