{"id":26644987,"url":"https://github.com/harshindcoder/people_analytics_case_study","last_synced_at":"2026-05-02T13:35:25.137Z","repository":{"id":284092927,"uuid":"953796442","full_name":"harshindcoder/People_Analytics_Case_Study","owner":"harshindcoder","description":"End to End People Analytics Project with database design and analysis using SQL and python programming language.","archived":false,"fork":false,"pushed_at":"2025-03-24T05:43:34.000Z","size":67,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-24T06:22:08.517Z","etag":null,"topics":["database-schema","people-analytics","python3","query-language","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/harshindcoder.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-24T05:01:40.000Z","updated_at":"2025-03-24T05:43:37.000Z","dependencies_parsed_at":"2025-03-24T06:33:44.262Z","dependency_job_id":null,"html_url":"https://github.com/harshindcoder/People_Analytics_Case_Study","commit_stats":null,"previous_names":["harshindcoder/people_analytics_case_study"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harshindcoder%2FPeople_Analytics_Case_Study","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harshindcoder%2FPeople_Analytics_Case_Study/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harshindcoder%2FPeople_Analytics_Case_Study/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harshindcoder%2FPeople_Analytics_Case_Study/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/harshindcoder","download_url":"https://codeload.github.com/harshindcoder/People_Analytics_Case_Study/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245352289,"owners_count":20601123,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["database-schema","people-analytics","python3","query-language","visualization"],"created_at":"2025-03-24T21:21:16.299Z","updated_at":"2026-05-02T13:35:25.059Z","avatar_url":"https://github.com/harshindcoder.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 Employee Retention Analysis using People Analytics\n\nWelcome 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.\n\n---\n\n## 🚀 Project Overview\n\nMany organizations face high turnover rates among new hires. This project uses **people analytics** to analyze employee satisfaction and identify key factors that influence retention.\n\n**Goal**: To improve retention by identifying actionable insights from employee feedback and process evaluation.\n\n---\n\n## 📈 Data Analysis Process\n\nThis project follows the **6-step data analysis process**:\n\n### 1. **Ask**\n- Define project scope and success criteria.\n- Collaborate with stakeholders (leaders, managers).\n- Example Questions:\n  - What do new hires need to succeed?\n  - What causes dissatisfaction?\n  - What’s the desired retention increase?\n\n---\n\n### 2. **Prepare**\n- Create a 3-month timeline and progress report plan.\n- Design and deploy an **employee survey**.\n- Define **data access rules** (e.g., only summarized data available to stakeholders).\n- Plan for **data visualization** and potential issues.\n\n---\n\n### 3. **Process**\n- Collect data ethically with **employee consent**.\n- Ensure transparency in data usage and storage.\n- Process steps:\n  - Restrict raw data access.\n  - Clean data for accuracy and completeness.\n  - Upload raw data securely to an **internal data warehouse**.\n\n---\n\n### 4. **Analyze**\n- Discover patterns and insights.\n- Key Findings Example:\n  - Long hiring process → Higher turnover.\n  - Transparent evaluations → Higher retention.\n- Use appropriate **data analysis tools** (Python, SQL, etc.).\n\n---\n\n### 5. **Share**\n- Share **summarized reports** with managers.\n- Managers deliver results with context to teams.\n- Encourage **team discussions** on improving engagement.\n\n---\n\n### 6. **Act**\n- Implement process improvements.\n- Repeat survey **annually** for comparison.\n- Measure success via **retention rate increase**.\n\n---\n\n## 📋 Survey Design \u0026 Data Involved\n\n### Survey Data Types:\n\n| Question | Type | Data Type |\n|----------|------|-----------|\n| Hiring satisfaction (1-10) | Quantitative | Integer |\n| Hiring duration (weeks) | Quantitative | Float |\n| Onboarding rating (1-5) | Quantitative | Integer |\n| Recommend company (1-10) | Quantitative | Integer |\n| Current job satisfaction (1-10) | Quantitative | Integer |\n| Challenges during hiring | Qualitative | String |\n| Suggestions for onboarding | Qualitative | String |\n| Reason for leaving | Qualitative | String |\n| Improvements for satisfaction | Qualitative | String |\n\n---\n\n## 🔍 Data Analysis Methods\n\n### Quantitative Analysis:\n- Tools: Python (**pandas**, **matplotlib**), Excel, SQL\n- Techniques:\n  - **Descriptive statistics** (mean, median)\n  - **Box plots** for hiring duration vs retention\n  - **Correlation matrices**\n  - **Bar/line charts** for trends across teams\n\n### Qualitative Analysis(Can be done on strings with undefined categories):\n- Tools: LLMs (e.g., GPT), **spaCy**, **NLTK**\n- Techniques:\n  - **LLM-based categorization** of open text (e.g., reasons for leaving: Compensation, Management)\n  - **Sentiment analysis**\n  - **Word clouds** and **topic modeling** for key themes\n\n---\n\n## 📊 Visualization Examples\n- Box plot: Hiring duration vs retention\n- Bar chart: Average onboarding score by department\n- Pie chart: Categorized reasons for leaving\n- Word cloud: Common suggestions from new hires\n\n---\n\n## 📅 Timeline\n- Survey deployment: Month 1\n- Data collection and processing: Month 2\n- Analysis and reporting: Month 3\n\n---\n\n## 🛠 Tools Used\n- **Survey Tools**: Google Forms\n- **Analysis**: Python, SQL\n- **Visualization**: Matplotlib, Tableau\n- **Storage**: Internal Data Warehouse (SQL-based)\n\n---\n\n## 📬 Contact\nFor questions or contributions, reach out to **Harsh Indoria** via GitHub Issues or email at harsh.ind.coder@gmail.com.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharshindcoder%2Fpeople_analytics_case_study","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharshindcoder%2Fpeople_analytics_case_study","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharshindcoder%2Fpeople_analytics_case_study/lists"}