Teaching data analytics across the computing curricula
Dan Brandon · Journal of computing sciences in colleges · 2015
The Economist calls analytics golden vein [6], and many experts now say it is the science of winning [7]. Business and technologists have many names for this new science, business intelligence (BI), data analytics, and data mining are among the most common. For these reasons, colleges are rushing to develop curriculums, courses, and teaching methods to prepare students for this field. However teaching students this new science is challenging since it involves a variety of disciplines, and teaching to different disciplines must be done in such a manner as to focus on that discipline's main contribution to this inter-disciplinary topic. For example, the Computing Curriculum 2005 [1] provides undergraduate guidelines for five defined sub-disciplines of computing. This paper describes a general approach that was developed that provides intuitive and interactive learning of analytics core principles while also providing for extensions relevant for each discipline. That general approach is illustrated with the popular analytics affinity analysis algorithm.