(241a) How to Make a Data Science Curriculum Embrace Engineering Domains and Vice Versa

The world’s attention on big data, data science, and machine learning has been triggered by successful applications in recommendation systems, natural language processing, computer vision, image classification, autonomous systems and processes, and social media, etc. The technological advances are transforming traditional industries, manufacturing, and business operations. Compared to the successful industrial applications, the immersion of data science education into science and engineering curricula is just at its beginning and fast developing from graduate to undergraduate programs (Hicks and Irizarry, 2018; Braatz et al., 2019). New schools, departments, minors, concentrations, and modules of data science have been established worldwide in the recent few years.

A revolutionary impact of data science on science discoveries is the addition of data analytics to the three pillars of scientific research, i.e., theory, experiment, and computing, especially where models are not well established, such as the design of new material molecules with desired properties. On the other hand, industries are seeing the next industrial revolution that unleashes values in massive data generated from operations, manufacturing, and service systems.

However, due to the diverse disciplines of engineering and the even more diverse sets of analytical methods in data science, a critical challenge is how to create a curriculum that will equip the next generation workforce with the highly needed skills in data science and specialization in an application domain. This approach of data science plus a domain (i.e., DS+X) was adopted at the City University of Hong Kong to establish the first stand-alone School of Data Science in Asia. In this talk, we will introduce the curriculum design of the School of Data Science which embraces engineering and science domains, including energy, environment, smart manufacturing, smart cities, social media, and health analytics. The relevance to chemical engineering will be focused on. Conversely, we will discuss how chemical engineering education can be enriched with data science courses, modules, and even a minor program in data science. While data science applies to many areas in chemical engineering, it seems that it would be a natural enhancement of process systems engineering. It is anticipated that data scientists with a domain specialization will be in high demand.

References

Stephanie C. Hicks and Rafael A. Irizarry (2018). A Guide to Teaching Data Science, The American Statistician, 72:4, 382-391, DOI: 10.1080/00031305.2017.1356747.

Richard Braatz, Lloyd Colegrove, Sharon Glotzer, Curtis Martin, Joe Qin (2019). Data Science Education in Chemical Engineering, Panel Discussion Chaired by Leo Chiang, AIChE Annual Meeting, November 11, 2019, Orlando, FL.