Tras mucho buscar, al fin he conseguido recopilar una lista de libros que todo Científico de Datos o Ingeniero de Datos debería tener en su biblioteca personal. Sin más dilaciones, he aquí la lista (La descrición de los libros ha sido cogida de Amazon)
Para Data Scientist
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives."Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk
Python Machine Learning
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics. Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization
Learning From Data
Este es uno de los libros principales que hemos usado en mi facultad en la asignatura “Aprendizaje Automático”, está bastante bien, muy teórico pero bien explicado y con ejemplos sencillos de entender.
Data Science from Scratch
In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently.
Machine Learning for Hackers
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks.
Python for Data Analysis
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications.
Agile Data Science
With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.
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Para Data Engineer
Test-Driven Machine Learning
Este libro trata el testeo de software desde otra perspectiva, además de enseñarte qué es el TDD (Test-Driven Development), te enseña a aplicarlo enfocándolo a problemas de Machine Learning. Enfocar los problemas de Machine Learning desde este punto de vista te ayudará a crear modelos más robustos y comparar distinas técnicas de una forma sencilla y modular.
The Second Machine Age
A fundamentally optimistic book, The Second Machine Age will alter how we think about issues of technological, societal, and economic progress.
The Human Face of Big Data
The images and stories captured in The Human Face of Big Data are the result of an extraordinary artistic, technical and logistical juggling act aimed at capturing the human face of the Big Data Revolution. Big Data is defined as the real time collection, analyses and visualisation of vast amounts of the information.
Data Smart: Using Data Science to Transform Information into Insight
Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors.
Dataclysm: Who We Are (When We Think No One's Looking)
Our personal data has been used to spy on us, hire and fire us, and sell us stuff we don’t need. In Dataclysm, Christian Rudder uses it to show us who we truly are.
The Signal and the Noise: Why So Many Predictions Fail-but Some Don't
Sobre Big Data
Hadoop For Dummies
Let Hadoop For Dummies help harness the power of your data and rein in the information overload Big data has become big business, and companies and organizations of all sizes are struggling to find ways to retrieve valuable information from their massive data sets with becoming overwhelmed.
Hadoop: The Definitive Guide
Ready to unlock the power of your data? With this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop.
If you've been asked to maintain large and complex Hadoop clusters, this book is a must. Demand for operations-specific material has skyrocketed now that Hadoop is becoming the de facto standard for truly large-scale data processing in the data center.
Professional Hadoop Solutions
Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition.
MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems
Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using.
Learning Spark: Lightning-Fast Big Data Analysis
Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run.
Advanced Analytics with Spark: Patterns for Learning from Data at Scale
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.
Si te interesan los libros geek, visita nuestra recopilación 16 Libros de No-Ficción que todo Geek debería leer
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