Prathamesh Kavathekar

A "Data analysis" enthusiast with analytical skill and critical thinking skills to thrive in data-driven environments. Tackles challenges with positivity and drive to overcome. Works great alone or with others and consistently exceeds expectations.

• Client recognition for good performance in Q2 2023 . Lead a WO update project for japan which caused the reopening and resubmissions to health authority of a high volume of product complaint .

• Hacker Rank SQL (Basic) https://www.hackerrank.com/certificates/fdbcfd4269a1
• Hacker Rank SQL ( intermediate ) https://www.hackerrank.com/certificates/863a02dea762
• All India Council for professional Excellence (Python) / 24T896225256176748

Rolls Mania SQL Project

Description:
Worked on the Rolls Mania SQL project which involved analyzing and manipulating data for a food delivery service. The project focused on using SQL to perform various data operations, including querying, updating, and managing a relational database.

Key Responsibilities:

Data Extraction and Analysis: Utilized complex SQL queries to extract relevant data from large datasets to     support business decision-making.
Data Transformation: Performed data cleaning and transformation operations to ensure data integrity and accuracy.
Database Management: Created and managed database tables, views, and indexes to optimize data storage and retrieval.
Performance Optimization: Improved query performance by analyzing execution plans and implementing indexing strategies.
Reporting: Generated comprehensive reports summarizing key metrics and insights derived from the data analysis.
Problem Solving: Identified and resolved data-related issues and inconsistencies to maintain high-quality datasets.

Skills Utilized:
SQL (Structured Query Language)
Data Analysis
Database Management
Performance Tuning

Tools:
MySQL

Outcome:
Successfully completed the project by delivering actionable insights and maintaining an optimized database environment, demonstrating strong proficiency in SQL and data management.

Retail Sales Analysis – SQL Project

• Designed and implemented a retail sales database (p1_retail_db) to analyze sales trends, customer demographics, and product performance.
• Performed data cleaning by handling null values and ensuring data integrity.
• Conducted exploratory data analysis (EDA) using SQL queries to extract key insights.
• Developed business-driven SQL queries to analyze sales by category, customer spending patterns, peak sales periods, and high-value transactions.
• Created reports on sales trends, customer segmentation, and revenue performance to support data-driven decision-making.
• Utilized SQL functions such as aggregation, ranking, and time-based analysis for comprehensive reporting.

Zomato Data Analysis – Advanced SQL Project

Project Overview:
Conducted an advanced SQL-based analysis of Zomato’s restaurant and customer dataset to extract business insights related to restaurant performance, customer behavior, and revenue trends. Implemented complex SQL queries for data cleaning, exploratory data analysis (EDA), and business intelligence reporting.

Key Responsibilities:

• Designed and structured the Zomato dataset within a relational database.
• Performed data cleaning by handling missing values, duplicates, and ensuring data consistency.
• Executed exploratory data analysis to understand customer preferences, restaurant trends, and sales performance.
• Developed complex SQL queries for key business insights, such as:
  Top Restaurant Analysis – Identified highest-rated and most-ordered-from restaurants.
  Customer Segmentation – Analyzed repeat customers, order frequency, and average spending.
  Revenue Trends – Examined peak ordering times, monthly sales patterns, and high-revenue locations.
  Cuisine Popularity – Ranked cuisines based on order volume and revenue contribution.
  Geographic Insights – Analyzed restaurant distribution and demand trends across different cities.

SQL Techniques Used:

• Aggregations & Grouping: SUM(), AVG(), COUNT(), GROUP BY, HAVING.
• Window Functions: RANK(), DENSE_RANK(), ROW_NUMBER().
• Common Table Expressions (CTEs) and Subqueries for complex analysis.
• Joins & Filtering: Used multiple joins to extract insights from different tables.
• Date & String Functions: Extracted time-based insights and processed textual data.

Key Findings:

• High Revenue Restaurants: Certain high-rated restaurants generated maximum revenue due to consistent customer engagement.
• Peak Order Hours: Majority of orders placed between 7 PM – 10 PM, highlighting prime sales periods.
• Cuisine Popularity: Fast food and North Indian cuisines were the most frequently ordered.
• Customer Loyalty: A significant percentage of revenue came from repeat customers, emphasizing the importance of loyalty programs.
• City-Wise Demand: Metro cities had the highest order volume, whereas smaller cities showed untapped market potential.

Project Impact: Provided actionable insights to optimize restaurant listings, pricing strategies, and customer engagement models The findings can assist in decision-making regarding menu planning, peak-hour promotions, and location-based expansions.

Tools : MySQL