HR Data Analysis - Psyliq Internship

Key Problem Statements and Solutions:

1. Data Import and Transformation:
   • Demonstrated the process of importing employee data from Excel files and implemented transformations to ensure data cleanliness by removing unnecessary columns and rows.
2. Basic Visualization:
   • Created a straightforward bar chart to visually represent the distribution of employees across different departments.
3. Filtering Data:
   • Implemented a slicer to enable users to dynamically filter employees based on their job roles, enhancing data exploration capabilities.
4. Joining Data:
   • Employed appropriate join techniques to combine employee data with in-time and out-time data, facilitating comprehensive analysis.
5. Calculated Columns:
   • Developed calculated columns to categorize employees into age groups, contributing to demographic analysis.
6. Measures in DAX:
   • Utilized DAX to calculate the average monthly income for employees, presenting the results in a card visualization for quick insights.
7. Time Intelligence:
   • Implemented DAX for time intelligence, calculating year-over-year growth in monthly income, offering insights into financial trends.
8. Hierarchies:
   • Created hierarchies for date and time columns, enabling intuitive drill-down analysis for a deeper understanding of temporal patterns.
9. Advanced DAX Calculation:
   • Calculated the attrition rate for each department using advanced DAX calculations and visually represented it through a heatmap for effective interpretation.
10. Advanced Join:
    • Applied a left join to combine employee data with a different dataset, addressing potential pitfalls and ensuring data integrity.
11. Complex Filtering:
    • Developed a dynamic filter allowing users to filter employees based on both department and job role simultaneously, enhancing customization options.
12. Advanced Time Intelligence:
    • Implemented DAX to calculate the moving average of monthly income over a rolling 3-month period, providing a smoothed trend analysis.
13. Conditional Formatting:
    • Applied conditional formatting to highlight employees with the highest and lowest monthly incomes, aiding in quick identification of outliers.
14. Parameter Tables:
    • Utilized parameter tables to empower users to set their own thresholds for performance ratings, visualizing the results for personalized analysis.
15. Custom Visualizations:
    • Incorporated custom visuals or third-party visuals to present data in unique ways not available in default Power BI visuals, enhancing the visual appeal of the analysis.
16. Aggregations:
    • Optimized performance for large datasets by explaining the creation of aggregations, ensuring efficient data processing.
17. What-If Analysis:
    • Demonstrated the use of What-If parameters to showcase how attrition rates change with adjustments in factors such as salary increases.
18. Cross-Filtering:
    • Showcased the interactive experience for users through cross-filtering between visuals, allowing for a seamless exploration of interconnected data.
19. KPIs:
    • Developed Key Performance Indicators (KPIs) for employee performance using DAX calculations, providing a quick snapshot of critical metrics.
20. Dynamic Reporting:
    • Illustrated how to make the report dynamic through the use of bookmarks and buttons, enabling users to switch between different views of the data for a personalized experience.

Conclusion: The HR Data Analysis project showcased my proficiency in Power BI development, data analysis, and visualization, providing actionable insights into HR metrics. The project not only addressed specific problem statements but also demonstrated my ability to create dynamic and interactive reports for informed decision-making in human resources management.