June 15, 2019
June 15, 2019
October 19, 2019
The field of Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Individuals who have the skill set to effectively and efficiently analyze these large data sets are becoming increasingly valuable to industry and many areas of research. In this paper, we present our three years of experience with structuring, teaching, and assessing a course that actively engages students in real-world data analysis. Our course guides students through methods for data analysis using probabilistic and statistical techniques that are part of the undergraduate curriculum. The process is taught incrementally. We start with techniques that enhance basic principles of statistics and how they relate to data and provide context, then add additional computer based activities using relevant software and coding methods. Thus, students work in an interactive environment in which they are instructed on new techniques and then mentored in the use of those techniques through a series of active learning exercises.
Swamidurai, R., & Jones, C. M., & Pettis, C., & Kannan, U. (2019, June), Big Data Analytics - With an Infusion of Statistics for the Modern Student Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32155
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2019 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015