Data Science

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.

El libro abarca los conceptos de probabilidad, inferencia estadística, regresión lineal y machine learning. Les ayudará a desarrollar destrezas como programación en R, wrangling de datos, dplyr, visualización de datos, la creación de algoritmos, organización con UNIX, GitHub y la preparación de documentos con knitr y R markdown.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)

A book about how to be a scientist the modern, open-source way.

This book gives a brief, but rigorous, treatment of regression models intended for practicing Data Scientists.

Stop only using databases — start understanding them. A living study of PostgreSQL internals for backend engineers: how pages, indexes, and WAL actually work, written clearly while learning, not after mastery.

Digital Sustainability: From Principles to Practice shows how digital technologies shape our environmental and societal future, and how we can design them responsibly. It equips students and professionals with concrete tools to build more sustainable software, systems, and digital solutions. Less theory for its own sake, more competence for the reality we’re about to enter.

Essential Python libraries and frameworks that every aspiring data scientist, ML engineer, and Python developer should know.

Develop insights from data with tidy tools. Import, wrangle, visualize, and model data with the Tidyverse R packages.

A rigorous treatment of linear models for self learning data scientists. This book is only available in pdf form.

This book teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever. The material for this book was developed as part of the industry-leading Johns Hopkins Data Science Specialization. Printed versions are available through Lulu (see link below).

Tips and tricks for using d3.js (version 6), one of the leading data visualization tools for the web. It's aimed at getting you started and moving you forward. You can download for FREE or donate to encourage further development if you wish :-).

Google engineers are regular expression masters. Do you want to become one, too? The Smartest Way to Learn Python Regex transforms you into a regular expression master. The book leverages an innovative learning approach: (1) read a chapter, (2) watch a course video, and (3) solve a code puzzle. It's fun!