It mainly includes the introduction of Python, research design, data management, probability distribution, hypothesis testing of different data types, generalized linear model, survival analysis and Bayesian statistics.
At the same time, using Python, an open source language, not only provides a good understanding of data analysis and statistical test intuitively, but also explains the relevant mathematical formulas in simple terms. This book is very operable and provides relevant codes and data. Readers can reproduce it according to what is said in the book and deepen their understanding of relevant knowledge.
The author is Thomas Haslwanter, who has more than 65,438+00 years of teaching experience in academic institutions. He is a professor of medical engineering at the Austrian University of Applied Sciences in Linz, a lecturer at the Federal Institute of Technology in Zurich, Switzerland, and a researcher at the University of Sydney in Australia and the University of Tubingen in Germany.
He is experienced in medical research, focusing on the diagnosis, treatment and rehabilitation of vertigo. After fifteen years in-depth use of Matlab, he found Python very powerful and used it in statistical data analysis, sound and image processing and biological simulation applications.
The statistical analysis of python was translated by Li Rui, a doctoral student majoring in epidemiology and biostatistics in School of Public Health, Fudan University. He is a fan of Python, R and Lisp languages. His main research interests are statistical learning of omics data, machine learning modeling and data mining. Published 6 academic papers by the first author, including 4 SCI papers. Participate in 2 Chinese monographs.
Python statistical analysis consists of two parts: Python and statistics, distribution and hypothesis testing.
This book emphasizes the solutions to practical problems and serves as a bridge between statisticians/computer scientists and experimental experts (such as biologists, physicists, doctors, etc.). In order to make readers better understand the contents of this book, the author also provides practical examples and hands-on exercises (with answers at the end of the book), which makes this book widely accessible-from undergraduates of various majors to mature researchers seeking answers to specific questions.
Python statistical analysis is suitable for readers who are interested in statistics and Python, especially students and researchers who need to use Python's powerful functions for experimental data processing and statistical analysis. Interested friends can have a look!