{"id":23592790,"url":"https://github.com/tushar2704/hiring-process-analytics","last_synced_at":"2026-01-25T10:32:38.100Z","repository":{"id":188022284,"uuid":"677975112","full_name":"tushar2704/Hiring-Process-Analytics","owner":"tushar2704","description":"In this project, I am analyzing hiring process data to gain insights from about records of previous hires within a multinational company. 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By analyzing this data, I am aiming to uncover valuable trends and information about the company's hiring process, which can contribute to making informed decisions and improvements for the future.\n\n## Project Overview\n\nAs a data analyst data wrangling is important. This involves handling missing data, simplifying the dataset, detecting and dealing with outliers, and summarizing your findings. Therefore, the main goal is to provide meaningful conclusions that can help the company enhance its hiring process and decision-making.\n## Project Structure\n\nThe project repository is organized as follows:\n\n```\n\n├── LICENSE\n├── README.md          \u003c- README .\n\n├── reports            \u003c- Folder containing the final reports/results of this project.\n│   │\n│   └── Report.pdf     \u003c- Final analysis report in PDF.\n│   \n├── src                \u003c- Source for this project.\n│   │\n│   └── data           \u003c- Datasets used and collected for this project.\n\n```\n\n## Project Tasks\n\n### 1. Handling Missing Data\n\nInspecting the dataset for any missing values and determine the most suitable strategy for handling them. This might involve filling in missing values with appropriate alternatives or removing records with missing data, depending on the situation.\n\n### 2. Clubbing Columns\n\nSimplifying the dataset by combining categories within columns, where applicable. This step can streamline your analysis and help in drawing more accurate conclusions.\n\n### 3. Outlier Detection and Removal\n\nIdentifying potential outliers in the dataset that could skew your analysis. Deciding on the appropriate approach for dealing with these outliers, whether it's removing them, replacing them with more representative values, or leaving them as they are.\n\n### 4. Data Summary\n\nAfter cleaning and preparing the data, summarizing the findings. Calculating relevant statistical measures such as averages, medians, and other metrics. Utilizing visualizations such as charts and graphs to enhance the understanding of the data.\n\n## Data Analytics Tasks\n\nTo tackle the project, follow these data analytics tasks using Excel:\n\n### A. Hiring Analysis\n\nDetermining the gender distribution of hires. Calculating the number of males and females that have been hired by the company.\n\n### B. Salary Analysis\n\nCalculating the average salary offered by the company using Excel functions.\n\n### C. Salary Distribution\n\nCreating class intervals for the salaries in the company. This will help in visualizing and understanding the distribution of salaries.\n\n### D. Departmental Analysis\n\nUtilizing a suitable visualization, such as a pie chart or bar graph, to represent the proportion of employees working in different departments.\n\n### E. Position Tier Analysis\n\nUsing a chart or graph to illustrate the distribution of different position tiers within the company.\n\n\n## Insights\n\n- Most hired individuals are males, 6% prefer not to state gender.\n\n- Avg. Total Salary offered without Outliers is $49878\n\n- Avg. Total Salary offered with Outliers is $49976\n\n- 2892 employees had salary below $60000 out of total 4697 hired.\n\n- 1802 employees had salary above $60000 and below $100000 out of total 4697 hired.\n\n- Operation department had highest hires with 1843, followed by Service department with 1332 .\n\n- Same trend for department for all non hired individual as well.\n\n- Post c5 \u0026 c9 had the highest hired individuals, majority were males.\n\n- Post m6, m7, n6, n9, n10 were almost non existence.\n\n- Post c5 \u0026 c9 had majority of employees with salary below $40000.\n\n- Post c5 \u0026 c9 employees mostly belonged to Operations Department.\n\n- Service department also followed the same trends.\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE).\n## Author\n- \u003cins\u003e\u003cb\u003e©2023 Tushar Aggarwal. All rights reserved\u003c/b\u003e\u003c/ins\u003e\n- \u003cb\u003e[LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)\u003c/b\u003e\n- \u003cb\u003e[Medium](https://medium.com/@tushar_aggarwal)\u003c/b\u003e \n- \u003cb\u003e[Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)\u003c/b\u003e\n- \u003cb\u003e[New Kaggle](https://www.kaggle.com/tagg27)\u003c/b\u003e \n\n## Contact me!\nIf you have any questions, suggestions, or just want to say hello, you can reach out to us at [Tushar Aggarwal](mailto:info@tushar-aggarwal.com). We would love to hear from you!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushar2704%2Fhiring-process-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftushar2704%2Fhiring-process-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushar2704%2Fhiring-process-analytics/lists"}