{"id":30360497,"url":"https://github.com/apostolis-bloutsos-data/employee-data-eda","last_synced_at":"2026-05-09T03:38:18.282Z","repository":{"id":309187398,"uuid":"1035454860","full_name":"apostolis-bloutsos-data/employee-data-eda","owner":"apostolis-bloutsos-data","description":"Mini EDA project on synthetic employee records using Python, pandas, and matplotlib","archived":false,"fork":false,"pushed_at":"2025-08-10T12:48:12.000Z","size":103,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-19T14:39:56.320Z","etag":null,"topics":["data-analysis","eda","jupyter-notebook","matplotlib","pandas","python","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/apostolis-bloutsos-data.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-08-10T12:44:23.000Z","updated_at":"2025-08-10T12:49:58.000Z","dependencies_parsed_at":"2025-08-10T13:13:50.046Z","dependency_job_id":null,"html_url":"https://github.com/apostolis-bloutsos-data/employee-data-eda","commit_stats":null,"previous_names":["apostolis-bloutsos-data/employee-data-eda"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/apostolis-bloutsos-data/employee-data-eda","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apostolis-bloutsos-data%2Femployee-data-eda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apostolis-bloutsos-data%2Femployee-data-eda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apostolis-bloutsos-data%2Femployee-data-eda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apostolis-bloutsos-data%2Femployee-data-eda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/apostolis-bloutsos-data","download_url":"https://codeload.github.com/apostolis-bloutsos-data/employee-data-eda/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apostolis-bloutsos-data%2Femployee-data-eda/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32806462,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T08:22:46.396Z","status":"online","status_checked_at":"2026-05-09T02:00:06.633Z","response_time":123,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["data-analysis","eda","jupyter-notebook","matplotlib","pandas","python","seaborn"],"created_at":"2025-08-19T14:22:54.795Z","updated_at":"2026-05-09T03:38:18.262Z","avatar_url":"https://github.com/apostolis-bloutsos-data.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Employee Data EDA\n\nThis repository contains a **mini exploratory data analysis (EDA)** project on a small synthetic dataset of employee records.  \nThe goal is to demonstrate **data cleaning, grouping, summarization, and visualization** using Python’s **pandas** and **matplotlib** libraries.\n\n---\n\n##  Project Overview\nThe dataset includes:\n- Employee ID and Name\n- Department\n- Age\n- Salary\n- Start Date\n- Gender\n\nThe analysis covers:\n1. Inspecting and understanding the dataset\n2. Handling missing values (comparison of dropping vs imputation)\n3. Grouping and aggregating data to extract insights\n4. Creating simple visualizations for clarity\n5. Summarizing findings in business-friendly terms\n\n---\n\n##  Key Insights\n- Finance employees have the highest average salary and age.\n- IT is the largest department but has the lowest average salary.\n- HR and IT departments are currently single-gender; Finance is gender-balanced.\n- Missing values were **imputed** instead of dropped to preserve all records.\n- IT salaries have the widest range, HR salaries are the most compact.\n\n---\n\n##  Repository Structure\nemployee-data-eda/\n\n│── employee_data_insights_eda.ipynb # Jupyter notebook with the full analysis\n    \n    └── README.md # Project description and findings\n\n## View the Notebook\nYou can view the full analysis here:  \n[Employee Data Insights EDA Notebook](https://github.com/apostolis-bloutsos-data/employee-data-eda/blob/main/employee_data_insights_eda.ipynb)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapostolis-bloutsos-data%2Femployee-data-eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapostolis-bloutsos-data%2Femployee-data-eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapostolis-bloutsos-data%2Femployee-data-eda/lists"}