Tejaswini Mudunuru

Data Aspirant

[email protected] +91 8328044596

Resume

Hello, I am

Tejaswini Mudunuru.

1

Excel
Project

1

PowerBI
Project

2

SQL
Projects

About Me

I am an aspiring Data Analyst with a strong interest in transforming data into clear, actionable insights. I hold a Bachelor’s degree in BSc and have developed hands-on experience working with Excel, SQL, Power BI, and basic Python through practical, project-based learning.

Although I am at the early stage of my career and have not yet worked in a formal organizational setting, I have completed multiple end-to-end data analytics projects that mirror real business scenarios. These projects demonstrate my ability to clean and analyze data, identify trends, build dashboards, and communicate insights in a structured and meaningful way.

I approach data with curiosity and a problem-solving mindset, always focusing on understanding the “why” behind the numbers. I am continuously learning and refining my skills to stay aligned with industry practices.

I am eager to start my professional journey as a Data Analyst, contribute value through data-driven thinking, and grow alongside a team that

Key Skills

SQL

Python Visualization

Advanvced Excel

Power BI

MySQL

My Projects

SQL Murder Mystery🕵️

Domain/Function: Data Analysis/Crime Analysis

🍕 SQL Mini Project — The Great Pizza Analytics Challenge! 🍕

Domain/Function: Data Analytics / Business Analytics

Business Insights 360 📈

Domain/Function: Brick & mortar and e-commerce

Sales & Finance Analysis (P&L Report)📊

Domain/Function: Sales/Finance analysis

My Experience

📊 Data Analytics Internship

Millennium Software Solutions – Visakhapatnam
Feb 2024 – May 2024

During my Data Analytics internship, I gained hands-on experience working with real-world datasets using Excel, SQL, Power BI, and Python. I applied analytical techniques to clean, analyze, and visualize data to uncover meaningful business insights.

One of the key projects in my portfolio is the Music Store Data Analysis, where I:

Used PostgreSQL to query and analyze customer and sales data
Identified top-selling music genres by region using SQL aggregations and joins
Created visualizations using Python (Matplotlib, Seaborn) to clearly communicate trends and insights

Through this internship and related projects, I strengthened my ability to:

Perform data cleaning and transformation
Write efficient SQL queries
Build clear, insight-driven visualizations
Present findings in a structured and actionable format for business stakeholders

This experience helped me build a strong foundation in practical data analytics and reinforced my interest in solving business problems using data.

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

Data Analytics and Visualization Job Simulation

21 days SQL challenge with Indian Data club

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]

http://www.linkedin.com/in/tejaswini-mudunuru-615667297/

Call Me

+91 8328044596

GitHub

https://github.com/tejaswinivarma12

SQL Project

SQL Murder Mystery🕵️

Domain/Function: Data Analysis/Crime Analysis

A SQL-based investigation project where I analyzed interconnected crime, suspect, and witness datasets. Using joins, CTEs, and logical filtering, I uncovered clues step by step and identified the final suspect through data-driven reasoning.

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Engagement

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on GitHUB

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Compared claimed locations with keycard logs and physical evidence using SQL joins. Identified a mismatch between the suspect’s alibi and actual movements, supported by fingerprint and access-log evidence during the crime window

Used CTEs and multi-table joins to intersect access logs, alibis, and call activity. Eliminated false suspects and conclusively identified David Kumar as the killer through consistent data validation.

Project Details

SQL Murder Mystery is a data investigation project where I solved a fictional crime using only SQL and relational datasets. The objective was to identify the killer by analyzing interconnected tables containing crime reports, witness statements, suspect details, and location data.

I explored the database schema, cleaned and filtered records, and applied SQL techniques such as JOINs, CTEs, subqueries, and aggregations to uncover meaningful clues.

Each step of the analysis was validated by cross-checking information across multiple tables to avoid false conclusions. By breaking the problem into smaller logical queries and documenting the reasoning behind each step, I was able to systematically narrow down suspects and identify the final culprit.

This project demonstrates my ability to think analytically, work with complex relational data, validate insights, and communicate findings clearly—skills that closely reflect real-world data analysis and investigative scenarios.

SQL Project

🍕 SQL Mini Project — The Great Pizza Analytics Challenge! 🍕

Domain/Function: Data Analytics / Business Analytics

End-to-end SQL analytics project where I analyzed pizza sales data to generate actionable insights. Applied database creation, joins, aggregations, filtering, and NULL handling to evaluate orders, pricing, and category performance.

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Engagement

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on GitHUB

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on View Project on Google Drive – Full Report

Calculated total revenue per order by joining orders, order_details, and pizzas tables. Used JOIN, SUM, and GROUP BY to aggregate item-level sales into order-level values, helping analyze customer spending and revenue contribution per order.

Analyzed pizza sales performance by category using JOIN and GROUP BY. Aggregated total quantities sold across categories to identify customer preferences. Results showed the Classic category as the top-selling segment.

Project Details

The Great Pizza Analytics Challenge is an end-to-end SQL analytics project where I acted as a Data Analyst for IDC Pizza, transforming raw pizza sales data into actionable insights. The project involved database creation, importing dump files, and exploring relational tables.

Using SQL, I performed data filtering with WHERE, IN, BETWEEN, and LIKE, handled NULL values using COALESCE, and applied aggregations such as SUM, AVG, COUNT, GROUP BY, and HAVING to analyze sales performance. I also worked extensively with joins (INNER, LEFT, RIGHT, and SELF JOIN) to combine multiple tables and uncover insights like total order value, category-wise sales, and pricing differences across pizza sizes.

This project strengthened my SQL fundamentals, improved my ability to solve real-world analytical problems, and gave me hands-on experience working with structured datasets similar to business reporting scenarios.

PowerBI Project

Business Insights 360 📈

Domain/Function: Brick & mortar and e-commerce

Business Insights 360: Built an end-to-end Power BI dashboard integrating SQL and Excel data to deliver 360° business insights across sales, finance, sales, marketing, supply chain view, executive view. Designed KPIs and DAX measures to support data-driven decisions and performance tracking.

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Engagement

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on GitHUB

Supply chain performance dashboard showing Net Sales, Gross Margin, Net Profit %, and unit economics across segments and regions. Interactive visuals support analysis of product profitability, demand patterns, and operational efficiency.

Executive-level Power BI dashboard providing a 360° view of revenue, market share, forecast accuracy, and key KPIs. Enables analysis of revenue by division, channel, top customers, and products to support strategic decision-making.

Project Details

This project focuses on building an end-to-end Business Intelligence dashboard in Power BI to provide a 360-degree view of business performance across key functions. The dashboard integrates data from SQL databases and Excel files, enabling unified analysis across Sales, Finance, Supply Chain, Marketing, Products, and Executive perspectives.

I designed a robust data model and created DAX measures and KPIs to track revenue, profitability, customer performance, demand trends, and operational metrics.
Multiple interactive views allow stakeholders to analyze performance at different levels, identify growth opportunities, and monitor business health.

Integrated large datasets from SQL and Excel to build role-based dashboards. Enabled drill-down analysis and standardized KPIs to improve reporting efficiency and support executive and departmental decision-making.

Project video

Excel Project

Sales & Finance Analysis (P&L Report)📊

Domain/Function: Sales/Finance analysis

Built a multi-year Profit & Loss reporting system using advanced Excel. Analyzed Net Sales, COGS, and Gross Margin across fiscal years, quarters, months, and markets. Used Power Query, Pivot Tables, and basic DAX to deliver business-ready financial insights.

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Engagement

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on GitHUB

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on View Project on Google Drive – Full Report

Customer-wise Net Sales performance analysis showing year-over-year growth from 2019 to 2021 using Excel, enabling comparison of sales trends and identification of top-performing customers.

Monthly and quarterly P&L analysis showing Net Sales, COGS, Gross Margin, and GM% across fiscal years using Excel, enabling comparison of sales and profitability trends.

Project Details

This project focuses on analyzing multi-year sales and financial data to create structured Profit & Loss (P&L) reports for AtliQ Hardware using Microsoft Excel. The objective was to provide clear visibility into revenue, costs, and profitability across different business dimensions to support financial and sales performance evaluation.

The analysis includes P&L reporting by fiscal year, fiscal month, market, and sub-zone, enabling comparison of sales and margin trends over time. Key metrics such as Net Sales, Cost of Goods Sold (COGS), Gross Margin, and Gross Margin Percentage were calculated and evaluated to assess profitability and cost efficiency.

Advanced Excel features including Power Query, Power Pivot, Pivot Tables, and basic DAX were used to clean, model, and transform raw financial data into business-ready insights. Used advanced Excel features to convert raw data into structured P&L reports for sales and financial trend analysis.