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Job Role Impact on Employee Retention
Data Science and Analytics
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About
This dataset provides a detailed analysis of an organisation's employees, focusing on key aspects such as employee attrition, personal and job-related factors, and financials. It includes numerous parameters like age, gender, marital status, business travel frequency, daily rate of pay, departmental information (e.g., distance from home office, education level), and job-related details such as job involvement, job level, and specific job role. Further aspects cover salary hike percentages, performance ratings, relationship satisfaction, monthly income, and the number of companies previously worked for. Ultimately, it highlights the attrition status of employees, offering insight into modern workforce management philosophies and how they have evolved due to technological advancements.
Columns
- Age: The age of the employee. (Numerical)
- Attrition: Whether or not the employee has left the organisation. (Categorical)
- BusinessTravel: The frequency of business travel for the employee. (Categorical)
- DailyRate: The daily rate of pay for the employee. (Numerical)
- Department: The department the employee works in. (Categorical)
- DistanceFromHome: The distance from home in miles for the employee. (Numerical)
- Education: The level of education achieved by the employee. (Categorical)
- EducationField: The field of study for the employee's education. (Categorical)
- EmployeeCount: The total number of employees in the organisation. (Numerical)
- EmployeeNumber: A unique identifier for each employee profile. (Numerical)
- EnvironmentSatisfaction: The employee's satisfaction with their work environment. (Categorical)
- Gender: The gender of the employee. (Categorical)
- HourlyRate: The hourly rate of pay for the employee. (Numerical)
- JobInvolvement: The level of involvement required for the employee's job. (Categorical)
- JobLevel: The job level of the employee. (Categorical)
- JobRole: The role of the employee in the organisation. (Categorical)
- JobSatisfaction: The employee's satisfaction with their job. (Categorical)
- MaritalStatus: The marital status of the employee. (Categorical)
- MonthlyIncome: The monthly income of the employee. (Numerical)
- MonthlyRate: The monthly rate of pay for the employee. (Numerical)
- NumCompaniesWorked: The number of companies the employee has worked for. (Numerical)
- Over18: Whether or not the employee is over 18. (Categorical)
- OverTime: Whether or not the employee works overtime. (Categorical)
- PercentSalaryHike: The percentage of salary hike for the employee. (Numerical)
- PerformanceRating: The performance rating of the employee. (Categorical)
- RelationshipSatisfaction: The employee's satisfaction with their relationships. (Categorical)
- StandardHours: The standard hours of work for the employee. (Numerical)
- StockOptionLevel: The stock option level of the employee. (Numerical)
- TotalWorkingYears: The total number of years the employee has worked. (Numerical)
- TrainingTimesLastYear: The number of times the employee was taken for training in the last year. (Numerical)
- WorkLifeBalance: The employee's perception of their work-life balance. (Categorical)
- YearsAtCompany: The number of years the employee has been with the company. (Numerical)
- YearsInCurrentRole: The number of years the employee has been in their current role. (Numerical)
- YearsSinceLastPromotion: The number of years since the employee's last promotion. (Numerical)
- YearsWithCurrManager: The number of years the employee has been with their current manager. (Numerical)
Distribution
The dataset is typically provided in a CSV data file, specifically named
HR_Analytics.csv.csv. It has a file size of 227.98 kB and contains 35 distinct columns. Each column has 1470 valid records, with no mismatched or missing values reported across the listed columns.Usage
This dataset is ideal for:
- Understanding variables that contribute to employee attrition.
- Analysing data for patterns, outliers, or anomalies at individual or aggregated levels.
- Visualising data using charts and graphs to easily identify relationships influencing employee departures, particularly considering factors like age or job role.
- Exploring relationships between pairs of variables through correlation analysis to understand their influence on employment retention.
- Utilising descriptive analytics methods such as scatter plots, histograms, and box plots with aggregated values to gain deeper insights.
- Applying predictive analytics techniques like regressions, clustering, and decision trees to identify trends from past data points and build models to prepare organisations against potential high levels of employee departure.
- Identifying performance profiles of employees at risk for attrition to create personalised development or retention strategies.
- Assessing the impact of financial incentives or changes in job roles/structures on employee attitudes, satisfaction, and attrition rates.
- Analysing how different age groups respond to perks or exhibit turnover patterns to better engage diverse demographic segments.
Coverage
The dataset focuses on the employees of an organisation. While no specific geographic location is mentioned, the data points relate to employee characteristics and work experiences within this organisational context. It covers various demographic attributes including Age (ranging from 18 to 60), Gender (60% Male, 40% Female), and Marital Status (46% Married, 32% Single). It also includes details about education levels and fields of study. The data implicitly covers a period relevant to an employee's tenure and recent activities within the company, such as "TrainingTimesLastYear".
License
CC0 - Public Domain
Who Can Use It
- Researchers studying workforce dynamics and human resources.
- HR Professionals seeking to understand and mitigate employee turnover.
- Business Analysts interested in identifying factors affecting employee retention and organisational performance.
- Data Scientists and Machine Learning Engineers developing predictive models for attrition risk.
- Organisational Leaders and Managers looking to improve employee engagement and satisfaction.
Dataset Name Suggestions
- Employee Attrition Factors Analysis
- Workforce Retention Insights Dataset
- HR Analytics Employee Turnover Drivers
- Employee Performance and Financials Data
- Job Role Impact on Employee Retention
Attributes
Original Data Source: Job Role Impact on Employee Retention
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