Books

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!

"It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and find their adventure.

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!

Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof.

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).

If you are an independent learner or an instructor for a data science, statistics, or public health course, check out the open case studies project (www.opencasestudies.org) and this guide which will describe the variety of ways our case studies can be used for hands-on data science activities.

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

Are you an above-average Python coder? Prove it! Coffee Break Python - Mastery Workout helps you boost your Python skills and reach mastery level. The approach is simple: you solve 99 really hard Python puzzles that get harder as you progress with the book. A clear path to Python mastery!

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!)

Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn't work) in the real world.

This book describes the algorithms and procedures used to fit statistical models to data. The material covered is taught in the Advanced Statistical Computing course in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

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.