The UEFA Data Analysis project is designed to provide insights into UEFA football matches using advanced data analytics. The project aims to:
Store and manage large datasets efficiently using Snowflake. Perform complex queries using SQL and Snowflake's optimized query engine. Conduct data cleaning, preparation, and transformation using Python and Pandas. Analyze match performance metrics such as team wins, goals, possession percentages, shots on target, and penalty shootouts. Deliver business insights through SQL queries, aggregations, and statistical analysis. Key Components: Data Source:
The UEFA match data is sourced from DataCamp, which provides clean and structured football match datasets. Data Cleaning and Transformation:
Data is cleaned and transformed using Python and Pandas. Irrelevant columns, missing values, and inconsistent data are handled during this stage. The clean data is then prepared for upload to Snowflake via Snowflake’s staging areas. Data Storage & Querying:
Snowflake is utilized as the core data warehouse for storing and querying the cleaned data. Multiple tables corresponding to the years 2020, 2021, and 2022 are created to store match data such as: Teams (home and away) Scores (home and away) Possession percentages Shots (total and on target) Penalty shootouts Match location Win/loss predictions Snowflake Staging Areas: Data is loaded into Snowflake using internal and external staging areas to ensure efficient data ingestion. Staging allows bulk uploads and ensures data consistency before transferring to final tables. Data Analysis:
SQL queries are executed in Snowflake to perform various analyses, including: Finding teams with the most wins. Identifying locations that hosted the most matches. Calculating average possession percentages and shots on target. Analyzing matches with the most goals or shots. Predicting match outcomes based on historical data. Complex queries use window functions, joins, and aggregations to derive meaningful insights from the data. Technologies Used:
Python: For data cleaning, processing, and interacting with Snowflake. Libraries: pandas, snowflake-connector-python, os, sys Pandas: Used for transforming raw CSV files into a clean, normalized format before uploading them into Snowflake. Snowflake: A scalable cloud data warehouse that enables fast querying and analysis of UEFA match data. Staging Areas: For data ingestion and management of external and internal files before loading into Snowflake. SQL: Used to perform queries, aggregate match statistics, and generate analytical reports.