https://github.com/harshindcoder/people_analytics_case_study
End to End People Analytics Project with database design and analysis using SQL and python programming language.
https://github.com/harshindcoder/people_analytics_case_study
database-schema people-analytics python3 query-language visualization
Last synced: 7 months ago
JSON representation
End to End People Analytics Project with database design and analysis using SQL and python programming language.
- Host: GitHub
- URL: https://github.com/harshindcoder/people_analytics_case_study
- Owner: harshindcoder
- License: mit
- Created: 2025-03-24T05:01:40.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-24T05:43:34.000Z (7 months ago)
- Last Synced: 2025-03-24T06:22:08.517Z (7 months ago)
- Topics: database-schema, people-analytics, python3, query-language, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 65.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📊 Employee Retention Analysis using People Analytics
Welcome to the Employee Retention Analysis project repository. This project applies the **data analysis process** to understand and improve the **retention rate of new employees** within an organization, utilizing both **quantitative** and **qualitative** data from surveys.
---
## 🚀 Project Overview
Many organizations face high turnover rates among new hires. This project uses **people analytics** to analyze employee satisfaction and identify key factors that influence retention.
**Goal**: To improve retention by identifying actionable insights from employee feedback and process evaluation.
---
## 📈 Data Analysis Process
This project follows the **6-step data analysis process**:
### 1. **Ask**
- Define project scope and success criteria.
- Collaborate with stakeholders (leaders, managers).
- Example Questions:
- What do new hires need to succeed?
- What causes dissatisfaction?
- What’s the desired retention increase?
---
### 2. **Prepare**
- Create a 3-month timeline and progress report plan.
- Design and deploy an **employee survey**.
- Define **data access rules** (e.g., only summarized data available to stakeholders).
- Plan for **data visualization** and potential issues.
---
### 3. **Process**
- Collect data ethically with **employee consent**.
- Ensure transparency in data usage and storage.
- Process steps:
- Restrict raw data access.
- Clean data for accuracy and completeness.
- Upload raw data securely to an **internal data warehouse**.
---
### 4. **Analyze**
- Discover patterns and insights.
- Key Findings Example:
- Long hiring process → Higher turnover.
- Transparent evaluations → Higher retention.
- Use appropriate **data analysis tools** (Python, SQL, etc.).
---
### 5. **Share**
- Share **summarized reports** with managers.
- Managers deliver results with context to teams.
- Encourage **team discussions** on improving engagement.
---
### 6. **Act**
- Implement process improvements.
- Repeat survey **annually** for comparison.
- Measure success via **retention rate increase**.
---
## 📋 Survey Design & Data Involved
### Survey Data Types:
| Question | Type | Data Type |
|----------|------|-----------|
| Hiring satisfaction (1-10) | Quantitative | Integer |
| Hiring duration (weeks) | Quantitative | Float |
| Onboarding rating (1-5) | Quantitative | Integer |
| Recommend company (1-10) | Quantitative | Integer |
| Current job satisfaction (1-10) | Quantitative | Integer |
| Challenges during hiring | Qualitative | String |
| Suggestions for onboarding | Qualitative | String |
| Reason for leaving | Qualitative | String |
| Improvements for satisfaction | Qualitative | String |
---
## 🔍 Data Analysis Methods
### Quantitative Analysis:
- Tools: Python (**pandas**, **matplotlib**), Excel, SQL
- Techniques:
- **Descriptive statistics** (mean, median)
- **Box plots** for hiring duration vs retention
- **Correlation matrices**
- **Bar/line charts** for trends across teams
### Qualitative Analysis(Can be done on strings with undefined categories):
- Tools: LLMs (e.g., GPT), **spaCy**, **NLTK**
- Techniques:
- **LLM-based categorization** of open text (e.g., reasons for leaving: Compensation, Management)
- **Sentiment analysis**
- **Word clouds** and **topic modeling** for key themes
---
## 📊 Visualization Examples
- Box plot: Hiring duration vs retention
- Bar chart: Average onboarding score by department
- Pie chart: Categorized reasons for leaving
- Word cloud: Common suggestions from new hires
---
## 📅 Timeline
- Survey deployment: Month 1
- Data collection and processing: Month 2
- Analysis and reporting: Month 3
---
## 🛠 Tools Used
- **Survey Tools**: Google Forms
- **Analysis**: Python, SQL
- **Visualization**: Matplotlib, Tableau
- **Storage**: Internal Data Warehouse (SQL-based)
---
## 📬 Contact
For questions or contributions, reach out to **Harsh Indoria** via GitHub Issues or email at harsh.ind.coder@gmail.com.