Books

Utilisez R comme un SIG pour gérer et cartographier vos données spatiales

The Working Notes complement Applied Data Science for Credit Risk and Probability of Default Rating Modeling with R, offering practice-oriented insights. Based on the author’s GitHub repository, they address real-world challenges and are regularly updated to reflect ongoing developments.

This book provides a practical guide to critical data science methods, focusing on their application in credit risk management. Using examples in R and Python, it presents step-by-step processes for applying various analytical techniques while highlighting the importance of aligning methods with the specific characteristics of the data. Designed for practitioners and those with foundational data science and banking knowledge, the book bridges theory and practice with real-world examples.

Unveil the Secrets of the grapes in the Vineyard! Join us on a journey through the world of wine like never before. Discover the magic of data science and machine learning as we uncork the mysteries hidden in the wine dataset. From predicting to redefining winemaking, get ready for a revolution. Stay tuned for a taste of tomorrow, today!

Disassemble and assemble R bytecode

This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

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".

Discover the thrilling world of Reinforcement Learning (RL) in our engaging eBook! Learn the fundamentals of RL, from AI agents and environments to rewards and actions. Explore real-world applications like robotics, healthcare, and personalized recommendations, where RL is transforming industries.

Build reproducible analytical pipelines to output consistent, high-quality data products using R, Github and Docker. Learn about functional and literate programming to keep your code concise, easier to test and share and easily understandable by others by packaging it. Run your pipelines on Github Actions and focus on what matters: analysing data!

The book covers all the key skills needed for preparing, exploring, and analysing longitudinal data. To facilitate understanding and help readers learn these skills, it interweaves statistical modelling with computer code and visualizations. It does this using real-world data, code, and outputs that readers can replicate.