Data-Science literacy for future security and intelligence professionals

Abstract

ABSTRACT Teaching data literacy topics, such as machine learning, to security studies students is difficult because there are limited security-related teaching materials (e.g. datasets, user friendly software) for instructors. To address this challenge, we conducted an exploratory study to evaluate an asynchronous training module and software prototype with 15 college students. A key finding from this study is the importance of a simple teaching software tool and security case studies. The module boosted knowledge of key concepts and awareness of ‘big data’ accountability issues. We also found that teaching data-science concepts – even at an elementary level – requires that students have basic proficiencies working with datasets and spreadsheets, which suggests the need to integrate these skills throughout security studies curricula. This research also highlights the importance of building partnerships with data-science instructors to integrate data-science literacy in security studies and intelligence studies.