Data-Science literacy for future security and intelligence professionals

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.

KEYWORDS:

• Data science
• machine learning
• security and intelligence

Acknowledgements

Two Ph.D. students, Ahnaf Farhan and Sebastian Ayala Urtaza, in the Department of Computer Science at the University of Texas at El Paso (UTEP), developed the EasyML Online (Easy Machine Learning Online) system hosted on Amazon Web Service (AWS). The project was supported in parts by the UTEP Interdisciplinary Research and Education Program (IDRE). The authors wish to thank Michael Landon-Murray for his helpful feedback on previous drafts of this article.

This study was approved by Institutional Review Boards (IRB) at the University at Albany (#21X017) and Arizona State University (STUDY #00013667) as an exempt study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Student 8.

2 In 2019, NASPAA launched a call for white papers on a data-science curriculum for public service to start to address these deficiencies. See: https://www.naspaa.org/call-white-papers-data-science.

3 See: https://teachablemachine.withgoogle.com/

4 Student 5.

5 Student 3.

6 Student 6.

7 Student 13.

8 Student 7.

9 Student 7.

10 Student 6.

11 Student 8.

12 Student 8.

Additional information

Funding

This work was supported by University of Texas at El Paso: [Grant Number ].