Data science: Accelerating innovation and discovery in chemical engineering
A ll of science and engineering, including chemical engineering, is being transformed by new sources of data from high throughput experiments, observational studies, and simulation. In this new era of data enabled science and engineering, discovery is no longer limited by the collection and processing of data but by data management, knowledge extraction, and the visualization of information. The term data science has become increasingly popular across industry, and academic disciplines to refer to the combination of strategies and tools for addressing the oncoming deluge of data. The term data scientist is a common descriptor of an engineer or scientist from any disciplinary background who is equipped to seamlessly process, analyze, and communicate in this data intensive context. The core areas of data science are often identified as data management, statistical and machine learning, and visualization. In this Perspective, we present an overview of these core areas, discuss application areas from within chemical engineering research, and conclude with perspectives on how data science principles can be included in our training. As has been noted for several years, chemical engineers of all varieties, from the practicing process engineer to the academic researcher, are being asked more and more often to manipulate, transform, and analyze complex data sets. The complexity often stems from the size of the data set itself, but this is not the only factor. For example, the stream of information available to an engineer in a modern plant is tremendous because of the proliferation of inexpensive instrumentation and the nearly ubiquitous high bandwidth and low latency connectivity. In the area of research and discovery, a student or researcher conducting data intensive experiments, such as high resolution particle tracking, might generate more data in an afternoon than a student from a previous decade in the entire time spent earning his or her Ph.D. For those conducting mathematical modeling and computer simulations, advanced algorithms and hardware now give simulators unprecedented resolution but at the cost of massive increases in the data set. Underlying all of these examples is the cheap (near free) cost of data storage and the ubiquitous availability of our data from cloud based services. The aforementioned examples may appear to be vastly different from the outset. However, common themes in the limitation of our current approaches quickly emerge. Because our training of new chemical engineers (at all levels) has not kept pace with the explosion of data, each chemical engineer in the previous examples will likely approach her or his work in the same manner: manual searching for relationships in the data, classical visualization of monovariate or bivariate correlations in features, and a hypothesis driven approach to science reminiscent of a data poor era when the researcher or engineer could essentially manipulate relevant data in their mind. Simply put, without knowledge about and training on how to handle data skillfully, most of the information from our plants and refineries, our data intensive experiments, and our computer simulations is thrown away, simply because we do not know how to extract knowledge from it. Fortunately, there is a potential solution on the horizon. Through the lens of the nascent field of data science, we can see an emergent (and limited) set of tasks needed by all of the previous chemical engineers: (1) to manage a huge data set consisting of ensembles of spatiotemporal data, (2) to sensibly read the data in a computationally scalable manner, and (3) to extract knowledge from this pile of information with robust techniques whose statistical reliability can be quantified. It also goes without saying that data science itself is not a panacea. Chemical engineering fundamentals are of the utmost importance, and Correspondence concerning this article should be addressed to D. A. C. Beck at dacb@uw.edu and J. Pfaendtner at jpfaendt@uw.edu.