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Integrated Data Analysis and Visualization Project for Employee Data using Python, and SQL.

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Integrated Data Analysis and Visualization Project for Employee Data

In this data analysis project, data preprocessing and data visualization task also performed for Employees table.

Data: The Employees table contains six tables such as department, department_employee, department_manager, employee, salary, and title table. Each of those includes information about employees such as employee_id, first_name, last_name, salary, title, department_id, etc.

In this project few SQL queries solved to answer the given questions, all questions are given below:

  1. Check for data inconsistency.
  2. Which department has the highest average salary of active employees ? Give some plots to show the avg salary department-wise.
  3. Which title has the highest avg salary? Give some plots to show the avg salary title-wise.
  4. Distribution of salary across titles.
  5. Distribution of salary across departments.
  6. How many active managers in each department. Is there any department with no manager?
  7. Composition of titles department-wise. Appropriate plots.
  8. Composition of departments title-wise. Appropriate plots.
  9. Salaries of active department managers. Which department's manager who is active earns the most?
  10. What are the titles of active department managers? Are they managers only?
  11. Past history of salaries of managers across department (yearly)
  12. Distribution of salaries of active employees working for more than 10 years vs 4 years vs 1 year.
  13. Average number of years employees work in the company before leaving (title wise).
  14. Average number of years employees work in the company before leaving (Dept wise).
  15. Median annual salary increment department wise.
  16. Retrieve employees who are also managers.
  17. Average salaries with department wise and appropritae plot. Find employees who earn more than their department's average salary.
  18. Find the employee(s) with the highest salary in each department.

Note: Please find attached employees_schema.sql and employee_dump.sql files for performing this data analysis task.