Data Science

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. Printed copies are available through Lulu.

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.

"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.

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

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).

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.

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

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

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!

Tips and tricks for using d3.js (version 7), 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 :-).

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