A DATA SCIENCE ENHANCED FRAMEWORK FOR APPLIED AND COMPUTATIONAL MATH

Abstract

Aim/Purpose The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole. Background The framework can help students grasp new mathematical applications by comparing them to a common reference model. Methodology In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale. Contribution The enhanced framework improves the Computational Math scale by integrat ing selected concepts from the field of Data Science. Findings The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer imple mentations. Future Research We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts. Keywords framework, applied math, computational math, data science, concordance