| Learning Android, 2nd Edition Posted: 14 Jan 2014 07:51 AM PST 
Book Description Want to build apps for Android devices? This book is the perfect way to master the fundamentals. Written by experts who have taught this mobile platform to hundreds of developers in large organizations and startups alike, this gentle introduction shows experienced object-oriented programmers how to use Android's basic building blocks to create user interfaces, store data, connect to the network, and more. Throughout the book, you'll build a Twitter-like application, adding new features with each chapter. You'll also create your own toolbox of code patterns to help you program any type of Android application with ease. - Become familiar with the Android platform and how it fits into the mobile ecosystem
- Dive into the Android stack, including its application framework and the APK application package
- Learn Android's building blocks: Activities, Intents, Services, Content Providers, and Broadcast Receivers
- Create basic Android user interfaces and organize UI elements in Views and Layouts
- Build a service that uses a background process to update data in your application
Table of Contents
Chapter 1. Android Overview
Chapter 2. Java Review
Chapter 3. The Stack
Chapter 4. Installing and Beginning Use of Android Tools
Chapter 5. Main Building Blocks
Chapter 6. Yamba Project Overview
Chapter 7. Android User Interface
Chapter 8. Fragments
Chapter 9. Intents, Action Bar, and More
Chapter 10. Services
Chapter 11. Content Providers
Chapter 12. Lists and Adapters
Chapter 13. Broadcast Receivers
Chapter 14. App Widgets
Chapter 15. Networking and Web Overview
Chapter 16. Interaction and Animation: Live Wallpaper and Handlers Book Details - Paperback: 288 pages
- Publisher: O’Reilly Media; 2nd Edition (January 2014)
- Language: English
- ISBN-10: 1449319238
- ISBN-13: 978-1449319236
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| Raspberry Pi Cookbook Posted: 14 Jan 2014 07:48 AM PST 
Book Description The world of Raspberry Pi is evolving quickly, with many new interface boards and software libraries becoming available all the time. In this cookbook, prolific hacker and author Simon Monk provides more than 200 practical recipes for running this tiny low-cost computer with Linux, programming it with Python, and hooking up sensors, motors, and other hardware—including Arduino. You'll also learn basic principles to help you use new technologies with Raspberry Pi as its ecosystem develops. Python and other code examples from the book are available on GitHub. This cookbook is ideal for programmers and hobbyists familiar with the Pi through resources such as Getting Started with Raspberry Pi (O'Reilly). - Set up and manage your Raspberry Pi
- Connect the Pi to a network
- Work with its Linux-based operating system
- Use the Pi's ready-made software
- Program Raspberry Pi with Python
- Control hardware through the GPIO connector
- Use Raspberry Pi to run different types of motors
- Work with switches, keypads, and other digital inputs
- Hook up sensors for taking various measurements
- Attach different displays, such as an LED matrix
- Create dynamic projects with Raspberry Pi and Arduino
Table of Contents
Chapter 1. Setup and Management
Chapter 2. Networking
Chapter 3. Operating System
Chapter 4. Software
Chapter 5. Python Basics
Chapter 6. Python Lists and Dictionaries
Chapter 7. Advanced Python
Chapter 8. GPIO Basics
Chapter 9. Controlling Hardware
Chapter 10. Motors
Chapter 11. Digital Inputs
Chapter 12. Sensors
Chapter 13. Displays
Chapter 14. Arduino and Raspberry Pi Appendix A. Parts and Suppliers Book Details - Paperback: 414 pages
- Publisher: O’Reilly Media (December 2013)
- Language: English
- ISBN-10: 1449365221
- ISBN-13: 978-1449365226
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| Learning jQuery Deferreds Posted: 14 Jan 2014 07:45 AM PST 
Book Description Orchestrating asynchronous function calls in JavaScript often leads to callback hell, but there is a reliable way to avoid this painful state of affairs. With this concise and simple guide, you'll learn how to use jQuery deferreds and promises, an elegant approach for managing asynchronous calls in both client and server applications. This book contains 18 examples that use deferreds to solve progressively challenging real-world programming problems, along with 75 stimulating puzzles (and their solutions) that will help you understand how and when to use deferreds. You'll learn new tricks in a fun way, and become immersed in the practice of event-based programming. - Understand the logic behind creating deferreds and returning promises
- Get a structured explanation of jQuery's deferred API
- Delve into the dynamics of using deferreds
- Explore a broad collection of useful deferred recipes developed by the authors
- Gain hands-on experience by solving challenges that accompany each recipe
- Go deeper into deferreds: encounter novel abstractions and mind-bending use cases
Table of Contents
Chapter 1. Introduction
Chapter 2. The jQuery Deferred API
Chapter 3. Deferred Recipes
Chapter 4. More Time in the Mental Gymnasium Appendix A. Hints for Selected Challenges
Appendix B. The Promises/A+ Specification
Appendix C. Converting an ArrayBuffer to Base 64 Book Details - Paperback: 132 pages
- Publisher: O’Reilly Media (December 2013)
- Language: English
- ISBN-10: 1449369391
- ISBN-13: 978-1449369392
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| Programming Elastic MapReduce Posted: 14 Jan 2014 07:42 AM PST 
Book Description Although you don't need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop framework in Amazon Web Services (AWS). Authors Kevin Schmidt and Christopher Phillips demonstrate best practices for using EMR and various AWS and Apache technologies by walking you through the construction of a sample MapReduce log analysis application. Using code samples and example configurations, you'll learn how to assemble the building blocks necessary to solve your biggest data analysis problems. - Get an overview of the AWS and Apache software tools used in large-scale data analysis
- Go through the process of executing a Job Flow with a simple log analyzer
- Discover useful MapReduce patterns for filtering and analyzing data sets
- Use Apache Hive and Pig instead of Java to build a MapReduce Job Flow
- Learn the basics for using Amazon EMR to run machine learning algorithms
- Develop a project cost model for using Amazon EMR and other AWS tools
Table of Contents
Chapter 1. Introduction to Amazon Elastic MapReduce
Chapter 2. Data Collection and Data Analysis with AWS
Chapter 3. Data Filtering Design Patterns and Scheduling Work
Chapter 4. Data Analysis with Hive and Pig in Amazon EMR
Chapter 5. Machine Learning Using EMR
Chapter 6. Planning AWS Projects and Managing Costs Appendix A. Amazon Web Services Resources and Tools
Appendix B. Cloud Computing, Amazon Web Services, and Their Impacts
Appendix C. Installation and Setup Book Details - Paperback: 174 pages
- Publisher: O’Reilly Media (December 2013)
- Language: English
- ISBN-10: 1449363628
- ISBN-13: 978-1449363628
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| Anonymizing Health Data Posted: 14 Jan 2014 07:39 AM PST 
Book Description With this practical book, you will learn proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets. Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors' experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others. - Understand different methods for working with cross-sectional and longitudinal datasets
- Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets
- Reduce the size and complexity of massive datasets without losing key information or jeopardizing privacy
- Use methods to anonymize unstructured free-form text data
- Minimize the risks inherent in geospatial data, without omitting critical location-based health information
- Look at ways to anonymize coding information in health data
- Learn the challenge of anonymously linking related datasets
Table of Contents
Chapter 1. Introduction
Chapter 2. A Risk-Based De-Identification Methodology
Chapter 3. Cross-Sectional Data: Research Registries
Chapter 4. Longitudinal Discharge Abstract Data: State Inpatient Databases
Chapter 5. Dates, Long Tails, and Correlation: Insurance Claims Data
Chapter 6. Longitudinal Events Data: A Disaster Registry
Chapter 7. Data Reduction: Research Registry Revisited
Chapter 8. Free-Form Text: Electronic Medical Records
Chapter 9. Geospatial Aggregation: Dissemination Areas and ZIP Codes
Chapter 10. Medical Codes: A Hackathon
Chapter 11. Masking: Oncology Databases
Chapter 12. Secure Linking
Chapter 13. De-Identification and Data Quality Book Details - Paperback: 212 pages
- Publisher: O’Reilly Media (December 2013)
- Language: English
- ISBN-10: 1449363075
- ISBN-13: 978-1449363079
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