An Introduction to Statistics with Python: With Applications in the Life Sciences

This textbook provides an introduction to Python and its use for statistical data analysis,with updated chapters on Python,data input and visualization,experimental design,confidence intervals,pattern recognition,and useful programming tools. It is intended for masters and PhD students and anyone who wants to perform statistical data analysis.

Format: Hardback
Length: 336 pages
Publication date: 16 November 2022
Publisher: Springer Nature Switzerland AG

This comprehensive textbook,now in its second edition, serves as an introduction to Python and its applications in statistical data analysis. It encompasses a wide range of topics, including common statistical tests for continuous, discrete, and categorical data, linear regression analysis, survival analysis, and Bayesian statistics. The introductory chapters on Python, data input, and visualization have been extensively revised and updated for this edition. The chapter on experimental design has been expanded, and programs for determining confidence intervals commonly used in quality control have been introduced. Additionally, a new chapter has been dedicated to exploring patterns in data, including time series. An appendix provides valuable programming tools, such as testing tools, code repositories, and GUIs. The accompanying working code for Python solutions, along with easy-to-follow examples, further enhances the readers' understanding of the subject matter. Furthermore, accompanying data sets and Python programs are readily available online. As Python continues to evolve with advancements in the ecosystem, it has emerged as a prominent language for scientific computing, providing a robust environment for conducting statistical data analysis.

This textbook is primarily designed for advanced students pursuing master's and Ph.D. degrees in the fields of life and medical sciences. It offers a solid foundation in statistics, making it suitable for individuals who wish to delve into statistical data analysis. However, its comprehensive nature also makes it accessible to anyone with a desire to perform statistical analysis, regardless of their academic background.

The book is organized into nine chapters, each covering a specific aspect of statistical data analysis using Python. The first chapter provides an overview of Python, highlighting its features and benefits as a programming language. It also introduces the basic concepts of object-oriented programming and emphasizes the importance of modularity in software development.

The second chapter focuses on data input and visualization, covering topics such as reading data from files, manipulating data structures, and creating visual representations using libraries like Matplotlib and Seaborn. It also introduces the concept of exploratory data analysis, which involves analyzing data to identify patterns and relationships.

The third chapter introduces common statistical tests for continuous, discrete, and categorical data. It covers descriptive statistics, including measures of central tendency. It also discusses hypothesis testing, including the t-test, chi-square test, and ANOVA. The chapter also covers inferential statistics, including regression analysis and correlation analysis.

The fourth chapter delves into linear regression analysis, covering topics such as linear regression models, residual analysis, and model selection. It also discusses the use of regularization techniques to improve the performance of linear regression models.

The fifth chapter explores survival analysis, which is concerned with analyzing data where the outcome variable is a time-to-event variable. It covers survival curves, Kaplan-Meier curves, and Cox proportional hazards models. The chapter also discusses the use of survival analysis in the context of clinical trials and cancer research.

The sixth chapter introduces Bayesian statistics, which is a statistical approach that incorporates prior knowledge and probabilities into the analysis of data. It covers topics such as Bayesian inference, Markov chain models, and Bayesian networks. The chapter also discusses the use of Bayesian statistics in the context of machine learning and artificial intelligence.

The seventh chapter explores the use of Python for data mining, which involves the extraction of patterns and insights from large datasets. It covers topics such as clustering, classification, and regression. The chapter also discusses the use of machine learning algorithms

The eighth chapter introduces the concept of big data, which refers to the large volumes of structured and unstructured data generated by various sources such as social media, sensors, and scientific experiments. It covers topics such as data preprocessing, data cleaning, and data visualization. The chapter also discusses the use of distributed computing and cloud computing for big data analysis.

The ninth chapter discusses the use of Python for machine learning, which involves the use of algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. The chapter also discusses the use of neural networks and deep learning for machine learning.

In conclusion, this textbook provides a comprehensive and up-to-date introduction to Python and its applications in statistical data analysis. It covers a wide range of topics, from basic programming concepts to advanced statistical techniques, and is designed to cater to the needs of students, researchers, and practitioners in the fields of life and medical sciences. With its extensive coverage, practical examples, and accompanying online resources, it is an invaluable resource for anyone seeking to leverage the power
power of Python for statistical data analysis.

Weight: 672g
Dimension: 161 x 241 x 27 (mm)
ISBN-13: 9783030973704
Edition number: 2nd ed. 2022