Harsha S S

Data Analyst

[email protected] +91 8075600428

Resume

Hello, I am

Harsha S S.

1

Excel
Project

2

PowerBI
Projects

1

SQL
Project

1

Python
Project

About Me

Hi, I’m an aspiring Data Analyst with a strong foundation in statistics and a real love for making sense of data. I hold a Master’s degree in Statistics and a Bachelor’s in Mathematics, which led me to find my true passion in data analytics. Using tools like Excel, Power BI, SQL, and Python, I’ve learned how to dig into data to uncover insights that can help businesses make smarter decisions.

While I haven’t worked in an organization yet, I’ve completed several real-world business projects that you can explore below. From analyzing sales trends to segmenting customers, these projects showcase my hands-on skills and dedication to creating data-driven solutions. I’m eager to bring my skills and enthusiasm
to a team that values fresh perspectives and a data-focused approach.

Key Skills

Excel

Power BI

SQL

Python

stakeholder Management

Good Communication

My Projects

Sales Analytics

Domain/Function: Sales Analysis

AtliQ Sales/Business Insight 360

Domain/Function: Multi-Functional Business Analytics

Weekly User Retention Analysis Using SQL

Domain/Function: User Retention Analysis

Laptop Price Prediction using Machine Learning

Domain/Function: Predictive Analytics / Data Science

Good Cabs Analysis

Domain/Function: Transportation and Mobility

My Experience

Student Intern | Laptop Price Prediction
Edure | Apr - May 2024
• Developed a machine learning model in Python to predict laptop prices based on key features, achieving a high R² score of 92% using random forest regression.
• Extracted insights to enhance decision-making and optimize pricing strategies by 15%, contributing to improved market strategy and customer satisfaction. Integrated the model into the data pipeline, enabling real-time price updates.

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

Diploma in Data Analytics

SQL (Basic) Certificate

SQL (Intermediate) Certificate

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/harshass/

Call Me

+91 8075600428

GitHub

https://github.com/harshass124

Excel Project

Sales Analytics

Domain/Function: Sales Analysis

This Sales Analysis Project in Excel reviews customer performance, market trends, and product insights to support strategic decisions on discounts, market growth, and inventory. It includes division and new product analyses plus top/bottom products by sales and quantity.

View Project
Engagement

View Project
on GitHUB

AtliQ Hardware's 2019-2021 net sales reveal key growth areas, top customers, and high-performing divisions, with insights on emerging market leaders.

Market Sales Performance Table highlights sales growth, key customer segments, and year-over-year changes, showcasing top markets and improvement area

Project Details

The Sales Analysis Project in Excel offers a comprehensive view of sales performance by analyzing customer trends, product insights, and division-based results. Key objectives include assessing customer performance, comparing market trends to sales targets, and evaluating the impact of new products on sales. A detailed Division Report highlights each business unit's strengths, helping identify areas that drive growth and those needing improvement. Additional insights into the top 10 products by sales and top/bottom 5 by quantity reveal high-demand items and those requiring marketing support. Through these analyses, businesses gain critical insights into sales trends, customer demand, and product performance. Using ETL processes, data model relationships, and calculated columns, the project creates accurate, user-friendly reports that inform strategic decisions on sales goals, customer discounts, market expansion, and inventory optimization.

PowerBI Project

AtliQ Sales/Business Insight 360

Domain/Function: Multi-Functional Business Analytics

The 'AtliQ Sales/Business Insight 360' Power BI project analyzes sales, finance, marketing, and supply chain data to derive actionable insights. It incorporates multiple views and modes to track business performance and trends effectively.

View Project
Engagement

View Project
on GitHUB

Tracks net sales trends from Sept 2020-Aug 2021, highlighting seasonal peaks, dips, and stable periods for year-over-year comparison.

Shows 2021’s financial growth, with sales increases, detailed cost breakdowns, and margin improvements, highlighting profitability trends.

Project Details

The 'AtliQ Sales/Business Insight 360' Power BI project provides insights into business performance across sales, finance, marketing, and supply chain. It integrates data from multiple sources to create an interactive dashboard for informed decision-making.

Key features include:

Sales Analysis: Visualizes product performance, top-selling products, and trends.
Finance Insights: Displays financial metrics like revenue, expenses, and profit margins.
Marketing Analytics: Tracks the impact of marketing campaigns on sales and customer engagement.
Supply Chain Management: Provides insights into inventory levels, procurement, and delivery performance.
Leveraging Power BI’s visuals, filters, and slicers, the project makes data easy to explore and understand, empowering stakeholders to monitor business performance and trends in real time.

SQL Project

Weekly User Retention Analysis Using SQL

Domain/Function: User Retention Analysis

This project analyzes weekly user retention using SQL, tracking returning users over 10 weeks after their first purchase. It calculates each user's "Week 0" for their first order and outputs retention metrics, helping businesses refine engagement strategies and marketing decisions.

View Project
on GitHUB

The orders table lists user purchases with dates and amounts, which helps in analyzing buying frequency and spending habits.

The retention table shows weekly user returns for 10 weeks after their first purchase, highlighting engagement trends over time.

Project Details

The project aims to analyze user retention over a 10-week period by tracking users who return after their first purchase.
It begins by identifying each user’s "Week 0," marking their first order date, and then calculates weekly retention metrics to track user behavior.
SQL functions are used for date manipulation, grouping, and aggregation to derive meaningful insights from the data.
The analysis helps businesses understand user engagement, evaluate retention strategies, and identify trends that influence customer loyalty.
By analyzing retention trends, businesses can refine their strategies to improve customer loyalty, optimize marketing efforts, and increase profitability.
By analyzing retention trends, businesses can refine their strategies to improve customer loyalty, optimize marketing efforts, and increase profitability.

Python Project

Laptop Price Prediction using Machine Learning

Domain/Function: Predictive Analytics / Data Science

This project uses machine learning to predict laptop prices based on specifications like brand, RAM, processor, and storage type. By modeling laptop data, it reveals how features impact prices, helping consumers and businesses make informed purchasing and pricing choices.

View Project
on GitHUB

Scatter plot showing the positive correlation between RAM and laptop prices.

Bar chart displaying laptop brand frequencies, highlighting market representation.

Project Details

The Laptop Price Prediction project leverages data analysis and machine learning to forecast laptop prices based on various specifications, aiding consumers and retailers. Models such as Linear Regression, Ridge Regression, Random Forest, and Support Vector Machine were trained and evaluated, with Random Forest yielding the highest accuracy at an R-squared score of 90.6%. Insights include the influence of RAM on prices, weight distributions, brand frequencies, and average prices by operating system, providing a comprehensive overview of pricing trends. Key tools include Pandas, Scikit-learn, and Matplotlib in Python.

PowerBI Project

Good Cabs Analysis

Domain/Function: Transportation and Mobility

GoodCabs uses data-driven insights to enhance ride-hailing. With tier-based rewards, flexible driver options, tailored programs like Journey Plus, and the GreenDrive Initiative for EV adoption, we ensure satisfaction and sustainability. Explore my GitHub and live dashboard for detailed insights!

View Project
Engagement

View Project
on GitHUB

The model links trips, passengers, cities, and targets, connecting facts and dimensions for insights on trends, performance, and city-specific metrics

Good Cabs analysis covers Operational (city performance), Customer (loyalty), Financial(health), and a Reality Check (gaps vs. target).

Project Details

Good Cabs is transforming ride-hailing with data-driven strategies. We've introduced tier-based rewards for customers and seasonal work flexibility for drivers to boost service quality and motivation. Initiatives like the Good Cabs Journey Plus program offer tailored tourist packages and corporate partnerships to drive demand and loyalty. Our GreenDrive Initiative supports EV adoption, focusing on sustainability and tackling infrastructure challenges through strategic partnerships. By using predictive analytics, we optimize routes, reduce fuel costs, and minimize delays, ensuring a seamless experience for passengers. This project showcases how insights drive innovation and value creation.