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Parallel R
Parallel R

It's tough to argue with R as a high-quality, cross-platform, open source statistical software product - unless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets. You'll learn the basics of Snow, Multicore, Parallel, and some Hadoop-related tools, including how to find them, how to use them, when they work well, and when they don't. With these packages, you can overcome R's single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R's memory barrier. ...
Ethics of Big Data
Ethics of Big Data

What are your organization's policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. Authors Kord Davis and Doug Patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices. ...
Twisted Network Programming Essentials, 2nd Edition
Twisted Network Programming Essentials, 2nd Edition

Get started with Twisted, the event-driven networking framework written in Python. With this introductory guide, you'll learn the key concepts and design patterns to build event-driven client and server applications for many popular networking protocols. You'll also learn the tools to build new protocols using Twisted's primitives. Start by building basic TCP clients and servers, and then focus on deploying production-grade applications with the Twisted Application infrastructure. Along the way, you can play with and extend examples of common tasks you'll face when building network applications. If you're familiar with Python, you're ready for Twisted. ...
Essential SQLAlchemy
Essential SQLAlchemy

Essential SQLAlchemy introduces a high-level open-source code library that makes it easier for Python programmers to access relational databases such as Oracle, DB2, MySQL, PostgreSQL, and SQLite. SQLAlchemy has become increasingly popular since its release, but it still lacks good offline documentation. This practical book fills the gap, and because a developer wrote it, you get an objective look at SQLAlchemy's tools rather than an advocate's description of all the "cool" features. ...
A Developer's Guide to Data Modeling for SQL Server
A Developer's Guide to Data Modeling for SQL Server

Effective data modeling is essential to ensuring that your databases will perform well, scale well, and evolve to meet changing requirements. However, if you're modeling databases to run on Microsoft SQL Server 2008 or 2005, theoretical or platform-agnostic data modeling knowledge isn't enough: models that don't reflect SQL Server's unique real-world strengths and weaknesses often lead to disastrous performance. ...
Big Data Now
Big Data Now

This collection represents the full spectrum of data-related content we've published on O'Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with "What is data science?" and from there we've pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we've covered and identify a number of core data areas. ...
Big Data Now: 2012 Edition
Big Data Now: 2012 Edition

The Big Data Now anthology is relevant to anyone who creates, collects or relies upon data. It's not just a technical book or just a business guide. Data is ubiquitous and it doesn't pay much attention to borders, so we've calibrated our coverage to follow it wherever it goes. In the first edition of Big Data Now, the O'Reilly team tracked the birth and early development of data tools and data science. Now, with this second edition, we're seeing what happens when big data grows up: how it's being applied, where it's playing a role, and the consequences - good and bad alike - of data's ascendance. ...
MongoDB: The Definitive Guide, 2nd Edition
MongoDB: The Definitive Guide, 2nd Edition

Manage the huMONGOus amount of data collected through your web application with MongoDB. This authoritative introduction—written by a core contributor to the project—shows you the many advantages of using document-oriented databases, and demonstrates how this reliable, high-performance system allows for almost infinite horizontal scalability. This updated second edition provides guidance for database developers, advanced configuration for system administrators, and an overview of the concepts and use cases for other people on your project. Ideal for NoSQL newcomers and experienced MongoDB users alike, this guide provides numerous real-world schema design examples. ...
Introduction to Computer Science Using Python
Introduction to Computer Science Using Python

Introduction to Computer Science Using Python: A Computational Problem-Solving Focusintroduces students to programming and computational problem-solving via a back-to-basics, step-by-step, objects-late approach that makes this book easy to teach and learn from. Students are provided with a thorough conceptual grounding in computational problem solving before introducing them to specific Python syntax, thus giving them the background to become successful programmers in any language. Dierbach offers students a thorough grounding in imperative programming before introducing them to object-oriented programming. His step-by-step pedagogical approach makes this an accessible and student-friendly introduction to programming that eases students into program-writing through a variety of hands-on exercises. ...
Data Mining and Business Analytics with R
Data Mining and Business Analytics with R

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. ...
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