Piyush Sonawane

Data Analyst

[email protected] +91 9284617040

Resume

Hello, I am

Piyush Sonawane.

1

Excel
Project

1

PowerBI
Project

1

SQL
Project

1

Python
Project

About Me

I am a final-year Computer Engineering student focused on using data to support business decisions. I analyze datasets using SQL, Python, Excel, and Power BI to identify trends, monitor KPIs, and generate insights that can drive operational and strategic improvements. My projects emphasize translating raw data into clear dashboards and reports that highlight performance metrics, risks, and opportunities. I enjoy working with real-world data, understanding business context, and communicating insights in a way that is easy for stakeholders to interpret. I am currently preparing for Data Analyst roles where I can contribute to data-driven decision-making and continuous business improvement.

Key Skills

Excel

PowerBI

Python

SQL(Structured Query Language)

Exploratory Data Analysis (EDA)

Data Cleaning & Preprocessing

Data Visualization & Dashboarding

KPI Analysis & Reporting

Python (Pandas, NumPy, Matplotlib)

SQL (Joins, Subqueries, CTEs, Window Functions)

Power BI (DAX, Data Modeling, Interactive Dashboards)

Microsoft Excel (Pivot Tables, VLOOKUP/XLOOKUP, Power Query, Charts)

Git & GitHub

Data-driven Decision Making

My Projects

Customer-wise Net Sales Performance Analysis (2019–2021)

Domain/Function: Sales Analytics | Business Intelligence | Retail Performance Analysis

Business Insights 360: End-to-End Performance & Financial Analytics Dashboard

Domain/Function: Business Intelligence | Financial Analytics | Executive Reporting

Sales & Forecast Data Analysis Using SQL (Star Schema Based)

Domain/Function: Data Analytics | Database Management | Sales & Forecast Analysis

Hotel Booking Data Analysis Using Python

Domain/Function: Exploratory Data Analysis | Data Visualization | Booking Analytics

Awards & Certificate

Excel: Mother of Business Intelligence

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

Python: Beginner to Advanced For Data Professionals

SQL Beginner to Advanced For Data Professionals

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.

Mail Me

[email protected]

https://www.linkedin.com/in/piyush-sonawane-005422257/

Call Me

+91 9284617040

GitHub

https://github.com/2Piyush0

Excel Project

Customer-wise Net Sales Performance Analysis (2019–2021)

Domain/Function: Sales Analytics | Business Intelligence | Retail Performance Analysis

Analyzed multi-year customer sales data using Excel to evaluate net sales growth, customer contribution, and year-over-year performance. Built an interactive and visually intuitive sales performance report for business decision-making.

View Project
on GitHUB

Other Tools

Microsoft Excel

Pivot Tables

Power Query

Conditional Formatting

Advanced Excel Formulas

Customer-wise net sales dashboard displaying sales values for 2019, 2020, and 2021 with conditional formatting to highlight revenue distribution and year-over-year growth percentages.

Extended customer sales analysis showing aggregated net sales, growth trends, and total revenue contribution using pivot tables and visual indicators for quick comparison.

Project Details

This project focuses on analyzing customer-wise net sales performance for Atliq Hardwares from 2019 to 2021 using Microsoft Excel. The dataset includes sales figures across multiple customers, regions, markets, and divisions. Pivot tables and Power Query were used to clean, transform, and structure the data efficiently. Conditional formatting and data bars were applied to highlight high-performing customers and year-over-year growth trends. The analysis enables stakeholders to compare sales performance across years, identify top revenue-generating customers, and evaluate percentage growth between 2020 and 2021. The final report provides a clear and interactive view of sales insights, supporting data-driven business decisions such as customer prioritization and performance monitoring.

PowerBI Project

Business Insights 360: End-to-End Performance & Financial Analytics Dashboard

Domain/Function: Business Intelligence | Financial Analytics | Executive Reporting

Developed an interactive Power BI dashboard providing 360° business insights across finance, sales, marketing, and supply chain. Enabled stakeholders to track KPIs, profitability, trends, and performance across regions, customers, and products.

View Project
on GitHUB

Other Tools

Power BI

DAX

Power Query

Microsoft Excel

Data Modeling (Star Schema)

Home navigation page of the Business Insights 360 dashboard providing quick access to finance, sales, marketing, supply chain, executive, and support views with a clean and intuitive layout.

Finance dashboard displaying key financial KPIs, detailed P&L statement, net sales trends over time, and region- and segment-wise performance comparisons using interactive visuals.

Project Details

This project involves building a comprehensive Business Intelligence dashboard in Power BI to analyze Atliq Hardwares’ performance across multiple business functions. The dashboard integrates finance, sales, marketing, supply chain, and executive-level views into a single analytics solution. Data was cleaned and transformed using Power Query, followed by building a robust data model and writing DAX measures for KPIs such as Net Sales, Gross Margin, Net Profit %, and Forecast Accuracy. Interactive slicers enable analysis by region, market, customer, product, and time period. The solution provides trend analysis, P&L statements, top and bottom product insights, and executive summaries, supporting data-driven strategic and operational decisions.

Live Interactive Dashboard

SQL Project

Sales & Forecast Data Analysis Using SQL (Star Schema Based)

Domain/Function: Data Analytics | Database Management | Sales & Forecast Analysis

Analyzed sales and forecast data using SQL by querying fact and dimension tables. Performed data exploration, customer-level analysis, and supported downstream BI reporting through structured and optimized SQL queries.

View Project
on GitHUB

Other Tools

MySQL

MySQL Workbench

SQL (Joins

Aggregations

Filtering)

Relational Database Concepts

SQL query executed on the fact_forecast_monthly table displaying forecast quantities across customers, products, and fiscal years for analytical and reporting purposes.

Customer dimension table view showing customer details such as platform, channel, market, region, and sub-zone used for segmentation and sales analysis.

Project Details

This project focuses on analyzing sales and forecast data using SQL in a structured relational database environment. The dataset follows a star schema design with fact tables such as fact_sales_monthly and fact_forecast_monthly, along with dimension tables including dim_customer, dim_product, and dim_date. Using MySQL Workbench, SQL queries were written to retrieve, filter, and aggregate data across customers, products, regions, and fiscal years. The analysis enables understanding customer distribution, forecast quantities, and time-based trends. This project strengthens foundational SQL skills such as joins, grouping, filtering, and working with real-world analytical schemas, while supporting business intelligence use cases and dashboard development.

Python Project

Hotel Booking Data Analysis Using Python

Domain/Function: Exploratory Data Analysis | Data Visualization | Booking Analytics

Performed exploratory data analysis on hotel booking data using Python to understand city-wise hotel distribution and booking platform trends. Visualized insights using bar charts to support data-driven decision-making.

View Project
on GitHUB

Other Tools

Python

Pandas

NumPy

Matplotlib

Jupyter Notebook

Bar chart visualization showing the distribution of hotels across major cities such as Mumbai, Hyderabad, Bangalore, and Delhi using Pandas and Matplotlib.

Bar chart displaying booking counts across different platforms, highlighting the most commonly used booking channels and overall customer booking behavior.

Project Details

This project focuses on exploratory data analysis of hotel booking data using Python in a Jupyter Notebook environment. The dataset was analyzed using Pandas and NumPy to explore booking patterns across cities and booking platforms. Value counts and aggregations were performed to identify high-demand cities and the most frequently used booking channels. Matplotlib was used to create bar charts for visualizing city-wise hotel counts and platform-wise booking distribution. The analysis helps in understanding customer preferences, market concentration, and platform dominance. This project demonstrates core Python skills required for data analysis, including data manipulation, aggregation, and visualization.