Python & SQL Time Series Analysis

This project demonstrates end-to-end time-based data analysis using Python (Pandas) and SQL. It focuses on extracting insights from date and time data for real-world business scenarios.

Key Features

• Date and time conversion using Python (datetime, pandas)
• Extraction of components (year, month, day, weekday)
• Time-based aggregations (daily, weekly, monthly)
• SQL queries for business analysis
• Handling real-world use cases like last week revenue and customer activity

Project Files

• Python_Sql_time_series_calculation.ipynb
  Jupyter notebook covering date-time transformations and analysis using Pandas

• Time_calculation_SQL.sql
  SQL queries for time-based filtering, aggregation, and business insights

• Time_Calculation_SQL_Guide.pdf
  Reference guide explaining SQL concepts used in the project

Business Use Cases

• Total revenue generated in the last week
• Weekday-wise revenue analysis
• Customer activity tracking over time
• Identifying consistent/loyal customers

Tools & Technologies

• Python (Pandas, datetime)
• SQL (MySQL)
• Jupyter Notebook

Why This Project

Time-based analysis is a critical skill for data analysts. This project showcases practical implementations that can be directly applied in real business scenarios like sales tracking, retention analysis, and performance monitoring.

Author

Ratnajit Chakraborty LinkedIn: https://www.linkedin.com/in/ratnajit-chakraborty-076ab520a/