Abstract: Quantitative decision making (management science, business statistics) textbooks rarely address data cleansing issues, rather, these textbooks come with neat, clean, well-formatted data sets for the student to perform analysis on. However, with a majority of the time spent on gathering and pre-conditioning data, students need to be trained on what to look for when receiving data. A critical scan of the data needs to be performed (at a minimum) to look for errors in the data set before data analysis can be performed.
Keywords: data anyalsis, Data cleansing, data formatting, data pre-conditioning, pareto principle
Download this article: ISEDJ - V17 N6 Page 22.pdf
Recommended Citation: Snyder, J. (2019). Data Cleansing: An Omission from Data Analytics Coursework. Information Systems Education Journal, 17(6) pp 22-29. http://isedj.org/2019-17/ ISSN: 1545-679X. (A preliminary version appears in The Proceedings of EDSIGCON 2018)