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SUBHAYAN KUNDU

Data Analyst

+91 8335869388 [email protected]

Resume

Hello, I am

SUBHAYAN KUNDU.

1

Excel Project

1

PowerBI Project

1

SQL Project

1

Python Project

About Me

I am a Data Analyst with hands-on experience in Excel, SQL, Power BI, and Python, focused on transforming raw data into actionable business insights.

With a background in Computer Science & Engineering, I developed a strong interest in data analysis and problem-solving. I have worked on real-world datasets, performing data cleaning, exploratory and statistical analysis, and building interactive dashboards.

My projects include sales analytics, business intelligence, and performance analysis, where I identified trends and delivered insights to support decision-making.

I also completed a Data Analyst internship, where I used SQL and Power BI to analyze data and develop dashboards for reporting and insights.

I enjoy working on end-to-end data workflows and am actively seeking opportunities to contribute as a Data Analyst.

Key Skills

Data Visualization

Power BI

Microsoft Excel (Advanced Excel)

Power Query

DAX

Data Cleaning

Data Modeling

MySQL

python

Pandas

Numpy

EDA

matplotlib

seaborn

My Projects

My Experience

Data Analyst Intern

Besant Technologies (Remote) | Oct 2025 – Nov 2025

• Cleaned and transformed 840+ accident records using SQL & Power Query

• Built an interactive Power BI dashboard to identify high-risk zones

• Optimized DAX measures and data model for better performance

• Generated insights to support decision-making and risk mitigation

Tools: Power BI, SQL, Power Query, DAX

Awards & Certificate

Excel: Mother of Business Intelligence

Get Job Ready: Power BI Data Analytics for All Levels 3.0

SQL Beginner to Advanced For Data Professionals

Python: Beginner to Advanced For Data Professionals

SQL (Intermediate) Certificate

SQL (Intermediate) Certificate

Data Analytics Internship Certificate – Besant Technologies

Data Analytics Internship Certificate – Besant Technologies

Let's Connect

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

[email protected]

https://www.linkedin.com/in/subhayan-kundu/

+ 91 8335869388

https://github.com/Sub02

Python Project

Hospitality Revenue & Occupancy Analysis | Python EDA

Domain/Function: Hospitality Analytics

Performed end-to-end EDA on hospitality data using Python. Cleaned data, handled outliers, and created KPIs like occupancy rate. Identified revenue trends, city performance, and demand patterns to generate business insights.

View Project
Engagement

View Project
on GitHUB

View Project
on GitHub

Other Tools

Pandas

NumPy

Matplotlib

Hospitality Revenue & Occupancy Analysis | Python EDA

Analysis of revenue distribution across booking platforms showing high dependency on third-party channels (~40%) and strong contribution from MakeMyTrip (~20%).

Hospitality Revenue & Occupancy Analysis | Python EDA

Comparison of weekday and weekend occupancy showing significantly higher weekend demand (~72% vs ~51%), highlighting opportunities for pricing optimization and demand management.

Project Details

This project focuses on analyzing hospitality booking data to uncover insights related to revenue, occupancy, and customer behavior.

I performed data cleaning, handled missing values, and removed outliers using statistical methods. Feature engineering was applied to create key metrics such as occupancy percentage.

Using Python libraries like Pandas and Matplotlib, I conducted exploratory data analysis to identify trends across cities, hotel categories, and booking platforms.

Key findings include higher weekend occupancy (~72% vs ~50% weekday), Mumbai generating the highest revenue, and luxury hotel categories contributing the most revenue.

These insights can help businesses optimize pricing strategies, improve occupancy, and make data-driven decisions.

Project video

PowerBI Project

Business-Insights-360-PowerBI

Domain/Function: Enterprise Business Intelligence (Finance, Sales, Marketing & Supply Chain Analytics)

Developed an enterprise Power BI solution analyzing 1.8M+ records across Finance, Sales, Marketing, and Supply Chain. Built 5 interactive dashboards using Power Query, DAX, and star-schema modeling to deliver KPI tracking, profitability analysis, and executive-level insights.

View Project
Engagement

View Project
on GitHUB

Other Tools

Power Query

DAX

SQL

Excel

Business-Insights-360-PowerBI

Designed a star-schema data model integrating fact and dimension tables to analyze 1.8M+ records, enabling efficient DAX calculations, optimized query performance, and scalable Power BI reporting.

Business-Insights-360-PowerBI

Developed a central BI 360 navigation hub providing unified access to Finance, Sales, Marketing, Supply Chain, and Executive dashboards, enabling cross-functional KPI monitoring and faster business insights.

Project Details

Developed an enterprise Business Intelligence solution using Power BI to analyze 1.8M+ transactional records for AtliQ Hardware, replacing fragmented Excel-based reporting with a centralized analytics platform supporting Finance, Sales, Marketing, Supply Chain, and Executive decision-making.

Designed a star-schema data model integrating multiple datasets and implemented Power Query for ETL transformations and DAX measures for KPI calculations. Built 5 interactive dashboards (Finance, Sales, Marketing, Supply Chain, Executive) to track revenue growth, gross margin %, net profit, customer performance, and regional market trends.

Enabled real-time KPI monitoring and cross-functional insights, helping leadership identify top-performing products, underperforming markets, and operational bottlenecks for data-driven strategic decisions.

Project video

Live Interactive Dashboard

SQL Project

sales-supply-chain-sql-analysis

Domain/Function: Sales & Supply Chain Analytics

Performed end-to-end analysis on a multi-table dataset (~7M+ records) using SQL. Applied star schema modeling, created a custom fact table for forecast vs actual analysis, and used joins, CTEs, and window functions to extract key business insights in sales and supply chain.

View Project
Engagement

View Project
on GitHUB

View Project
on GitHub

Other Tools

MySQL

Data Modeling (Star Schema)

Excel (for validation)

sales-supply-chain-sql-analysis

SQL query using CTE, joins, aggregations, and conditional logic to calculate forecast error and accuracy, enabling analysis of customer-level performance in sales and supply chain.

sales-supply-chain-sql-analysis

Output displaying top customers based on forecast accuracy, with key metrics such as total sales, forecast error, and accuracy percentage across different markets.

Project Details

This project focuses on analyzing sales and supply chain performance using SQL on a large multi-table dataset (~7M+ records).

I worked with fact and dimension tables following a star schema design and created a custom fact table (fact_act_est) by backfilling data to align forecast and actual values for accurate analysis.

Using SQL techniques such as joins, aggregations, CTEs, subqueries, and window functions, I performed analysis on sales trends, customer contribution, product performance, and channel distribution.

The project helped identify key insights such as strong product growth, high dependency on key customers, and dominant contribution from the retail channel.

This project demonstrates my ability to work with structured data, design efficient queries, and translate data into actionable business insights.

Project video

Excel Project

Excel-Sales-Analytics

Domain/Function: FMCG

Analyzed ~800K FMCG sales transactions across 23 markets, 298 products, and 189 customers. Built an interactive Excel dashboard using Pivot Tables, Power Query, and slicers to evaluate revenue trends, customer contribution, and market performance.

View Project
Engagement

View Project
on GitHUB

View Project
on GitHub

Other Tools

POWER QUERY

Pivot Tables

Slicers

Excel-Sales-Analytics

• Highlighted regional revenue contribution trends across multiple markets. • Enabled quick identification of growth opportunities and underperforming regions.

Excel-Sales-Analytics

• Identified top-performing customers driving the highest revenue share. • Detected high-value customer segments using sales growth analysis.

Project Details

Dataset: 800K+ Transactions | Markets: 23 | Products: 298 | Customers: 189

Developed an interactive Sales Analytics Dashboard in Microsoft Excel to analyze large-scale FMCG sales data and uncover revenue trends, customer contribution, and market performance insights.

Key Analysis

Evaluated Revenue Trends across 23 markets to identify growth patterns.

Analyzed Customer Net Sales Performance to detect high-value revenue contributors.

Assessed Market Performance vs Targets to identify underperforming regions.

Identified Top 10 Products by Revenue to understand product demand.

Tools & Techniques

Microsoft Excel (Advanced)

Pivot Tables

Power Query

Slicers & Interactive Filters

KPI Tracking & Data Visualization

Business Impact

Enabled stakeholders to monitor sales performance faster, identify revenue drivers, and detect underperforming markets, supporting data-driven sales strategy and product portfolio decisions.

Project video