A Longitudinal Analysis of Job Skills for Entry-Level Data Analysts

Tianxi Dong, Jason Triche

Research output: Contribution to journalArticlepeer-review


The explosive growth of the data analytics field has continued over the past decade with no signs of slowing down. Given the fast pace of technology changes and the need for IT professionals to constantly keep up with the field, it is important to analyze the job skills and knowledge required in the data analyst and business intelligence (BI) analyst job market. In this research, we examine over 9,000 job postings for entry-level data analytics jobs over five years (2014–2018). Using a text mining approach and a custom text mining dictionary, we identify a preliminary set of analytic competencies sought in practice. Further, the longitudinal data also demonstrates how these key skills have evolved over time. We find that the three biggest trends include proficiency with Python, Tableau, and R. We also find that an increasing number of jobs emphasize data visualization. Some skills, like Microsoft Access, SAP, and Cognos, declined in popularity over the time frame studied. Using the results of the study, universities can make informed curriculum decisions, and instructors can decide what skills to teach based on industry needs. Our custom text mining dictionary can be added to the growing literature and assist other researchers in this space.

Original languageEnglish
Pages (from-to)312-326
Number of pages15
JournalJournal of Information Systems Education
Issue number4
StatePublished - 2020


  • Business analytics
  • Business intelligence
  • Careers
  • Employment skills
  • Job skills
  • Text processing


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