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By J. Smith
J. Smith
Articles
November 24,2024
Last Updated: 21 December 2025
Hits: 1287
  • Tableau Visualizations
  • SQLite Database Management
  • CGM Data Analysis
  • Python Data Processing
  • Diabetes Data Analytics

Working with CGM Data: Python, SQLite, and Tableau in a 4-Part Series

%%sql
-- Find the total count of duplicate rows in the CLARITY_DATA table
SELECT SUM(duplicate_count - 1) AS total_duplicates
FROM (
    SELECT COUNT(*) AS duplicate_count
    FROM CLARITY_DATA
    GROUP BY Date, Time, DateTime, Value, Treatment, Source
    HAVING COUNT(*) > 1
) as duplicates;

Abstract

A comprehensive 4-part series on analyzing continuous glucose monitor (CGM) data using Python, SQLite, and Tableau. Each part focuses on a specific step of the process, from building a clean dataset to creating interactive visualizations. Designed to be accessible for readers of all expertise levels, the series provides practical guidance for managing and interpreting CGM data. The post also links to each detailed article, providing a clear pathway for readers to follow the project step by step.

Key Points

Purpose of the Series: Guide readers through the process of analyzing CGM data, demonstrating practical applications of Python, SQLite, and Tableau.

  • Overview of the Steps:
    • Part 1: Build and prepare the base dataset with Python.
    • Part 2: Use SQLite to manage a growing dataset efficiently.
    • Part 3: Clean and process new data for consistency and reliability.
    • Part 4: Create insightful visualizations with Tableau.

Read more: Working with CGM Data: Python, SQLite, and Tableau in a 4-Part Series

Details
By J. Smith
J. Smith
Articles
October 20,2024
Last Updated: 23 November 2024
Hits: 708
  • 5K@EASD
  • Diabetes Management
  • Race Results
  • Gender Participation
  • Tableau Visualizations

5K@EASD Race Results: Trends from 2023 and 2024

5K@EASD Race Results

Abstract

This post explores participation trends from the 2023 and 2024 5K@EASD events, highlighting the role of physical activity in diabetes management. Key insights include geographic distribution, gender-based participation, and race performance trends. Interactive Tableau visualizations offer a detailed, data-driven analysis of how the event's engagement has evolved over two years.

Key Points

  • The 5K@EASD promotes physical activity to support diabetes management, similar to the 5K@ADA.
  • Data from the 2023 and 2024 events are analyzed to highlight trends in participation.
  • Geographic distribution of participants and gender-based trends are explored.
  • Interactive visualizations created in Tableau provide insights into performance and participation metrics.
  • The analysis identifies evolving trends in gender representation and race times across two years.

Read more: 5K@EASD Race Results: Trends from 2023 and 2024

Details
By J. Smith
J. Smith
Articles
August 25,2024
Last Updated: 19 October 2024
Hits: 1018
  • Python
  • Data Retrieval
  • API Handling
  • Large Datasets
  • Data Manipulation

Advanced Data Retrieval with Python

COVID-19, RSV, and Flu
"COVID-19, RSV, and Flu" by NIAID, licensed under CC BY 4.0.

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.

Read more: Advanced Data Retrieval with Python

Details
By J. Smith
J. Smith
Articles
July 6,2024
Last Updated: 19 October 2024
Hits: 1270
  • Tableau
  • Data Visualization
  • 5K@ADA
  • Race Results
  • Race Performance

Visualizing the 5K@ADA Race Results

5K@ADA Race Results

 

Abstract

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.

Read more: Visualizing the 5K@ADA Race Results

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