The Skill–Salary Correlation Study project focuses on understanding how various skills, experience levels, and educational backgrounds influence income across different industries and job roles. The objective of this project is to use real-world job and salary datasets to identify which skills deliver the highest return on investment in the job market and help professionals make data-driven career decisions. The project involves collecting and preparing data from sources such as Kaggle’s Data Science Salaries or Stack Overflow Developer Survey, Glassdoor reports, and LinkedIn job postings. The dataset includes information such as job title, skill set, years of experience, and annual salary. After cleaning and standardizing the data, binary indicators are created for top skills, allowing deeper comparison across professions. Exploratory data analysis (EDA) is conducted to compute and visualize average salaries by skill, experience level, and industry using Python libraries like pandas, matplotlib, and seaborn. Visualizations such as bar charts, heatmaps, and bubble plots highlight top-paying skills and combinations. For example, professionals with Python, SQL, and Tableau skills tend to earn significantly higher salaries than those with traditional tools like Excel. A simple linear regression model is built to predict salary based on key features like skills, experience, and industry, allowing quantitative assessment of each factor’s contribution. The model’s coefficients and R² score help identify which skills have the greatest financial impact. Finally, the project concludes with clear, actionable insights and career recommendations — showing which skill sets provide the best salary growth potential and how professionals can strategically upskill. Overall, the Skill–Salary Correlation Study demonstrates how data analytics can bridge the gap between education and employability, offering valuable intelligence for job seekers, educators, and industry leaders.