E-Books →Hands–On Healthcare Statistics with Python (Early Release)

Author Michael Korvink, a principal data scientist for Premier Healthcare Alliance, provides key characteristics of hypothesis testing, regression analysis, risk standardization, and other common analytical techniques used in interpretable analysis of healthcare quality, safety, and efficiency. This book is ideal for beginner data analysts, data scientists, business analysts, statisticians, and public health and epidemiology researchers.
You will
Explore the range of healthcare data sources in which data analysis can be applied
Learn varying statistical modeling techniques and when to use them
Examine healthcare data sources and risk factors commonly employed in outcomes modeling (including clinical, demographic, and social risk factors)
Learn methods to properly measure hospital performance and physicians
Identify data scenarios where statistical methods are appropriate
Implement a range of statistical techniques using Python
Make predictions, quantify error, and identify meaningful signals in healthcare data