🚀 Data Analyst Project Series – Part 2

HR Analytics Dashboard Project

🎯 Project Goal
The goal of this project is to analyze employee data and create an HR Analytics Dashboard that helps companies understand:
• Employee attrition
• Employee performance
• Department-wise analysis
• Salary trends
• Employee satisfaction
• Hiring and retention insights

This is one of the most popular real-world Data Analyst projects because every company tracks employee performance and retention.

🛠 STEP 1: Choose an HR Dataset

Recommended Datasets
Search on Kaggle:
• HR Analytics Dataset
• Employee Attrition Dataset
• IBM HR Analytics Dataset

📂 STEP 2: Understand the Dataset

Common Columns in HR Data
Column Name: Employee ID
Meaning: Unique employee number

Column Name: Age
Meaning: Employee age

Column Name: Gender
Meaning: Male/Female

Column Name: Department
Meaning: Department name

Column Name: Job Role
Meaning: Employee role

Column Name: Salary
Meaning: Employee salary

Column Name: Attrition
Meaning: Employee left or not

Column Name: Years at Company
Meaning: Work experience

Column Name: Satisfaction Score
Meaning: Employee satisfaction

Column Name: Performance Rating
Meaning: Employee performance

🧹 STEP 3: Data Cleaning
HR data usually contains:
• Missing values
• Duplicate employees
• Incorrect salary formats
• Inconsistent department names

✔ Cleaning Tasks

Remove Duplicate Employees
Example:
Same Employee ID appearing multiple times.

Handle Missing Values
Check:
• Missing salary
• Missing department
• Empty performance ratings

Standardize Text
Example:
• “Human Resources”
• “HR”
• “human resources”

Convert all into one standard format.

Correct Data Types
Examples:
• Salary → Number
• Joining Date → Date
• Attrition → Yes/No

📊 STEP 4: Define HR KPIs
KPIs are very important in HR Analytics.

Essential KPIs

✔ Total Employees
COUNT(Employee_ID)

✔ Attrition Count
COUNT(CASE WHEN Attrition = 'Yes' THEN 1 END)

✔ Attrition Rate
(Employees_Left / Total_Employees) * 100

Purpose:
Measures employee turnover.

✔ Average Salary
AVG(Salary)

✔ Average Satisfaction Score
AVG(Satisfaction_Score)

🗄 STEP 5: HR Data Analysis Using SQL
Now start analyzing the HR data.

📌 SQL Query Examples

1. Attrition by Department
SELECT Department,
COUNT(*) AS Employees_Left
FROM HR_Data
WHERE Attrition = 'Yes'
GROUP BY Department
ORDER BY Employees_Left DESC;

2. Average Salary by Job Role
SELECT Job_Role,
AVG(Salary) AS Avg_Salary
FROM HR_Data
GROUP BY Job_Role
ORDER BY Avg_Salary DESC;

3. Employee Count by Gender
SELECT Gender,
COUNT(*) AS Employee_Count
FROM HR_Data
GROUP BY Gender;

4. Top Departments with Highest Satisfaction
SELECT Department,
AVG(Satisfaction_Score) AS Avg_Satisfaction
FROM HR_Data
GROUP BY Department
ORDER BY Avg_Satisfaction DESC;

📈 STEP 6: Build HR Dashboard
Use:
• Power BI
• Tableau

🎨 Dashboard Layout

Section 1: KPI Cards
Display:
• Total Employees
• Attrition Rate
• Average Salary
• Satisfaction Score

These should appear at the TOP.

Section 2: Charts

✔ Bar Chart
Use for:
• Attrition by Department

✔ Pie Chart
Use for:
• Gender Distribution

✔ Line Chart
Use for:
• Hiring Trend Over Time

✔ Heatmap
Use for:
• Performance vs Satisfaction

✔ Tree Map
Use for:
• Department-wise Employee Distribution

🎛 STEP 7: Add Dashboard Filters
Add slicers for:
✔ Department
✔ Gender
✔ Job Role
✔ Experience Level
✔ Attrition Status

This makes the dashboard interactive.

🎨 STEP 8: Improve Dashboard Design

Design Tips
✔ Use HR-friendly colors
✔ Avoid too many visuals
✔ Keep important KPIs visible
✔ Add icons where necessary
✔ Maintain spacing and alignment