Human Resources Dashboard
Executive Summary
In this project, the Human Resources manager at XYZ and Co. has requested a comprehensive dashboard to gain insights on the company's hiring. They require a summary view with high-level insights and a detailed record of every member, past and present, at the company
Methodology
I prompted ChatGPT to write a python script using the 'faker' library to generate a realistic dataset of 7,777 records for human resources. The dataset included the following attributes:
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Employee ID
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First Name
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Last Name
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Gender: 46% probability for ‘Female’ and a 54% probability for ‘Male’
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Province and City: Randomly assigned from a predefined list of states and their cities.
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Hire Date: Custom probabilities for each year from 2015 to 2024.
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Department: Randomly chosen from a list of departments with specified probabilities.
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Job Title: Randomly selected based on the department, with specific probabilities for each job title within the department.
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Education Level: Determined based on the job title, chosen from a predefined mapping of job titles to education levels.
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Performance Rating: Randomly selected from ‘Excellent’, ‘Good’, ‘Satisfactory’, ‘Needs Improvement’ with specified probabilities.
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Overtime: 30% probability for ‘Yes’ and a 70% probability for ‘No’.
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Salary: Generated based on the department and job title, within specific ranges.
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Birth Date: Generated based on age group distribution and job title requirements, ensuring consistency with the hire date.
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Termination Date: Assigned to a subset of employees (11.2% of the total) with specific probabilities for each year from 2015 to 2024, ensuring the termination date is at least 6 months after the hire date.
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Adjusted Salary: Calculated based on gender, education level, and age, applying specific multipliers and increments.
Overview
Summary Dashboard
The Overview section provided a snapshot of the overall HR metrics:
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Displayed the total number of hired employees, active employees, and terminated employees.
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Visualized the total number of hired and terminated employees over the years.
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Presented a breakdown of total employees by department and job titles.
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Showed the distribution of employees by city and province.
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Demographics
The Demographics section offered insights into the composition of the workforce, including:
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Presented the gender ratio in the company.
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Visualized the distribution of employees across age groups and education levels.
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Showed the total number of employees within each age group.
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Showed the total number of employees within each education level.
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Presented the correlation between employees' educational backgrounds and their performance ratings.
The Income section focused on salary-related metrics, including:
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Compared salaries across different education levels for both genders to identify any discrepancies or patterns.
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Presented how age correlated with salary for employees in each department.
Employee Records Sheet
Provided a comprehensive list of all employees with necessary information such as name, department, position, gender, age, education, and salary. Users were able to filter the list based on any of the available columns.