Overview The Skill–Salary Correlation Study is a data analytics project designed to explore how technical skills, experience levels, job roles, and industry sectors influence salary outcomes in the data science and technology fields. It implements a complete end-to-end data analysis pipeline that converts raw salary data into meaningful insights through data processing, statistical modeling, and interactive visualization. The project serves as a decision-support system for professionals, organizations, and educators who want to understand which skills command higher salaries, how experience impacts earnings, and which skill combinations hold the highest market value. Objectives Identify technical skills associated with higher compensation levels. Quantify the monetary value of individual and combined skills. Analyze how experience affects salary growth across roles. Examine industry-specific salary patterns and trends. Generate actionable insights for career planning and recruitment. Provide an interactive dashboard for visual exploration of salary data. Technical Architecture The project is organized into three main layers: data processing, statistical modeling, and interactive visualization. Data Processing The data preparation component cleans, normalizes, and structures datasets from multiple sources such as Kaggle or GitHub. It manages missing values, standardizes salary figures across currencies, extracts features from job listings, and exports ready-to-analyze datasets for modeling. Statistical Modeling Regression-based models are used to analyze how skills, experience, and industries affect salary outcomes. The system computes coefficients that represent the monetary impact of specific skills, identifies high-value skill combinations, and generates statistical summaries with performance metrics and visual outputs. Interactive Dashboard The visualization layer is built with Dash and Plotly, offering an interactive web interface where users can filter data by skills, job titles, industries, and countries. It provides dynamic charts that display salary distributions, correlations, and experience-based trends, enabling users to explore insights visually and intuitively. Key Features Works with both sample and custom datasets. Handles multiple data formats and missing values automatically. Provides one-command execution for data preparation, modeling, and dashboard deployment. Generates detailed reports, charts, and correlation analyses. Highlights top-paying skills and high-value skill combinations. Technology Stack Programming Language: Python 3.10+ Data Processing: Pandas, NumPy Statistical Modeling: Scikit-learn Visualization: Plotly, Dash, Matplotlib Data Acquisition: Kaggle API, Requests Development Tools: Jupyter Notebooks Project Structure src: Core scripts for data processing and modeling dashboard: Interactive visualization interface data: Raw and processed datasets reports: Generated charts and analytical outputs notebooks: Exploratory analysis workflows tests: Validation and testing scripts Workflow Process Data Preparation – Transform raw data into clean, analysis-ready datasets. Model Training – Build regression models to measure the impact of key factors. Report Generation – Produce visual and textual summaries of findings. Dashboard Deployment – Launch an interactive web dashboard for exploration. Use Cases For Job Seekers Discover high-value skills to focus on for career advancement. Understand salary expectations based on experience levels. Compare compensation across industries, regions, and roles. For Hiring Managers Benchmark salaries against market data. Identify cost-effective skill combinations for hiring. Analyze compensation structures across job roles. For Educational Institutions Design curricula aligned with high-demand skills. Offer students data-driven career insights. Monitor how skill valuations evolve over time. Future Scope Temporal trend analysis of changing skill valuations. Regional and company-size-based salary comparisons. Evaluation of remote work impact on compensation. Predictive modeling to forecast emerging high-value skills. Conclusion The Skill–Salary Correlation Study provides a structured, data-driven approach to understanding the economic value of skills in technology careers. By combining robust data analysis with interactive visualization, the project empowers professionals, employers, and educators to make informed decisions about skill development, hiring strategies, and career planning. Author Akash — Engineering Student and Data Enthusiast GitHub: github.com/akash-032 ## Project Overview The Skill-Salary Correlation Study is a data analytics project designed to investigate and quantify the relationships between technical skills, experience levels, job roles, industry sectors, and salary outcomes in the technology and data science fields. This project implements a complete end-to-end data analysis pipeline that transforms raw salary data into actionable insights through statistical modeling and interactive visualization. ## Project Objectives 1. Identify which technical skills correlate with higher salaries in the job market 2. Quantify the monetary value of specific skills and skill combinations 3. Analyze how experience levels impact compensation across different roles 4. Determine industry-specific salary trends for various technical positions 5. Generate actionable insights for professionals planning career development 6. Provide an interactive tool for exploring salary data across multiple dimensions ## Technical Architecture The project follows a modular architecture with distinct components: ### Data Processing Pipeline The data preparation module (`src/data_preparation.py`) handles: - Raw data acquisition from multiple sources (local files, Kaggle API, GitHub) - Data cleaning and normalization of salary information - Feature extraction from job descriptions and skill listings - Generation of derived metrics like skill combinations and experience bins - Export of processed datasets for analysis and visualization ### Statistical Modeling The modeling component (`src/model.py`) performs: - Multivariate regression analysis to isolate the impact of individual factors - Calculation of skill coefficients representing monetary value - Identification of high-value skill combinations - Generation of statistical reports and model performance metrics - Creation of data visualizations showing key relationships ### Interactive Dashboard The visualization layer (`dashboard/app.py`) provides: - A web-based interface built with Dash and Plotly - Interactive filters for skills, job titles, industries, and countries - Dynamic charts showing salary distributions and correlations - Responsive design for desktop and mobile viewing - Real-time data exploration capabilities ## Key Features ### Data Handling Capabilities - Works with included sample data or custom datasets - Handles missing data through intelligent imputation - Normalizes salaries across different currencies and regions - Automatically acquires data when needed (Kaggle, GitHub, or synthetic) - Processes various data formats and column structures ### Analysis Outputs - Skill-specific salary analysis showing the value of individual skills - Skill combination analysis identifying valuable skill pairings - Experience-salary relationship charts showing career progression - Industry and role-specific salary benchmarks - Correlation matrices showing relationships between different factors ### Visualization Components - Coefficient impact charts showing the monetary value of skills - Salary by skill bar charts for direct comparisons - Salary vs. experience trend lines with confidence intervals - Skill combination heat maps showing synergistic effects - Correlation matrices for identifying related skills ### Reports and Insights - Model summary reports with statistical performance metrics - Top insights highlighting the most significant findings - Data-driven recommendations for skill development - Project status reports tracking analysis progress - Publication-ready charts for presentations and sharing ## Implementation Details ### Technologies Used - **Programming Language**: Python 3.10+ - **Data Processing**: Pandas, NumPy - **Statistical Modeling**: Scikit-learn - **Visualization**: Matplotlib, Seaborn, Plotly, Dash - **Data Acquisition**: Kaggle API, Requests - **Development Tools**: Jupyter Notebooks ### Project Structure - `src/`: Core project code for data processing and modeling - `dashboard/`: Interactive visualization application - `data/`: Raw and processed datasets - `reports/`: Generated insights and visualizations - `notebooks/`: Exploratory analysis workflows - `tests/`: Testing framework for quality assurance ### Workflow Process 1. **Data Preparation**: Process raw data into analysis-ready datasets 2. **Model Training**: Build regression models to quantify relationships 3. **Report Generation**: Create statistical summaries and visualizations 4. **Dashboard Deployment**: Launch interactive visualization interface ## Use Cases ### For Job Seekers - Identify high-value skills to prioritize in learning and development - Understand how experience levels translate to salary expectations - Compare compensation across different industries and regions - Discover valuable skill combinations to enhance marketability ### For Hiring Managers - Benchmark salary offerings against market rates for specific skills - Understand the premium for in-demand technical capabilities - Identify cost-effective skill combinations for team building - Compare compensation structures across different roles and industries ### For Educational Institutions - Design curricula focused on high-value skills - Provide students with data-driven career guidance - Track changes in skill valuations over time - Demonstrate the return on investment for specific learning paths ## Future Extensions The project architecture supports several potential extensions: 1. **Temporal Analysis**: Track changes in skill valuations over time 2. **Geographic Comparisons**: Analyze regional differences in compensation 3. **Company Size Analysis**: Compare salary structures across different organization sizes 4. **Remote Work Impact**: Analyze how remote work affects compensation 5. **Predictive Modeling**: Forecast future skill valuations based on trends