This project presents an exploratory data analysis of IT salaries in the United States, based on the Kaggle Salaries dataset containing over 65,000 records.
The analysis focuses exclusively on US-based roles (over 90% of observations) to provide a clearer view of salary trends, experience levels and work modes in the American tech market.
All visualizations and analyses were performed using R.
- Kaggle: Salaries dataset (IT roles)
- Scope limited to United States only
- R
- Data manipulation and aggregation
- Statistical summaries
- Data visualization (boxplots, violin plots, trend charts)
- Comparison of salary distributions across experience levels (Entry, Mid, Senior)
- Median salary growth between career stages
- Variability of salaries at entry level
- Comparison of salaries for:
- On-site roles
- Fully remote roles
- Distribution analysis using violin plots
- Median salary trends from 2020 to 2025
- Identification of growth and slowdown patterns
- Transition from Entry-level to Mid-level results in a ~42.5% increase in median salary
- Entry-level salaries show the highest variability, indicating strong dependence on company and location
- Career progression remains the most effective way to increase earnings
- Salary distributions for on-site and fully remote roles are nearly identical
- No systematic “remote penalty” observed in the US tech market
- Strong salary growth observed between 2020–2024
- A noticeable decline in median salary appears in 2025 data
- This may indicate:
- market slowdown, or
- incomplete data for the early part of 2025
This project is part of my data analytics portfolio and demonstrates:
- exploratory data analysis skills,
- ability to derive career- and business-relevant insights,
- practical use of R for real-world datasets,
- interpretation of trends and distributions in labor market data.