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. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. ...
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. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. ...
Big Data Glossary
To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on first-hand experience with these tools in a production environment. This handy glossary also includes a chapter of key terms that help define many of these tool categories: NoSQL Databases, MapReduce, Storage, Servers, Processing, Natural Language Processing, Machine Learning, Visualization, Acquisition, Serialization. ...
To help you navigate the large number of new data tools available, this guide describes 60 of the most recent innovations, from NoSQL databases and MapReduce approaches to machine learning and visualization tools. Descriptions are based on first-hand experience with these tools in a production environment. This handy glossary also includes a chapter of key terms that help define many of these tool categories: NoSQL Databases, MapReduce, Storage, Servers, Processing, Natural Language Processing, Machine Learning, Visualization, Acquisition, Serialization. ...
Data Mashups in R.
How do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia. This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis. ...
How do you use R to import, manage, visualize, and analyze real-world data? With this short, hands-on tutorial, you learn how to collect online data, massage it into a reasonable form, and work with it using R facilities to interact with web servers, parse HTML and XML, and more. Rather than use canned sample data, you'll plot and analyze current home foreclosure auctions in Philadelphia. This practical mashup exercise shows you how to access spatial data in several formats locally and over the Web to produce a map of home foreclosures. It's an excellent way to explore how the R environment works with R packages and performs statistical analysis. ...
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. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing. Use the IPython interactive shell as your primary development environment; Learn basic and advanced NumPy (Numerical Python) features; Get started with data analysis tools in the pandas library; Use high-performance tools to load, clean, transform, merge, and reshape data; Create scatter pl ...
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. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing. Use the IPython interactive shell as your primary development environment; Learn basic and advanced NumPy (Numerical Python) features; Get started with data analysis tools in the pandas library; Use high-performance tools to load, clean, transform, merge, and reshape data; Create scatter pl ...
Spring Data
You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop. Through several sample projects, you'll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. ...
You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop. Through several sample projects, you'll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. ...
Bad Data Handbook
What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they've recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. ...
What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they've recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. ...
Distributed Network Data
Build your own distributed sensor network to collect, analyze, and visualize real-time data about our human environment - including noise level, temperature, and people flow. With this hands-on book, you'll learn how to turn your project idea into working hardware, using the easy-to-learn Arduino microcontroller and off-the-shelf sensors. Authors Alasdair Allan and Kipp Bradford walk you through the entire process, from prototyping a simple sensor node to performing real-time analysis on data captured by a deployed multi-sensor network. ...
Build your own distributed sensor network to collect, analyze, and visualize real-time data about our human environment - including noise level, temperature, and people flow. With this hands-on book, you'll learn how to turn your project idea into working hardware, using the easy-to-learn Arduino microcontroller and off-the-shelf sensors. Authors Alasdair Allan and Kipp Bradford walk you through the entire process, from prototyping a simple sensor node to performing real-time analysis on data captured by a deployed multi-sensor network. ...
Enterprise Data Workflows with Cascading
There is an easier way to build Hadoop applications. With this hands-on book, you'll learn how to use Cascading, the open source abstraction framework for Hadoop that lets you easily create and manage powerful enterprise-grade data processing applications - without having to learn the intricacies of MapReduce. Working with sample apps based on Java and other JVM languages, you'll quickly learn Cascading's streamlined approach to data processing, data filtering, and workflow optimization. This book demonstrates how this framework can help your business extract meaningful information from large amounts of distributed data. ...
There is an easier way to build Hadoop applications. With this hands-on book, you'll learn how to use Cascading, the open source abstraction framework for Hadoop that lets you easily create and manage powerful enterprise-grade data processing applications - without having to learn the intricacies of MapReduce. Working with sample apps based on Java and other JVM languages, you'll quickly learn Cascading's streamlined approach to data processing, data filtering, and workflow optimization. This book demonstrates how this framework can help your business extract meaningful information from large amounts of distributed data. ...
Managing Multimedia and Unstructured Data in the Oracle Database
Multimedia is the new digital frontier. Managers, software architects, administrators and developers need to fully comprehend this exciting new technology as its widespread use and acceptance cannot be ignored any longer. Managing Multimedia and Unstructured Data in the Oracle Database will give you a complete understanding of how to manage all data, especially multimedia. You will learn all the latest terminology, how to set up a database, load digital objects, search on them and even how to sell them. Whether you are a manager or database administrator, this book will give you the knowledge you need to take control of this rapidly growing and industry- changing technology. Technology which is transforming our lives. ...
Multimedia is the new digital frontier. Managers, software architects, administrators and developers need to fully comprehend this exciting new technology as its widespread use and acceptance cannot be ignored any longer. Managing Multimedia and Unstructured Data in the Oracle Database will give you a complete understanding of how to manage all data, especially multimedia. You will learn all the latest terminology, how to set up a database, load digital objects, search on them and even how to sell them. Whether you are a manager or database administrator, this book will give you the knowledge you need to take control of this rapidly growing and industry- changing technology. Technology which is transforming our lives. ...
Distibuted Systems
In today's digital environment, distributed systems are increasingly present in a wide variety of environments, ranging from public software applications to critical systems. Distributed Systems introduces the underlying concepts, the associated design techniques and the related security issues. Distributed Systems: Design and Algorithms, is dedicated to engineers, students, and anyone familiar with algorithms and programming, who want to know more about distributed systems. These systems are characterized by: several components with one or more threads, possibly running on different processors; asynchronous communications with possible additional assumptions (reliability, order preserving, etc.); local views for every component and no shared data between components. ...
In today's digital environment, distributed systems are increasingly present in a wide variety of environments, ranging from public software applications to critical systems. Distributed Systems introduces the underlying concepts, the associated design techniques and the related security issues. Distributed Systems: Design and Algorithms, is dedicated to engineers, students, and anyone familiar with algorithms and programming, who want to know more about distributed systems. These systems are characterized by: several components with one or more threads, possibly running on different processors; asynchronous communications with possible additional assumptions (reliability, order preserving, etc.); local views for every component and no shared data between components. ...