Conference Agenda

Higher education curricula are changing rapidly, shifting to more ML/AI in open-source and open-data environments often requiring coding, while sacrificing essential statistical thinking skills like understanding variation in data or conducting strategic experimentation. However, the ultimate questions are: Will the next generation of professionals in Computer-Aided Process Engineering (CAPE) and other fields be prepared for a data-driven future? Will the students really learn how to learn from data, e.g. to understand and optimize a process or to drive innovation and develop a new high-quality product?

Surprisingly, while students get access to more and more data and see more analytics in action in their daily life, the skill gaps reported by JMP customers in chemical, pharma or biotech industries are increasing. In this 1-hour workshop we will discuss these gaps and recommend some remedy, exploring best practices in teaching multivariate thinking and real-world problem solving. The JMP software used in this session is freely available to students, instructors and academic researchers at jmp.com/student, and all demo content will be shared with the participants.