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Data-Analyst-SQL

Welcome to the Data-Analyst-SQL repository! This repository showcases SQL projects focused on data analysis and business insights. It includes scripts, queries, and solutions to a variety of analytical challenges across different domains, such as retail, healthcare, and e-commerce.


Table of Contents


Projects

1. Pizza Sales Analysis

  • Description: SQL-based analysis of pizza sales data to understand sales patterns, high-performing items, and customer preferences.
  • Techniques Used: Aggregations, joins, filtering, and ranking.
  • Guide: Ayushi Jain

2. Music Store Sales Analysis

  • Description: Data insights for an online music store, focusing on genre popularity, sales by artist, and customer demographics.
  • Techniques Used: Joins, subqueries, and CTEs.
  • Guide: Rishabh Mishra

3. Online Sales Dashboard

  • Description: An interactive Power BI dashboard that tracks and visualizes e-commerce sales data.
  • Techniques Used: Data connections, table joins, complex parameters, and customized visualizations.
  • Tools Used: SQL, Power BI

4. Walmart Sales Forecasting Analysis

  • Description: Data exploration and forecasting for Walmart sales, using data wrangling, feature engineering, and EDA.
  • Dataset: Kaggle Walmart Sales Forecasting
  • Techniques Used: Data wrangling, feature engineering, trend analysis.

5. Healthcare Patient Analysis

  • Description: This dataset includes various features relevant to healthcare operations and patient records,
  • covering patient demographics, visit details, treatments, diagnoses, and costs.
  • Dataset: Kaggle Healthcare Dataset
  • Techniques Used: SQL, Excel

Requirements

  • MySQL 8.0 or higher
  • Basic understanding of SQL and relational databases

Installation

  1. Clone the repository:
    git clone https://github.com/kunalkumar2001/Data-Analyst-SQL.git
    cd Data-Analyst-SQL
    
    

Uasge

  1. Review each SQL file within the project folders for different datasets and analysis tasks.
  2. Run the queries in your MySQL environment or import the scripts directly.
  3. Modify the queries as needed to suit your data sources or analytical needs.

Contributing

Contributions are welcome! Please fork the repository, make changes, and submit a pull request for review.

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Data Analyst SQL Project for Portfolio

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