Abstract
This chapter is solely dedicated to the first half of the Assemble phase: Data Wrangling. Data wrangling is the overwhelmingly dominant part of a data scientists’ job, and this chapter (1) introduces wrangling to those unfamiliar with its place in the data lifecycle, and (2) puts an HR lens on data wrangling in terms of the employee lifecycle and HR data systems. This chapter concludes by introducing some common data formats, wrangling tools, as well as a few moderately advanced data transformation techniques, like binning, lagging, and z-scores.
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Notes
- 1.
Note: Creating an index is a complex process which requires analysis of collinearity, latent variables, and other considerations. If you want to create an index, consult with your analytics team.
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Rosett, C.M., Hagerty, A. (2021). Data Wrangling. In: Introducing HR Analytics with Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-67626-1_13
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DOI: https://doi.org/10.1007/978-3-030-67626-1_13
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67625-4
Online ISBN: 978-3-030-67626-1
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