I am David Raymond Kearney, political economist by training, data scientist by profession.
As a data scientist, I create automated member and provider engagement campaigns that leverage e-mail, IVR, SMS and live calls to increase medication adherence.
I also design experiments and employ randomized control trials to improve campaigns by identifying the most effective campaign variants, and productionlize machine learning models to identify members at risk of behaving in a way that contributes to poor health outcomes and most likely to benefit from campaigns.
I got my Ph.D. at Duke University, where my research focused on applying econometric and statistical techniques to explain and predict the allocation of fiscal transfers.
This site, will cover spark, h2o. hive and data science using python.
Posts
MCMC algorithms, sampling problems/sampling techniques, Bayesian data analysis
T-Tests The Purpose, Assumptions, How it Works, and Corrections.
Pandas profiling and Shap values for European Soccer Match Data
Data Science Content
Using quandl zillow api for Chicago and Evanston Home Sale Prices using ARIMA and EWMAs
Data Sources
Working with sqlite databases in Jupyter for Visualizing European Soccer Match Data
Using quandl zillow api for Chicago and Evanston Home Sale Prices using ARIMA and EWMAs
Creating E-Books (.epub) in python using ebooklib
Principal component analysis with sklearn
Working with sqlite databases in Jupyter for Visualizing European Soccer Match Data
Data Science Content
Working with sqlite databases in Jupyter for European Soccer Match Data
Stock Market Analysis of the S&P 500 Index using ARIMA and Seasonal ARIMA for forecasting
Machine Learning in Healthcare notes