Enhancing Student Learning Experience with Practical Big Data Analytics Techniques

Abstract

With the enormous growing volumes and varieties of consumer data, generated by various desktop, mobile, and Internet of Things applications, e-commerce, and other resources, and the recent advances of computational processing and data storage technologies, big data analytics has become an increasingly important tool of transforming large quantities of digital data into meaningful insight and decisions (see “Characteristics of Mobile Teaching and Learning”). The concept of big data has been around for decades, but it has only become a hot buzzword in the last few years, and its broad applications nowadays have been enthusiastically embraced by financial service providers, retailers, insurers, manufacturers, healthcare organizations, universities, and other enterprises. To meet the great demand for data scientists and engineers from almost every sector of industry, business, and government, several universities have recently started graduate programs in data science or data analytics. However, the number of undergraduate programs that have integrated big data analytics courses into their curricula still remains very small.

In this chapter, the author describes the design, implementation, and evaluation of two data analytics courses, Introduction to Data Mining and Introduction to Artificial Neural Networks, which have been developed and included in the undergraduate computer science program at the University of San Diego (USD). Since the spring of 2011, both courses have been offered as upper-division electives on a regular basis, and it has been a very successful learning experience for both the instructor and the students. The courses include, in addition to the coverage on key data analytics concepts, principles, and applications, a unique student lecture series, programming projects, and research activities to engage students in active learning. The author has also recently been offering the data mining course as a study abroad program in China, integrated with additional guest lectures by data scientists from the host institutions and field trips to visit top information technology firms in the host country. The author’s experience has shown that big data analytics can be successfully taught at the undergraduate level, and in fact, students enrolled in the courses have learned a great deal of data analytics techniques and have been able to apply them to solve many real-world problems.