Pedagogy of diversity and data analytics: Theory to practice

Abstract

A course in probability is a requirement in Baccalaureate Programs in Electrical and Computer Engineering. Students view this course as conceptual with little connection to real world problems. Efforts have been undertaken to mitigate this issue and connect the conceptual topics to data analytics to make the course relevant. Data analytics based exercises are now integral to the course. Several demos were created to link statistical concepts with practice through analysis of data. In this study, a demo created to illustrate the concept of diversity to improve the performance of a machine vision system is described. It incorporates concepts of Bayes' rule, single and multiple random variables, goodness fit tests, random number simulation and data analytics to illustrate the pedagogy of diversity and associated data processing. Student survey results suggest that these demos enhance that the learning experience in the engineering probability course. K E Y W O R D Sdiversity, engineering probability, Matlab, receiver operating characteristics curves

Introduction of data analytics in the engineering probability course: Implementation and lessons learnt

“…Students were also provided with sample solutions to the data analytics assignments (created using a different data set) to make them aware of the contents of their submission (plots, charts, explanations, etc.). For each assignment, appropriate demos were done in the class [6,9–12].…”

“…This meant that they had seen methods of finding the density of the sum, difference, product, ratio, maximum, and minimum of a pair of independent variables. This assignment was meant to demonstrate the benefits of signal processing algorithms such as the maximum, arithmetic mean, and the geometric mean of two variables [9]. The importance lies in the fact that two sets of data can be collected and processed to provide improved performance of the machine learning system.…”

Tutorial overview of simple, stratified, and parametric bootstrapping

“…Students were familiar with data analytics because they were required to solve one homework problem in Matlab (http://www.mathworks.com) every week with a unique dataset for every student (besides a set of common problems for the class). Students had already completed exercises on positive predictive values, confusion matrix, AUC, chi square testing involving multiple densities to determine the best fit, maximum likelihood estimation of parameters of densities, and statistical analysis of improvement in performance achieved through signal processing algorithms, namely arithmetic mean, geometric mean, maximum . The topic of bootstrapping was introduced after students were exposed to mathematical statistics (marginal, conditional, joint probabilities, and Bayes' rule), one and two random variables, parametric hypothesis testing (chi square tests), ROC curves, etc.…”