Blog Series: Earthquake Analysis in Washington State

Earthquake Analysis
Post 1: Earthquake Analysis – The Recipe for Seismic Analysis in Washington State

Discover the essential “ingredients” for analyzing Washington’s earthquakes, from gathering data on locations and magnitudes to understanding the foundational elements that drive seismic activity in the state.
Data Patterns
Post 2: Sifting Through the Ingredients – Exploring Earthquake Data in Washington State

Take a closer look at Washington’s earthquake data through exploratory analysis, identifying patterns in magnitude, depth, and timing. This post reveals which “flavors” make the region’s seismic activity unique.
Mapping Fault Lines
Post 3: Adding a Pinch of Precision – The Statistics Behind Earthquake Patterns in Washington State

Dive into the statistical techniques used to quantify earthquake patterns, from spatial clustering with Moran’s I to frequency analysis with Poisson distributions, revealing the underlying “spice” of Washington’s seismic trends.
Machine Learning Predictions
Post 4: The Main Course – Cooking Up Earthquake Predictions with Machine Learning

Discover how machine learning models, like RandomForest and MLPClassifier, are used to predict the characteristics and frequency of future earthquakes in Washington, providing a powerful tool for seismic forecasting.
Predictive Modeling Insights
Post 5: Plating the Masterpiece – Visualizing Washington’s Earthquake Risk

Explore interactive maps and visualizations that bring Washington’s earthquake data to life, highlighting risk areas, historical trends, and insights to aid in preparedness for future seismic events.

Data Science in Healthcare

Blog Series: K-Means Churn Analysis in Healthcare

Why Patients Ghost Us
Why Patients Ghost Us—and What We Can Do About It

Discover why patients disengage from mental health care. In this post, we explore the concept of patient churn, introduce clustering techniques to identify patient types, and uncover the first clues in solving the churn puzzle.
The Churn Puzzle
The Churn Puzzle: Risk Factors and Predictions

Explore the factors driving patient churn and discover how predictive modeling helps us flag high-risk patients before they disengage. This post uncovers travel time, no-show history, and more as key churn predictors.
Turning Data Into Action
Bridging the Gap: Turning Insights Into Patient Retention Strategies

Learn how data-driven insights are translated into actionable strategies to boost patient retention. From personalized interventions to policy changes, this post highlights real-world solutions making a difference.

Blog Series: A/B Test Analysis in Healthcare

A/B Test Overview
The Empty Chair: Uncovering the Cost of Patient No-Shows

Explore the far-reaching impacts of patient no-shows on healthcare providers and discover how A/B testing provides actionable solutions..
Experiment Design
Navigating the Maze: Charting a Course to Reduce Patient No-Shows

Dive into the details of designing an A/B test to reduce no-shows. Learn how hypothesis crafting, data preparation, and unbiased experimentation can address real-world healthcare challenges.
Statistical Analysis
Unlocking the Door: Insights from Our No-Show Experiment

Explore the results of our A/B test and discover which reminder method proved most effective. This post reveals actionable insights on reducing no-shows and improving patient communication.
Applying Insights
Harvesting the Rewards: The Financial Upside of Patient Attendance

See how reducing no-show rates boosts clinic revenue and operational efficiency. This post breaks down the financial impact and highlights the broader benefits of data-driven strategies..
Case Study
Charting New Waters: Lessons from Our Journey to Reduce No-Shows

Reflect on the key takeaways from our A/B testing journey and discover how these insights can solve challenges across healthcare and beyond. From patient communication to resource optimization, this post explores the broader implications of data-driven decision-making.