Books

Forgotten Muslim Pioneers throughout the Centuries

For every exercise I did my best to connect the specific statistical concepts with R code, and every time I use linear algebra I connect it with a concrete R example. In this book you will not find something such as "this is left as an exercise to the reader".

현대의 오픈 소스 방식으로 과학자가 되는 방법에 대한 책입니다.

Książka o tym, jak być naukowcem we współczesny, otwarto-źródłowy sposób.

關於如何以現代、開源方式成為科學家的書籍。

Um livro sobre como ser um cientista à maneira moderna e de código aberto.

Ein Buch darüber, wie man auf moderne, Open-Source-Weise Wissenschaftler wird.

現代のオープンソース方式で科学者になる方法についての本。

This book explores supervised machine learning for scientific research, addressing its limitations in interpretability, causality, and uncertainty quantification. By unifying philosophical justification with practical solutions, it provides a roadmap for turning machine learning into a rigorous tool for science.

Prologue + Part 1 (PDF version) : In order to put an end to all the religious stuff, Alex - assisted by his girlfriend Nicole - sets out to prove that God doesn't exist with the help of a questionnaire... and boy, is he going to screw things up!

Prologue + Partie 1 (version PDF) : Afin d’en finir avec toutes les histoires de religions, Alex - assisté de sa petite amie Nicole - se met en tête de prouver l’inexistence de Dieu à l’aide d’un questionnaire... et boudiou qu’il va foutre la merde !

Beyond practical applications, the McGinty Equation underscores the beauty and elegance of physics, demonstrating how theoretical concepts can be used to solve complex problems and uncover new truths about the universe. Its potential applications extend beyond quantum mechanics and into other fields, such as biology, finance, and computer science.

Compendium of 50 old math reasoning problems to lead the students to seek unconventional solutions.

Data Cleaning: The Ultimate Practical Guide is a guide to understanding what dirty data is, and how it gets into your dataset.This book will help you prevent most types of dirty data getting into your dataset, and clean out quickly and efficiently the remaining errors, so you can have clean, fit-for-purpose and analysis-ready data.