I translate complex datasets into high-impact business strategies. With a background in Mechanical Engineering, I bring a rigorous analytical framework to the world of data — specializing in full-stack analytics from SQL architecture to advanced BI visualization.

• 📊 Strategic Data Analysis: Focused on identifying revenue patterns and optimizing operational workflows.
• 🐍 Technical Stack: Python (Pandas, NumPy, Matplotlib, Seaborn) and SQL querying for relational databases.
• 📈 Business Intelligence: Experienced in building dual-platform dashboards using both Tableau and Power BI.
• ⚙️ Engineering Mindset: Applying first-principle thinking to solve real-world data problems.
• 📍 Based in Alipurduar, West Bengal, India.

Built a comparative analysis to study the impact of social media on student wellness and academic performance.

• Outcome: Confirmed a -0.95 correlation between addiction score and mental health — a near-perfect inverse relationship. High School students average only 5.46 hrs of sleep per night, the most vulnerable group across all academic levels. India and USA lead globally in cumulative addiction scores.
• Innovation: Developed identical, synced dashboards in both Tableau and Power BI to demonstrate tool-agnostic proficiency across 705 students from multiple countries.
• 🔗 Tableau Dashboard | 🔗 GitHub Repo

Built a Python-based RFM model to segment a 1,000-customer database and identify churn risk.

• Outcome: Segmented all 1,000 customers across 5 behavioral groups. Identified 207 "At Risk" customers (20.7%) with over ₹53.5L in recoverable revenue, and 273 "Hibernating" customers as the largest re-engagement opportunity from a total transaction base of ₹2.3Cr.
• Technique: Automated Recency, Frequency, and Monetary scoring using Python (Pandas, qcut quintile scoring), with custom segment labeling logic and visualization via Matplotlib and Seaborn.
• 🔗 GitHub Repo

Analyzing 526,000+ property transactions across 50 Connecticut towns (2011–2021) to guide investment and infrastructure decisions.

• Outcome: Identified a confirmed $40.29B surge in sales volume in 2020 — the highest in the entire 11-year dataset — with Single Family properties dominating at 273,000+ transactions. Mapped regional hotspots for targeted development planning.
• Technique: Year-over-year trend analysis, property-type breakdown, and geospatial visualization in Tableau across a large-scale real estate dataset.
• 🔗 Tableau Dashboard | 🔗 GitHub Repo

Constructed a normalized relational database from the ground up to solve inventory visibility and customer engagement gaps.

• Outcome: Built a complete 40+ query solution library across 6 progressive levels — from basic filtering to correlated subqueries and set operations — enabling automated identification of dead stock and high-value customers.
• Technique: Advanced Subqueries, Multi-table JOINs (INNER, LEFT, RIGHT), Aggregations, GROUP BY, and Set Operations in MySQL across a 6-table retail schema (customers, orders, products, order_items, payments, product_reviews).
• 🔗 GitHub Repo

• LinkedIn: Gopal Sarkar
• Visualization Hub: Tableau Public Profile