Books

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!

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.

"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.

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.

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!)

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

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

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.

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).

Biological Data Science with R covers data manipulation with dplyr, visualization with ggplot2, essential statistics, survival analysis, RNA-seq analysis, phylogenetic trees, predictive modeling and infectious disease forecasting, text mining and natural language processing, and more.

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 :-).