Virtual On line
June 22, 2020
June 22, 2020
June 26, 2021
The field of Big Data Analytics continues to rapidly grow and encompass a greater number of techniques from a host of fields. Through the utilization of activities and reading assignments aimed to stimulate students’ interest in the field of Big Data Analytics we make them aware that Linear Algebra plays an important role in Big Data, given certain inherent constraints found in the Linear Algebra course curriculum. Key concepts in the undergraduate curriculum are stressed as to their role in Big Data. We start with techniques that enhance basic principles of Linear Algebra, such as understanding the algorithm for matrix multiplication and transition matrices, and how they relate to data and provide context, then through modules focusing on relevant real world scenarios we present appropriate analysis methods. In particular, we examine the Leslie Matrix and population change, as well as the PageRank (PR) algorithm used by Google amongst other applicable Linear Algebra techniques. Based on student outcomes we consider challenge while aiming to seamlessly integrate Big Data Analytics with our Linear Algebra course.
Swamidurai, R., & Jones, C. M., & Pettis, C., & Kannan, U. (2020, June), Applications of Linear Algebra Applied to Big Data Analytics Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34153
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