Context
Introduction
The consumer electronics industry—particularly wearables—faces growing scrutiny as device defects can lead to physical injuries and regulatory penalties.
Role
• The learner acts as a Quality & Compliance Data Analyst at SafeWear Tech.
• They are responsible for detecting patterns in defect occurrences, assessing risk levels, and informing both product redesign and regulatory reporting workflows.
Business Objectives
• The task is to uncover defect clusters and prioritize them based on severity and frequency, enabling the company to proactively address hazards before consumer harm occurs.
• The learner’s technical skills in data analysis and understanding of defect management equip them to translate raw data into actionable insights.
Products
• The primary deliverable is a dashboard and written summary outlining defect hotspots, severity rankings, and suggested quality-control actions.
• This output illustrates successful alignment of data insights with operational and regulatory responses to product safety risks.
Codebook
Columns:
• defect_id: Unique identifier for each defect.
• product_id: Identifier for the product associated with the defect.
• defect_type: Type or category of the defect (e.g., cosmetic, functional, structural).
• defect_description: Description of the defect.
• defect_date: Date when the defect was detected.
• defect_location: Location within the product where the defect was found (e.g., surface, component).
• severity: Severity level of the defect (e.g., minor, moderate, critical).
• inspection_method: Method used to detect the defect (e.g., visual inspection, automated testing).
• repair_action: Action taken to repair or address the defect.
• repair_cost: Cost incurred to repair the defect (in local currency).
Dataset
License
Not Provided
Tags
Data Provenance
