Educational Data Virtual Lab: Connecting the Dots Between Data Visualization and Analysis

Area of Science:

• Data Science Education
• Information Systems
• Human-Computer Interaction

Background:

• Organizations need to upskill their workforce in data science.
• Existing tools may not be tailored for domain experts or integrate with organizational data.
• A need exists for accessible platforms that support the complete data lifecycle.

Purpose of the Study:

• To introduce and evaluate the Educational Data Virtual Lab (EDVL) platform.
• To assess user perceptions of EDVL for educational and operational purposes.
• To gauge the potential of EDVL in enhancing data science skills and collaboration.

Main Methods:

• Developed an open-source platform (EDVL) integrating coding, visualization, and data lifecycle management.
• Built upon FIWARE and Apache Zeppelin frameworks.
• Conducted a pilot study with a focus group within a multinational company to gather user feedback.

Main Results:

• User feedback from the pilot study was collected and analyzed.
• Potential users expressed positive perceptions regarding EDVL's educational value.
• Participants recognized EDVL's operational benefits for data exploration and analysis.

Conclusions:

• EDVL demonstrates significant potential as an educational tool for data science.
• The platform can facilitate collaboration among professionals with varying data expertise.
• EDVL offers a relevant and goal-driven approach to data science skill acquisition for domain experts.