June 14, 2015
June 14, 2015
June 17, 2015
Computing & Information Technology
26.1354.1 - 26.1354.12
San Francisco’s New Start-Up is Reengineering Engineering EducationAbstract Inertia associated with centuries of tradition has most engineering schools deliveringprograms in staid environments where the learning process is focused on the instructor (whosefirst priority is often research productivity) and students sit silently in rows, take notes and laterdo individual homework. Private and public institutions hire graduates who are often not jobready and must often invest significant resources to have new employees learn skills andattitudes that are essential for the job. A select few higher educational institutions have beensuccessful in reengineering the way that learning is imparted (e.g., Olin College in the US andAalborg University in Denmark). This paper describes an innovative way in which engineeringeducation is conceptualized and delivered at ___, a new venture-backed institution to belaunched in San Francisco in January 2015 in partnership with the University of ___. Thecurriculum and learning experiences are being designed to address the needs of the stakeholdersin the region, powered by internships in industry. The first program to be launched is a Master ofEngineering in Data Science. ___ is creating engineering education that is more collaborative, project-based, hands-on,industry relevant and diverse. The model seeks to meet gaps in traditional programs to betterserve industry needs by complementing technical training with important engineering andmachine learning competencies, including training in teamwork, communication, and hands onindustry involvement developing scalable predictive models. ___ will train a new Data Science Engineers, emphasizing both the technical backgroundnecessary to master this high demand discipline and the conceptualization ability to tacklecommon industry challenges with Data Science solutions, and the skills required to communicatethe value of these solutions. One of ___’s goals is to change how Data Science education isperceived and taught. Its curriculum model is needs/competency-based, learner-centric, project-based, and includes industry partnerships and continuous improvement. The practice-oriented 30-credit Master of Engineering in Data Science (ME in DS)prepares students for positions in high volume data management, predictive analysis, andvisualization. The program addresses the intersection of data engineering, machine learninganalytics, and generating real industry solutions. Students completing the degree will managedata-driven decision-making and use, analyze, and evaluate technologies and techniques in anenterprise setting. Graduates will be able to design innovative solutions to Data Sciencechallenges taking economic and societal interests into account. They will work in teams,communicate effectively, and manage projects and time effectively. Rather than a research-oriented thesis, the capstone experience is an applied development project conducted incollaboration with an industry sponsor. The ME in DS program is divided into three main areas: the technical core (fivefundamental topics in Data Science, the leadership core (professional and hands on experiencesin Data Science), and the focus core (elective to develop students’ Data Science interests).
Tamir, M., & Harichandran, R. S., & Morell, L. (2015, June), San Francisco’s New Start-up is Reengineering Engineering Education Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24691
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