Books

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

Master Kubernetes v1.36+ on your laptop without cloud bills. Build production-grade multi-node clusters using Kind (Kubernetes in Docker). From architecture basics to Ingress, Gateway API, and RBAC.

An illustrated exploration of rules, evidence, interpretation and the limits of formal reasoning.

An illustrated introduction to epistemology: evidence, definitions, models, proof and revising what we know.

Explore data before you trust it. Data Exploration and Visualisation with R shows how to uncover structure, diagnose data-quality problems, challenge misleading patterns, and turn imperfect observations into credible evidence through thoughtful, reproducible visual analysis.

Not another AI coding tutorial. A DX engineering book on using Claude Code to reduce developer friction - backed by METR, DORA, and Faros AI data. Processes first, then tools. 80% deterministic, 20% AI.

"Data Science Project: An Inductive Learning Approach" provides a comprehensive methodology for data science project development, emphasizing software engineering principles essential for reliable solutions. Dr. Filipe Verri, a senior data science project manager, guides readers through the origins, scope, and key concepts of data science. This book covers machine learning, data handling, and rigorous validation techniques, all essential for preparing readers to tackle complex, real-world projects.

The premise of this book is to provide students a hands-on, go-to resource for building a secure, customizable lab environment in order to enable their own specific pursuits.

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 practical book aimed for those familiar with functional programming in Scala who are yet not confident about architecting an application from scratch. Together, we will develop a purely functional application using the best libraries in the Cats ecosystem, while learning about design patterns and best practices.

What is a good software architecture? Why should we bother structuring the code and spending time testing it? If you like spending hours debugging your programs or staying late at the office to recover from a buggy deploy in production this book is definitely NOT for you!

This self-study course written as a book is your first step towards making money by coding and programming from home, and doing that with fun. You will understand how the Ruby language works, and establish a foundation for being successful freelance developer. 

This book teaches you how to assemble and lead a data science enterprise so that your organization can move towards extracting information from big data. This book is based on the acclaimed Johns Hopkins Executive Data Science Specialization. Printed copies of this book are available through Lulu.