64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Constructing a course on classification methods for undergraduate non-STEM students: Striving to reach knowledge discovery


64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: data science education, interpretation, visualization

Session: CPS 16 - Teaching statistics II

Monday 17 July 4 p.m. - 5:25 p.m. (Canada/Eastern)


Classification is one of the most common data analytics tasks. It is employed in myriad disciplines including marketing, finance, sociology, education, and public health. It is therefore appropriate to extend familiarity with data classification methods to non-STEM students who will face such problems during their professional careers. For this end, the current work presents a full data classification course, which integrates theoretical and practical skills, and designed to prepare non-STEM students for comprehensive data analysis tasks. The level of difficulty of the course depends not only on the background of the students but also on the course prerequisites and requirements as set by the specific department. The suggested framework begins with data preparation, provides a comprehensive toolbox, including methodical techniques and software tools, for data classification, and eventually leads students to the discovery of new knowledge and insights. We recommend addressing the teaching of the subject as a dynamic process that involves grasping the analytical task, understanding the terms and concepts, visualizing the classification, analyzing the data, interpreting the results, and drawing conclusions. The course combines theoretical study, practical projects, open discussions, and even competitions between class participants. We assume that the practical projects, carried out in small groups, will have a significant impact, so we recommend focusing them on real problems based on real data. Our research has an important contribution in providing non-STEM students with the ability to perform an analytical process from problem characterization through data analysis to decision-making. Our study will make the use of a variety of data-mining methods accessible as a substitute or as a support for classical statistics education.