Books

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

"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.

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

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.

Zefs Guide to Deep Learning is a short guide to the most important concepts in deep learning, the technique at the center of the current artificial intelligence revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 pages!

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!

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

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí

This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. This book is based on the Coursera Class "Developing Data Products" as part of the Data Science Specialization. Particular emphasis is paid to developing Shiny apps and interactive graphics.

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).

Data is changing nearly everything about our world, but many people don't know where to start. This book is designed for “non-technical” people (especially students) who want a hands-on introduction to data using free or open tools.

The book provides a modern look at introductory Biostatistical concepts and the associated computational tools using the latest developments in computation and visualization in the R language environment. The book includes practical data analysis based on datasets that can be downloaded here: https://github.com/muschellij2/biostatmethods.

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

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.

Longitudinal Data Analysis Using R is a practical, end-to-end guide to longitudinal data analysis that helps you move from raw data to credible substantive conclusions. It tackles the real pain points researchers face in longitudinal analysis, such as data preparation, exploring change over time, model choice, missing data, and interpretation, using clear explanations, real-world data, and fully reproducible R code.