PROJECTS
LinkedIn Job Market Analysis
SQL • Python • Tableau
This project analyzes over 100,000 LinkedIn job postings to identify the most in-demand skills, hiring trends, and geographic patterns across data-related roles.
Using SQL and Python, I extracted, cleaned, and structured the dataset before building an interactive Tableau dashboard to explore hiring demand at scale.
The analysis highlights clear patterns in technical skill requirements, the dominance of core tools such as Excel, SQL, and Python, and the concentration of hiring activity in major metropolitan areas.
Key Insights
• Excel remains the most in-demand skill across data roles, significantly outperforming other tools and highlighting its continued importance in business environments.
• SQL and Python form the core technical foundation for data roles, consistently appearing together across job postings.
• Hiring demand is heavily concentrated in major metropolitan areas such as New York, Chicago, and San Francisco, indicating geographic clustering of data opportunities.
• A small number of companies account for a disproportionate share of job postings, suggesting concentrated hiring among large organizations and staffing firms.
• There is a clear distinction between foundational skills (Excel) and specialized technical skills (SQL, Python, Tableau), reflecting different tiers of data roles.
Interactive dashboard — hover and click to explore insights.
End-to-end data analysis project using real-world job market data.
Supply Chain Efficiency & Supplier Performance Analysis
SQL • Python • Tableau
This project analyzes supply chain performance data to evaluate supplier efficiency, product performance, and operational delays.
Using Python (Pandas), I cleaned and explored the dataset before building an interactive Tableau dashboard to visualize key supply chain metrics.
The analysis focuses on identifying inefficiencies in supplier performance, understanding revenue distribution across product categories, and uncovering the primary drivers of delays within the supply chain.
Key Insights
• Supplier 3 has the longest lead times and relatively high defect rates, making it the least efficient supplier.
• Supplier 1 offers the best balance between speed and quality, with the lowest combination of lead time and defect rate.
• Skincare products generate the highest revenue, indicating a key area for business focus and optimization.
• Manufacturing lead time is a major contributor to overall supply chain delays.
• There is a clear trade-off between speed, cost, and quality across suppliers, highlighting opportunities for operational improvement.
Interactive Dashboard
This project demonstrates the ability to transform operational data into actionable insights that improve supplier performance and overall supply chain efficiency.
E-Commerce Sales & Operational Performance Analysis
Python • Pandas • Tableau
This project analyzes e-commerce transaction data to evaluate revenue drivers, fulfillment performance, cancellation patterns, and regional sales concentration.
Using Python (Pandas), I cleaned and prepared the dataset by handling missing values, standardizing categorical fields, and fixing date formatting before building an interactive Tableau dashboard.
The analysis reveals that revenue is concentrated in Amazon fulfillment and a small number of product categories, while elevated cancellation rates in specific categories highlight operational inefficiencies and opportunities for improvement.
Key Insights
• Amazon fulfillment drives the majority of total revenue compared to merchant fulfillment.
• “Set” and “Kurta” are the strongest product categories by revenue, indicating concentration in a small number of top-performing categories.
• Categories such as “Bottom” and “Western Dress” show the highest cancellation rates, pointing to operational or customer expectation issues.
• Revenue is geographically concentrated in high-performing states such as Maharashtra and Karnataka, indicating regional demand clusters.
• Order outcomes are overwhelmingly completed, but cancellation patterns still reveal meaningful areas for operational improvement.
Interactive Dashboard
This project demonstrates the ability to transform raw operational data into actionable insights that support revenue growth and process improvement.