Data Analytics Project for Beginners: T20 Cricket Analysis

📊 Project Overview

This project is a beginner-friendly data analytics case study focused on T20 cricket data. It demonstrates the end-to-end process of data collection, preprocessing, analysis, and dashboarding using Power BI and Python. The project is designed to help newcomers understand the basics of data analytics, visualization, and reporting with real-world sports data.

✨ Features

• End-to-end data pipeline: from raw data to interactive dashboards
• Data preprocessing with Python (Jupyter Notebook)
• Power BI dashboards for insightful visualizations
• Example DAX measures and calculated columns
• Parameter scoping and dashboarding tips
• Mockups and screen recordings for step-by-step guidance

🎥 Demo

• Screen Recordings:
  • Dashboard Building - Part 1
  • Dashboard Building - Part 2
  • Dashboard Building - Part 3
• Mockup:
  • Dashboard Mockup (PPTX)

📁 File Structure

├── Cricket Best 11.pbix                    # Power BI file for Best 11 analysis
├── Stage-2.pbix, Stage-3.pbix             # Power BI files for different project stages
├── t20_cric_1_power_query.pbix            # Power BI file with Power Query
├── t20_data_preprocessing.ipynb           # Jupyter Notebook for data preprocessing
├── t20_csv_files                          # Raw data in CSV format
├── t20_json_files                         # Raw data in JSON format
├── DAX Measures and Calculated columns.xlsx # Example DAX and calculated columns
├── Paramaeter Scoping.pdf                 # PDF on parameter scoping
├── web_scrapping_codes                    # Web scraping scripts (Python)
├── dashboard_building_screenrecord_*.mp4  # Screen recordings
├── Mock Up.pptx                           # Dashboard mockup

🗂️ Data Sources

• T20 cricket data (CSV/JSON)
• Data collected via web scraping (see web_scrapping_codes)

🚀 How to Use

1. Data Preprocessing (Python)

• Open t20_data_preprocessing.ipynb in Jupyter Notebook
• Run the notebook to clean and preprocess the raw data
• Export the processed data for use in Power BI

2. Dashboarding (Power BI)

• Open any .pbix file in Power BI Desktop
• Load the processed data
• Explore the dashboards and DAX measures
• Refer to the screen recordings for step-by-step dashboard building

🛠️ Requirements

• Power BI Desktop
• Python 3.x (for Jupyter Notebook)
• Jupyter Notebook (pip install notebook)
• Required Python libraries: pandas, numpy, etc. (see notebook for details)

🙏 Credits

• T20 cricket data from public sources

📄 License

This project is for educational purposes. Please check data source terms before commercial use.