Football Data Scraping with Python
Budget: $30 – $250 AUD
I'm looking for a Python expert to assist with web scraping and data extraction from specific URLs on official football league sites for statistical analyses. This project involves duplicating existing mathematical formulas and creating clusters of pre-named functions in the provided Python code. The end goal is to produce a CSV file containing the processed data.
Ideal skills and experience for this job include:
- Python Developer for Data Extraction, Web Scraping, Automation, and API Integration
- Proficiency in Python and data scraping libraries/ (Pandas, Openpyxl & Numpy)
- Data Extraction & Web Scraping: Mastering the art of gathering data from dynamic websites, PDFs, and more using tools like Scrapy, Selenium, and Beautiful Soup.
- Automation: Designing custom bots and scripts to automate repetitive tasks, streamline processes, and enhance productivity.- Experience with data manipulation and analysis
- Back-End Development
- Understanding of football statistics
- Ability to replicate mathematical formulas
- Familiarity with creating clusters of functions in code
- Experience working with CSV files
Please note that I have specific URLs to target, so guidance on what to scrape is not required.
The data should be scraped and updated daily. Store the processed data in a NoSQL database with daily updates. Focus on scraping Previous Scores statistics. The processed data should be stored in MongoDB with daily updates. Create a dashboard view for tracking daily data updates. Include tabular views in the dashboard to list daily data updates. The dashboard should be accessible via a web interface. The dashboard should include a basic summary with key metrics and visualizations of daily updates.
Ideal skills and experience for this job include:
- Python Developer for Data Extraction, Web Scraping, Automation, and API Integration
- Proficiency in Python and data scraping libraries/ (Pandas, Openpyxl & Numpy)
- Data Extraction & Web Scraping: Mastering the art of gathering data from dynamic websites, PDFs, and more using tools like Scrapy, Selenium, and Beautiful Soup.
- Automation: Designing custom bots and scripts to automate repetitive tasks, streamline processes, and enhance productivity.- Experience with data manipulation and analysis
- Back-End Development
- Understanding of football statistics
- Ability to replicate mathematical formulas
- Familiarity with creating clusters of functions in code
- Experience working with CSV files
Please note that I have specific URLs to target, so guidance on what to scrape is not required.
The data should be scraped and updated daily. Store the processed data in a NoSQL database with daily updates. Focus on scraping Previous Scores statistics. The processed data should be stored in MongoDB with daily updates. Create a dashboard view for tracking daily data updates. Include tabular views in the dashboard to list daily data updates. The dashboard should be accessible via a web interface. The dashboard should include a basic summary with key metrics and visualizations of daily updates.