Statistics, Data Science and Experimental Design in Engineering Curricula

For over more than a century, statistical thinking has proven to be extremely valuable in many fields of human endeavor. The statistical analysis of observational data and the use of designed experiments are corner stones of the scientific method. They should therefore be key tools in engineers’ toolbox. Given the increasing availability of large data sets and the fact that modern products and processes are more and more complex, the need for engineers with strong statistical skills, predictive modelling capabilities and expertise in design of experiments will increase dramatically in the near future. It is the duty of engineering academics to ensure that engineering curricula prepare future engineers for the new challenges ahead, connecting statistics, data science and design of experiments with engineering applications.

In this seminar, Peter Goos presents his view on the place of statistics, data science and design of experiments in engineering curricula. More specifically, he discusses best practices, resources and enabling software teaching statistics, data science and design of experiments. Considering five different scenarios, Peter describes how a modern stats curriculum can deliver better solvers of real-world engineering problems. The seminar was in fact a recorded JMP-SEFI webinar from June 5th, 2019. SEFI is the European Society for Engineering Education, while JMP is a division of the SAS Institute.