Study Notes on Job Skills for Entry-Level Data Analysts

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

Authors

  • Tianxi Dong, School of Business, Trinity University, San Antonio, TX 78212, USA (tianxi.dong@trinity.edu)

  • Jason Triche, University of Montana, Missoula, MT 59801, USA (jason.triche@umontana.edu)

ABSTRACT

The data analytics field has seen explosive growth over the past decade, indicating its importance in the IT sector. Given the rapid pace of technological changes, this study analyzes the job skills and knowledge required in the data analyst and business intelligence (BI) analyst market.

Key Insights:

  • Job Postings Analyzed: Over 9,000 entry-level data analyst job postings from 2014-2018.

  • Methodology: Utilized text mining and a custom text mining dictionary to identify key skills.

  • Findings:

    • Key skills trending upward include proficiency with Python, Tableau, and R.

    • Skills like Microsoft Access, SAP, and Cognos are declining in popularity.

    • The emphasis on data visualization is increasing.

Implications:

Universities can tailor their curricula based on trending skills. Instructors can adapt teaching topics according to industry needs, and companies can use findings to train their employees. The custom text mining dictionary developed can be useful for other researchers in the field.

Keywords: Business analytics, Business intelligence, Careers, Employment skills, Job skills, Text processing

1. INTRODUCTION

The explosive growth of the data analytics field, with no slowing signs, is underscored by several statistics:

  • 2018 Skills Demand: Two of the top five high-paying tech skills related to data analytics (CIO.com).

  • Forecast: Demand for data analysts expected to rise by 28% by 2020 (IBM).

Key challenges in this sector include:

  • Ambiguity of roles and responsibilities among data analysts and BI analysts.

  • Differentiating essential skills from trendy ones.

Key Research Questions:

  1. Which data analyst skills remained steady from 2014 to 2018?

  2. What skills were previously popular but are now less attractive?

  3. Which skills are gaining attention in the current market?

Entry-Level Definition:

  • Entry-level hires are students nearing graduation or those switching careers with necessary analytical skills.

2. LITERATURE REVIEW

2.1 Definitions of Relevant Concepts

Understanding the terminology is crucial:

  • Data Science: Finding value in data to create additional products (Loukides, 2011).

  • Data Analytics: Process of inspecting, cleaning, transforming, and modeling data for decision-making (Lewis-Beck, 1995).

  • Business Intelligence (BI): Applications, technologies, architectures for gathering, storing, analyzing operational data (Gupta, Goul, and Dinter, 2015).

  • Big Data: Not just large amounts of data, but also tools for manipulation and analysis (Burkholder, 1992).

2.2 Skills, Knowledge, and Abilities

  • Skills: Competencies developed through training/experience.

  • Knowledge: Theoretical/practical understanding of subjects.

  • Abilities: Natural talents to perform tasks (Lauby, 2013).

These categories help to analyze job postings effectively, combining soft skills and technical requirements.

2.3 Relevant Studies

Other studies have highlighted skills required in analytics:

  • Deng et al. (2016): Mapped skills required for business analytics roles to educational outcomes.

  • Gardiner et al. (2018): Emphasis on design and development of analytical systems alongside soft skills.

3. METHODOLOGY

  • Data Collection: Focused on Indeed.com due to shared postings across job sites. Used the Common Crawl dataset for historical data.

  • Search Terms: Wildcard searches for various analyst roles were employed. Data scientist roles were excluded as they required more experience.

  • Data Processing: Used Natural Language Processing (NLP) techniques to filter and analyze nouns from job postings.

  • Dictionary Development: An original dictionary was built combining theoretical and empirical approaches, resulting in a set of relevant terms categorized into skills and software.

4. RESULTS

4.1 U.S. State Analysis

This section analyzed the distribution of job postings by state. Notable findings include:

  • Top States (2018): Virginia, Texas, California, New York, Illinois.

  • Standardized Comparison: Adjusted for business density revealed the District of Columbia at the top when normalized by the number of businesses.

4.2 Evolution of Knowledge Required

Academic requirements saw trends from 2014 to 2018:

Degree

2014

2015

2016

2017

2018

Bachelor

60.60%

60.70%

62.30%

64.80%

70.30%***

Master

12.90%

12.10%

14.30%

15.90%

15.30%*

MBA

6.00%

4.10%

4.10%

5.00%

4.60%*

Ph.D.

3.70%

3.80%

4.40%

4.40%

5.60%**

4.3 Evolution of General Domain Skills

The study noted a steady demand for skills with statistical significance:

Skill Type

2018 %

General Analytics

69.90%**

General Statistics

28.20%***

Modeling

21.00%**

Data Management

49.80%***

4.4 Evolution of Software Skills

The following table presented data on software skills:

Skill

2014

2015

2016

2017

2018

SQL Server

10.40%

12.10%

13.40%

15.20%

17.80%***

Access

13.60%

11.60%

11.30%

11.60%

10.40%**

Tableau

5.30%

8.30%

13.20%

18.20%

18.90%***

5. DISCUSSION

5.1 Research Questions Analysis

  • Steady Skills: General statistics and personal productivity tools remained in demand.

  • Declining Skills: Microsoft Access and SAP saw reduced interest.

  • Gaining Skills: Python, SQL Server, and Tableau showed significant growth.

5.2 Contribution to Curriculum Development

  • Universities can adjust courses to emphasize programming, data visualization, and database skills.

  • Training companies can align offerings with findings about industry trends.

5.3 Limitations

  • The analysis was constrained by the historical data availability from Common Crawl.

  • Statistical limitations due to single data points from each year potentially masked trends over time.

6. CONCLUSION

The research identified key trends between 2014 and 2018, emphasizing the importance of programming and data visualization in the analytics job market. The findings can lead to informed curriculum decisions for universities and training programs.

7. ACKNOWLEDGEMENTS

Acknowledgments to contributors and research support (Faculty Research Start-up Fund for 2018 of Trinity University).

8. REFERENCES

A comprehensive list of references that were cited throughout the document, including foundational skills and analytics roles.

APPENDIX A. Final Dictionary

Including technical skills, software packages, and academic degree terminology relevant to the findings.

APPENDIX B. Evolution of Software Skills for Data Analysts

Detailed tables displaying software skill trends over the analyzed years, including any relevant statistical significance indicators.

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