Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. I will also assume that the reader is a beginner in Python or R, at a minimum. We will not discuss setting up a development environment or using the many IDEs available. Furthermore, no crash course on Python or R programming is provided in these pages. In Python, examples primarily use Pandas, NumPy, statsmodels, scipy.stats, and Scikit-learn.

Practical Healthcare Statistics in Python PDF

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