Data Analysis with Open Source Tools

Data Analysis with Open Source Tools

Author: Philipp K. Janert

Publisher: "O'Reilly Media, Inc."

Published: 2010-11-11

Total Pages: 540

ISBN-13: 9781449396657

DOWNLOAD EBOOK

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora


Book Synopsis Data Analysis with Open Source Tools by : Philipp K. Janert

Download or read book Data Analysis with Open Source Tools written by Philipp K. Janert and published by "O'Reilly Media, Inc.". This book was released on 2010-11-11 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis "Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla "An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora

Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities

Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities

Author: Segall, Richard S.

Publisher: IGI Global

Published: 2020-02-21

Total Pages: 237

ISBN-13: 1799827704

DOWNLOAD EBOOK

With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.


Book Synopsis Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities by : Segall, Richard S.

Download or read book Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities written by Segall, Richard S. and published by IGI Global. This book was released on 2020-02-21 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

Practical Data Analysis

Practical Data Analysis

Author: Dhiraj Bhuyan

Publisher: Dhiraj Bhuyan

Published: 2019-11-30

Total Pages: 323

ISBN-13:

DOWNLOAD EBOOK

“Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com


Book Synopsis Practical Data Analysis by : Dhiraj Bhuyan

Download or read book Practical Data Analysis written by Dhiraj Bhuyan and published by Dhiraj Bhuyan. This book was released on 2019-11-30 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com

Foundations for Architecting Data Solutions

Foundations for Architecting Data Solutions

Author: Ted Malaska

Publisher: "O'Reilly Media, Inc."

Published: 2018-08-29

Total Pages: 190

ISBN-13: 1492038695

DOWNLOAD EBOOK

While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect


Book Synopsis Foundations for Architecting Data Solutions by : Ted Malaska

Download or read book Foundations for Architecting Data Solutions written by Ted Malaska and published by "O'Reilly Media, Inc.". This book was released on 2018-08-29 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect

Data Analytics Using Open-Source Tools

Data Analytics Using Open-Source Tools

Author: Jeffrey Strickland

Publisher: Lulu.com

Published: 2016-07

Total Pages: 708

ISBN-13: 1365213846

DOWNLOAD EBOOK

This book is about using open-source tools in data analytics. The book covers several subjects, including descriptive and predictive modeling, gradient boosting, cluster modeling, logistic regression, and artificial neural networks, among other topics.


Book Synopsis Data Analytics Using Open-Source Tools by : Jeffrey Strickland

Download or read book Data Analytics Using Open-Source Tools written by Jeffrey Strickland and published by Lulu.com. This book was released on 2016-07 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about using open-source tools in data analytics. The book covers several subjects, including descriptive and predictive modeling, gradient boosting, cluster modeling, logistic regression, and artificial neural networks, among other topics.

Guidelines for Preparing Patent Landscape Reports

Guidelines for Preparing Patent Landscape Reports

Author: World Intellectual Property Organization

Publisher: WIPO

Published: 2015-08-24

Total Pages: 131

ISBN-13: 9280525298

DOWNLOAD EBOOK

These Guidelines are designed both for general users of patent information, as well as for those involved in producing Patent Landscape Reports (PLRs). They provide step-by-step instructions on how to prepare a PLR, as well as background information such as objectives, patent analytics, concepts and frameworks.


Book Synopsis Guidelines for Preparing Patent Landscape Reports by : World Intellectual Property Organization

Download or read book Guidelines for Preparing Patent Landscape Reports written by World Intellectual Property Organization and published by WIPO. This book was released on 2015-08-24 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: These Guidelines are designed both for general users of patent information, as well as for those involved in producing Patent Landscape Reports (PLRs). They provide step-by-step instructions on how to prepare a PLR, as well as background information such as objectives, patent analytics, concepts and frameworks.

Open Source Software in Life Science Research

Open Source Software in Life Science Research

Author: Lee Harland

Publisher: Elsevier

Published: 2012-10-31

Total Pages: 583

ISBN-13: 1908818247

DOWNLOAD EBOOK

The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition - high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems. The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an ‘omics’ platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry. Discusses a broad range of applications from a variety of sectors Provides a unique perspective on work normally performed behind closed doors Highlights the criteria used to compare and assess different approaches to solving problems


Book Synopsis Open Source Software in Life Science Research by : Lee Harland

Download or read book Open Source Software in Life Science Research written by Lee Harland and published by Elsevier. This book was released on 2012-10-31 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition - high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems. The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an ‘omics’ platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry. Discusses a broad range of applications from a variety of sectors Provides a unique perspective on work normally performed behind closed doors Highlights the criteria used to compare and assess different approaches to solving problems

Building Open Source Network Security Tools

Building Open Source Network Security Tools

Author: Mike Schiffman

Publisher: John Wiley & Sons

Published: 2002-12-03

Total Pages: 450

ISBN-13: 0471445452

DOWNLOAD EBOOK

Learn how to protect your network with this guide to building complete and fully functional network security tools Although open source network security tools come in all shapes and sizes, a company will eventually discover that these tools are lacking in some area—whether it's additional functionality, a specific feature, or a narrower scope. Written by security expert Mike Schiffman, this comprehensive book will show you how to build your own network security tools that meet the needs of your company. To accomplish this, you'll first learn about the Network Security Tool Paradigm in addition to currently available components including libpcap, libnet, libnids, libsf, libdnet, and OpenSSL. Schiffman offers a detailed discussion of these components, helping you gain a better understanding of the native datatypes and exported functions. Next, you'll find several key techniques that are built from the components as well as easy-to-parse programming examples. The book then ties the model, code, and concepts together, explaining how you can use this information to craft intricate and robust security programs. Schiffman provides you with cost-effective, time-saving guidance on how to build customized network security tools using existing components. He explores: A multilayered model for describing network security tools The ins and outs of several specific security-related components How to combine these components into several useful network security techniques Four different classifications for network security tools: passive reconnaissance, active reconnaissance, attack and penetration, and defensive How to combine techniques to build customized network security tools The companion Web site contains all of the code from the book.


Book Synopsis Building Open Source Network Security Tools by : Mike Schiffman

Download or read book Building Open Source Network Security Tools written by Mike Schiffman and published by John Wiley & Sons. This book was released on 2002-12-03 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to protect your network with this guide to building complete and fully functional network security tools Although open source network security tools come in all shapes and sizes, a company will eventually discover that these tools are lacking in some area—whether it's additional functionality, a specific feature, or a narrower scope. Written by security expert Mike Schiffman, this comprehensive book will show you how to build your own network security tools that meet the needs of your company. To accomplish this, you'll first learn about the Network Security Tool Paradigm in addition to currently available components including libpcap, libnet, libnids, libsf, libdnet, and OpenSSL. Schiffman offers a detailed discussion of these components, helping you gain a better understanding of the native datatypes and exported functions. Next, you'll find several key techniques that are built from the components as well as easy-to-parse programming examples. The book then ties the model, code, and concepts together, explaining how you can use this information to craft intricate and robust security programs. Schiffman provides you with cost-effective, time-saving guidance on how to build customized network security tools using existing components. He explores: A multilayered model for describing network security tools The ins and outs of several specific security-related components How to combine these components into several useful network security techniques Four different classifications for network security tools: passive reconnaissance, active reconnaissance, attack and penetration, and defensive How to combine techniques to build customized network security tools The companion Web site contains all of the code from the book.

Practical Data Analysis

Practical Data Analysis

Author: Hector Cuesta

Publisher: Packt Publishing Ltd

Published: 2016-09-30

Total Pages: 338

ISBN-13: 1785286668

DOWNLOAD EBOOK

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.


Book Synopsis Practical Data Analysis by : Hector Cuesta

Download or read book Practical Data Analysis written by Hector Cuesta and published by Packt Publishing Ltd. This book was released on 2016-09-30 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

R for Data Science

R for Data Science

Author: Hadley Wickham

Publisher: "O'Reilly Media, Inc."

Published: 2016-12-12

Total Pages: 521

ISBN-13: 1491910364

DOWNLOAD EBOOK

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results


Book Synopsis R for Data Science by : Hadley Wickham

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results