scribus01/synthax-week3-task1-project-2-richardekpadi

This project is an HR Analytics Dashboard built to study employee attrition, meaning how often and why employees leave a company. The README frames employee turnover as a costly problem for organizations, since it affects productivity, hiring costs, and overall workforce stability, and the project's goal is to turn raw employee data into insights HR teams can act on. The work combines SQL for analyzing the underlying data with Power BI for building the visual dashboard, supported by Power Query and DAX for shaping and calculating the data, and Microsoft Excel as an additional tool. The stated business objectives are to break down attrition by department and job role, measure retention and turnover rates, identify what drives employees to leave, look at compensation and demographic patterns, and give HR leaders insights to improve retention. The dashboard itself is organized into several pages. An executive overview page shows headline numbers like total employees, attrition rate, retention rate, average salary, and average tenure. A department and job role page and an attrition drivers page dig into job satisfaction, environment satisfaction, overtime, income bands, years at the company, and distance from home. A workforce and compensation page covers salary broken down by department and job role, and a demographics page covers education, marital status, and age distribution. From this analysis, the README lists several findings: attrition varies by department, employees who work overtime are more likely to leave, lower job satisfaction correlates with higher attrition, and shorter tenure employees leave more often. Based on these findings, the recommendations include reviewing overtime policies, strengthening employee engagement efforts, improving retention programs for early career staff, and reviewing compensation regularly. The README does not describe any setup steps or code to run, since this is a data analysis and dashboard project rather than a software application. It is credited to a single author, Ekpadi Richard Babatunde, described as a data analyst working with SQL, Power BI, Excel, and data visualization.