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Abstract

Explore advanced Python techniques for efficient data retrieval, focusing on handling large datasets, optimizing API calls, and ensuring data integrity. Whether you're working in healthcare analytics, financial modeling, or large-scale data science, these strategies will enhance your workflow and analysis capabilities.

Key Points

• Efficient data retrieval using advanced Python techniques.
• Strategies for handling large datasets and optimizing API performance.
• Best practices for data cleaning and preparation.
• Essential tools for developers and data analysts in high-demand fields.

Explore the visualization of 5K@ADA race results using Tableau. Learn how to create interactive maps and charts that showcase geographic distribution and performance metrics. Discover how calculated fields and interactivity features in Tableau make the data analysis both engaging and informative.

Key Points

• Introduction to 5K@ADA Race: Brief overview of the race and its significance in promoting diabetes awareness and research.
• Data Preparation: Steps involved in preparing the dataset, including the left join between ADA Race Data and Countries datasets.
• Creating Visualizations in Tableau: Explanation of the visualizations created to analyze the 5K@ADA race data.

Explore the intricacies of web scraping with Python using advanced techniques to handle dynamic content and multi-page structures. Efficiently extract, clean, and prepare 5K@ADA race results data for analysis with Selenium and BeautifulSoup.

Key Points

• Tools and Setup: Using Selenium and BeautifulSoup for web scraping.
• Dynamic Content: Handling and interacting with dynamic web pages.
• Data Extraction: Extracting data from HTML tables and managing pagination.
• Data Cleaning: Normalizing and preparing data for analysis.