SQL-Python-Ecommerce-Project

1. E-commerce Website Development with Database Integration: SQL: Used for designing and managing the underlying database schema for an e-commerce platform. This includes tables for products, customers, orders, categories, reviews, etc. SQL is used for data definition (DDL) and data manipulation (DML) to store, retrieve, update, and delete e-commerce data. Python: Employed for building the web application logic (e.g., using frameworks like Flask or Django). Python interacts with the SQL database to handle user authentication, product display, shopping cart functionality, order processing, and other dynamic features of the e-commerce site.
2. E-commerce Data Analysis and Insights: SQL: Used for extracting, filtering, and aggregating raw e-commerce data from a database. This involves writing complex queries to retrieve specific information, such as sales trends, customer demographics, product performance, or inventory levels. Python: Utilized for advanced data analysis, cleaning, transformation, and visualization. Libraries like Pandas are used for data manipulation, cleaning missing values, and feature engineering. Matplotlib and Seaborn are common for creating visualizations to present insights on customer behavior, revenue analysis, sales performance, and marketing effectiveness. Key Components and Skills Involved: Database Management Systems: MySQL, PostgreSQL, SQLite, SQL Server. Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, SQLAlchemy (for ORM), Flask/Django (for web development), pyodbc or mysql.connector (for database connectivity). SQL Concepts: DDL (CREATE, ALTER, DROP), DML (SELECT, INSERT, UPDATE, DELETE), Joins, Subqueries, Aggregations, Stored Procedures. E-commerce Concepts: Customer segmentation, sales forecasting, inventory management, order fulfillment, product recommendations.