About This Project
This website is a comprehensive NFL data analytics platform designed to track and visualize team statistics over multiple seasons. It integrates web scraping, database management, data analysis, and front-end interactivity to deliver a seamless experience for exploring NFL statistics.
Tech Stack & Development Process
I leveraged a variety of technologies to collect, process, store, and visualize the data:
- Data Collection: Used Playwright to scrape team stats and information from multiple sources.
- Data Processing: Utilized Pandas for cleaning and combining the datasets.
- Database Management: Stored data in PostgreSQL to efficiently manage 4,613 games spanning from 2008 to 2024 with 234 columns.
- Backend Development: Used Python and Jinja2 templates to dynamically generate HTML pages.
- Front-End Development: Styled the website with CSS and Bootstrap to ensure a responsive and user-friendly interface.
- Dynamic Data Updates: Implemented JavaScript to allow real-time updates for tables and charts based on user selections.
- Data Visualization: Integrated Plotly.js to generate interactive charts for trend analysis.
- Statistical Computations: Used Pandas to calculate averages and other key metrics.
Dataset Overview
- Data Range: 2008 - 2024
- Number of Games: 4,613
- Columns: 234
- Key Stats: Passing yards, rushing yards, scoring trends, opponent trends, and advanced analytics.
About Me
I am an experienced business analyst with a strong background in Python, SQL, and data visualization. I specialize in exploratory data analysis, statistical modeling, and unstructured data analysis. My passion lies in leveraging data to drive decision-making, optimize processes, and create insightful dashboards. I hold a Bachelor of Science in Biology from Texas A&M University and a Data Analytics Certification from the University of Texas at Austin Data Analytics Bootcamp.
Skills
- Programming: Python, SQL, JavaScript, HTML/CSS
- Data Science: Pandas, NumPy, scikit-learn, TensorFlow
- Data Visualization: Tableau, Plotly, Matplotlib, JavaScript Charting
- Databases: PostgreSQL, SQL Server, MongoDB
- Big Data & ETL: Hadoop, PySpark, Databricks
- Machine Learning: Regression, Decision Trees, Forecasting
Connect With Me
Feel free to check out my work, projects, and professional background using the links below:
Thank You!
Thank you for taking the time to check out my project. I appreciate your interest in my work, and I hope you find the insights and visualizations useful.