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Survey Analytics Pipeline (Quantitative + NLP)
Workforce AnalyticsEnd-to-end pipeline for processing employee surveys, combining quantitative analysis with qualitative text analytics.
Overview
💡 Challenge
HR teams struggle to extract meaningful insights from employee surveys that combine Likert-scale questions with open-ended responses.
⚡ Solution
Built an automated pipeline that processes survey data, calculates engagement scores, performs sentiment analysis on text responses, and generates visualizations.
🎯 Impact
Transforms raw survey data into actionable insights within 24 hours, enabling faster response to employee concerns.
Technical Details
🛠️ Tech Stack
PythonPandasNLTKPower BISQL
✨ Key Features
- ✓Automated data cleaning and validation
- ✓Engagement score calculation with benchmarking
- ✓Sentiment analysis on open-ended responses
- ✓Demographic segmentation and drill-downs
Key Learnings
- →Survey data quality issues are common and need robust handling
- →Combining quant and qual insights provides richer narratives
- →Anonymization is critical for sensitive HR analytics
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.