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Computer Science and Education in Computer Science. 19th EAI International Conference, CSECS 2023, Boston, MA, USA, June 28–29, 2023, Proceedings

Research Article

Using Extract, Transform, and Load Framework and Data Visualization Tools to Enhance Career Services for Analytics Master’s Program Student

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-44668-9_27,
        author={Putranegara Riauwindu and Vladimir Zlatev},
        title={Using Extract, Transform, and Load Framework and Data Visualization Tools to Enhance Career Services for Analytics Master’s Program Student},
        proceedings={Computer Science and Education in Computer Science. 19th EAI International Conference, CSECS 2023, Boston, MA, USA, June 28--29, 2023, Proceedings},
        proceedings_a={CSECS},
        year={2023},
        month={10},
        keywords={ETL Microsoft Power BI Relational Database Structured \& Unstructured Data Data Manipulation Career Development Job Posting Analytics Industry Insights},
        doi={10.1007/978-3-031-44668-9_27}
    }
    
  • Putranegara Riauwindu
    Vladimir Zlatev
    Year: 2023
    Using Extract, Transform, and Load Framework and Data Visualization Tools to Enhance Career Services for Analytics Master’s Program Student
    CSECS
    Springer
    DOI: 10.1007/978-3-031-44668-9_27
Putranegara Riauwindu1,*, Vladimir Zlatev1
  • 1: Boston University, Boston
*Contact email: putrangr@bu.edu

Abstract

Tailored industry and occupation information for analytics graduates is vital to make a well-informed career decision, especially for Boston University Metropolitan College (BU MET) Applied Business Analytics students and graduates. This paper proposes an Extract Transform and Load (ETL) framework and Data Visualization method to provide students with easy-to-use and intuitive occupation information.

Multiple analytics-related industry and occupation data were extracted and aggregated from third-party sources, primarily from Lightcast and US Government Official Data. The resulting data underwent manipulation using Microsoft Power Query and Microsoft Excel and were stored in Microsoft SharePoint, with structured data in a flat table and unstructured data in a standalone file with a URL generated for linking the data. A relational database schema was then created to connect the ETL data output for visualization and analysis.

Interactive and user-friendly visualizations were created in Microsoft Power BI, resulting in two dashboards providing students with current information on the job market landscape: (i) Analytics Career Prospect, which offers data on top occupations, salary and wage information, job posting trends, required skills information, hiring industries and companies’ information, education information, and job location; and (ii) Job Market Consultation, which provides a more in-depth analysis of required skills, industry performance and description, and specific job information reports such as Industry Insight, Industry Snapshot, Industry Supply Chain, Industry staffing pattern, and job posting analytics.

The resulting two dashboards provide “one-stop” search places for career research and shorten the cycle time of tedious searches.

Keywords
ETL Microsoft Power BI Relational Database Structured & Unstructured Data Data Manipulation Career Development Job Posting Analytics Industry Insights
Published
2023-10-11
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-44668-9_27
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