Synthetic Employee Data for People Analytics

Synthetic data designed for practicing people analytics workflows. The dataset contains artificial employee records for modeling HR scenarios without privacy concerns. Specific row counts, column details, and authorship are not provided.

Use Cases

• Practice attrition modeling using synthetic employee attributes and performance metrics.
• Analyze compensation equity across synthetic job roles and departments.
• Build synthetic promotion prediction models based on tenure and performance scores.
• Test diversity and inclusion analysis methods on artificially generated demographic features.

Strengths

• Data is synthetic, enabling safe experimentation without real employee privacy risks.
• Specifically tagged for use in people analytics practice scenarios.

Limitations

• Unknown size and row count limit statistical reliability for model training.
• Synthetic nature means patterns may not reflect real-world organizational dynamics.
• Lack of column details prevents assessment of feature relevance and completeness.

Provenance

Source

Kaggle

Collection Method

Synthetically generated for analytics practice.