Название: Modern Statistics: Intuition, Math, Python, RАвтор: Mike X Cohen
Издательство: SincXpress Education SRL
Год: 22 December 2023
Страниц: 799
Язык: английский
Формат: pdf (true)
Размер: 59.3 MB
"Modern Statistics: Intuition, Math, Python, R" is an authoritative and comprehensive textbook designed to guide university students and professionals through the intricate world of statistics, Data Science, and Machine Learning. This extensive 700-page volume, with its impressive array of 390 figures and over 35,000 lines of code (all code and a sample chapter are freely available on github), offers a unique blend of theoretical and practical knowledge, making it an indispensable resource for those seeking to deepen their understanding of statistical methodologies and their applications in the modern digital era.
Beginning with foundational concepts, the book delves into what data are and how they can be visualized, setting the stage for more complex subjects. It covers key areas such as descriptive statistics, data simulation, transformations, and data quality assessment and improvement, providing a robust framework for data analysis. The exploration of probability theory, sampling, and distributions paves the way for an in-depth discussion of hypothesis testing, including a detailed examination of the t-test family.
The book places a strong emphasis on the practical implementation of statistical techniques, with extensive Python and R code examples that bring to life the concepts of correlations, confidence intervals, ANOVA, and regression analysis. The chapters on permutation tests, power and sample size calculations, biases, and data communication underscore the importance of robust statistical practice and effective data storytelling in research and professional settings.
It is no understatement to write that modern applied statistics relies 100% on programming. There is simply no way to implement statistical procedures without knowing at least a little bit of coding. Fortunately, there are well-developed coding libraries that implement the low-level details. This means that you don’t need to be a professional programmer to be comfortable applying statistics. But you do need to be comfortable with coding. In this book, I use Python and R. Those are arguably the two most popular languages for modern statistics. I’m pretty sure they won’t always be so popular; some other language will be developed that is better, easier, and faster3 . But the good news is that all programming languages share some similarities, so learning statistics in Python or R will help you apply statistics in any other language. In other words, learning coding is time invested, not time wasted, even if you use a different language in practice. Anyway, ChatGPT (or other advanced language AI) can translate into SAS, MATLAB, Julia, or other languages with decent accuracy.
This textbook is not only a rich source of statistical knowledge but also a practical guide to applying these concepts using the latest software tools. It bridges the gap between statistical theory and real-world data analysis, ensuring readers are equipped to tackle complex data challenges with confidence.
Whether you are a student embarking on your statistical journey or a seasoned professional looking to refine your skills, "Modern Statistics: Intuition, Math, Python, R" is a vital addition to your educational toolkit.
Targeted at those who appreciate the rigor of statistical analysis and the nuances of data interpretation, this book is a testament to the ever-evolving field of statistics and its pivotal role in the age of Data Science and Machine Learning.
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