https://github.com/mikaremi/hr-analytics

contains an analysis of HR data
https://github.com/mikaremi/hr-analytics

Last synced: about 1 month ago
JSON representation

contains an analysis of HR data

• Host: GitHub
• URL: https://github.com/mikaremi/hr-analytics
• Owner: Mikaremi
• Created: 2024-02-06T12:33:17.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-14T12:16:50.000Z (10 months ago)
• Last Synced: 2024-04-15T10:57:42.090Z (10 months ago)
• Size: 1.24 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Hrdata Analysis

### Project Overview

This data analysis project aims to provide insights about employees who are working at a particular company. By analyzing the various aspects of the hr data, we set to identify trends, make data driven recommendations, and gain a deeper understanding of the employees in the company.

### Data source

HR DATA: The primary dataset used for this analysis is the "HR DATA.csv" file, containing detailed informations about each employee in the company.

### Project requirements:

- Excel - Data cleaning

- SQL - Data analysis

- Power Bi - Creating a report

  

### Data cleaning

In the initial data preparation phase, I performed the following tasks:

1. Data loading and inspection

2. Handling missing values

3. Data cleaning and formatting

### EDA

1. What is the age distribution in the company?

2. What is the gender breakdown in the company?

3. How does gender vary accross departments and job tittle?

4. What is the race distribution in the company?

5. What is the average length of employment in the company?

6. Which department has the highest turnover rate?

7. What is te tenure distribution for each department?

8. How many employees work remotely for each department?

9. What is te distribution of employees accross different states?

10. How are job tittles distributed in the company?

11. How many employee hire counts varies over time?

### Data Analysis

``` sql

SELECT 

 department,

 total_count,

 terminated_count,

 (round((CAST(terminated_count AS FLOAT)/total_count), 2))*100 AS turnover_rate

 FROM

    (SELECT 

     department,

     COUNT(*) AS total_count,

     SUM(CASE

        WHEN new_termdate IS NOT NULL AND new_termdate <= GETDATE() THEN 1 ELSE 0

	    END

	    ) AS terminated_count

     FROM hr_data

     GROUP BY department

	 ) AS subquery

ORDER BY turnover_rate DESC;

```

### Results

1. Most of the employees are Male.

2. Most of the employees are working at the headquaters

3. Most of the employees in the company are White.

4. Engineering department has the most numbers of employees in the company.

5. Most of the employees in the company are aged 51+

6. The year 2020 recorded the highest % hire change