Job qualifications study for data science and big data professions

Abstract

Purpose

The lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.

Design/methodology/approach

1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.

Findings

The findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.

Originality/value

This research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

Citation

Halwani, M.A., Amirkiaee, S.Y., Evangelopoulos, N. and Prybutok, V. (2022), "Job qualifications study for data science and big data professions", Information Technology & People, Vol. 35 No. 2, pp. 510-525. https://doi.org/10.1108/ITP-04-2020-0201