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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.

Abstract

Blood glucose monitoring (BGM) through twice daily fingerstick readings has been a fundamental part of tracking and visualizing glucose trends since my type 2 diabetes diagnosis in January 2023. While I now use a continuous glucose monitor (CGM) for continuous tracking, BGM remains an important data source for structured trend analysis, treatment evaluation, and accuracy validation. This project focuses on refining BGM visualizations to align with standardized glucose reporting formats, improving readability and insight into key glucose metrics. The updated approach enhances trend analysis while maintaining the value of fingerstick readings as a reliable reference point.

Key Points

• BGM remains a valuable tool – Despite using a CGM, twice-daily fingerstick readings provide structured data that support trend analysis and treatment evaluation.
• Clear trend visualization – Updated BGM visualizations improve formatting consistency, making it easier to track monthly and daily glucose trends.
• Treatment impact assessment – Data comparisons show significant reductions in average blood glucose and A1c following treatment adjustments.
• Standardized reporting formats – The visualizations now follow structured layouts similar to CGM reports, improving clarity and usability.
• BGM as a complementary data source – Fingerstick readings help verify CGM data, track fasting and postprandial glucose, and provide long-term continuity in data analysis.