Python for Data Analysis, 2nd Edition

Python for Data Analysis, 2nd Edition

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions…

Advanced Analytics with Spark

Advanced Analytics with Spark, 2nd Edition

In the second edition of 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. Updated for…

Agile Data Science 2.0

Agile Data Science 2.0

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development…

Oracle ADF Survival Guide

Oracle ADF Survival Guide

Quickly get up to speed with Oracle’s Application Development Framework (ADF). Rapidly build modern, user-friendly applications that will be easy to re-use, expand, and maintain. Oracle ADF Survival Guide covers the latest 12c version and explains all the important concepts and parts, including ADF Faces, ADF Task Flows, ADF…

Cassandra: The Definitive Guide, 2nd Edition

Cassandra: The Definitive Guide, 2nd Edition

Imagine what you could do if scalability wasn’t a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition—updated for Cassandra 3.0—provides the technical details and practical…

Thoughtful Machine Learning with Python

Thoughtful Machine Learning with Python

Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests…

High Performance Spark

High Performance Spark

Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries…

Docker for Data Science

Docker for Data Science

Learn Docker “infrastructure as code” technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world data set to fail to be easily managed. The set may not…

Deep Learning

Deep Learning

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but…