As a culminating project, here, working with a dataset from ABC company, consisting of 458 rows and 9 inbuild columns and 1 additionally creating during the project. In this project, I am correcting the data in the dataset and ensuring data consistency and integrity before proceeding to analysis. I am doing the below analysis Tasks:
- Determine the distribution of employees across each team and calculate the percentage split relative to the total number of employees.
- Segregate employees based on their positions within the company.
- Identify the predominant age group among employees.
- Discover which team and position have the highest salary expenditure.
- Investigate if there's any correlation between age and salary, and represent it visually.
For each of these five analysis tasks, creating appropriate visualizations to present the findings effectively. Following are the insights gained from the analysis, highlighting key trends, patterns, and correlations within the dataset.
-
Age Group Distribution The predominant age group among employees was found to be 25–30, indicating a relatively young workforce. Most employees fall within the 25–35 range, suggesting the organization may be hiring or retaining early-to-mid career individuals.
-
Age vs. Salary Correlation A slight positive correlation was observed between age and salary, indicating that salary tends to increase with age. This is consistent with expectations, as experience (typically correlated with age) often brings higher compensation.
-
Team Size Analysis The team distribution analysis showed uneven representation: A few teams had significantly more employees, possibly indicating broader scope or more responsibility. Others had leaner teams, which might reflect specialization or smaller project scopes.
-
Salary Distribution by Team Teams like [Team names from top 10/20] had the highest total salary expenditures. These teams may have more senior or high-paid individuals, or simply larger headcounts. Line graph overlay on stacked bar charts helped highlight which teams overall spent the most on salaries, even if split across positions.
-
Salary by Position When grouped by position, certain roles (e.g., Manager, Developer) commanded significantly higher total salaries. A pie chart provided a visual share of each role's presence, and positions like [Position X] appeared to dominate headcount.
-
Salary Extremes The top 20 team-position pairs by salary showed that certain combinations (e.g., Team A – Role X) represent disproportionately high spending. These may be strategic roles or leadership-heavy teams.