Unstructured Data Analytics

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Unstructured Data Analytics

Jean Paul Isson
Unstructured Data Analytics Book PDF

Author : Jean Paul Isson
Publisher : John Wiley & Sons
Page : 432 pages
File Size : 47,7 Mb
Release : 2018-03-13
Category : Computers
ISBN : 9781119129752

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Unstructured Data Analytics by Jean Paul Isson Pdf

Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis.

Unstructured Data Analysis

Matthew Windham
Unstructured Data Analysis Book PDF

Author : Matthew Windham
Publisher : SAS Institute
Page : 166 pages
File Size : 53,9 Mb
Release : 2018-09-14
Category : Computers
ISBN : 9781635267099

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Unstructured Data Analysis by Matthew Windham Pdf

Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Tapping into Unstructured Data

William H. Inmon,Anthony Nesavich
Tapping into Unstructured Data Book PDF

Author : William H. Inmon,Anthony Nesavich
Publisher : Pearson Education
Page : 362 pages
File Size : 55,6 Mb
Release : 2007-12-11
Category : Business & Economics
ISBN : 9780132712910

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Tapping into Unstructured Data by William H. Inmon,Anthony Nesavich Pdf

The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data. William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond. This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike. Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more

Big Data For Dummies

Judith S. Hurwitz,Alan Nugent,Fern Halper,Marcia Kaufman
Big Data For Dummies Book PDF

Author : Judith S. Hurwitz,Alan Nugent,Fern Halper,Marcia Kaufman
Publisher : John Wiley & Sons
Page : 336 pages
File Size : 41,6 Mb
Release : 2013-04-02
Category : Computers
ISBN : 9781118644171

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Big Data For Dummies by Judith S. Hurwitz,Alan Nugent,Fern Halper,Marcia Kaufman Pdf

Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Big Data, Mining, and Analytics

Stephan Kudyba
Big Data Mining and Analytics Book PDF

Author : Stephan Kudyba
Publisher : CRC Press
Page : 306 pages
File Size : 44,5 Mb
Release : 2014-03-12
Category : Computers
ISBN : 9781466568716

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Big Data, Mining, and Analytics by Stephan Kudyba Pdf

This book ties together big data, data mining, and analytics to explain how readers can leverage them to transform their business strategy. Illustrating basic approaches of business intelligence to data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and Internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.

The Text Mining Handbook

Ronen Feldman,James Sanger
The Text Mining Handbook Book PDF

Author : Ronen Feldman,James Sanger
Publisher : Cambridge University Press
Page : 423 pages
File Size : 46,9 Mb
Release : 2007
Category : Computers
ISBN : 9780521836579

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The Text Mining Handbook by Ronen Feldman,James Sanger Pdf

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Data Architecture: A Primer for the Data Scientist

W.H. Inmon,Daniel Linstedt,Mary Levins
Data Architecture A Primer for the Data Scientist Book PDF

Author : W.H. Inmon,Daniel Linstedt,Mary Levins
Publisher : Academic Press
Page : 431 pages
File Size : 47,6 Mb
Release : 2019-04-30
Category : Computers
ISBN : 9780128169179

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Data Architecture: A Primer for the Data Scientist by W.H. Inmon,Daniel Linstedt,Mary Levins Pdf

Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture

Data Analytics and AI

Jay Liebowitz
Data Analytics and AI Book PDF

Author : Jay Liebowitz
Publisher : CRC Press
Page : 187 pages
File Size : 49,5 Mb
Release : 2020-08-06
Category : Computers
ISBN : 9781000094671

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Data Analytics and AI by Jay Liebowitz Pdf

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

Text Analytics

John Atkinson-Abutridy
Text Analytics Book PDF

Author : John Atkinson-Abutridy
Publisher : CRC Press
Page : 201 pages
File Size : 42,9 Mb
Release : 2022-05-03
Category : Computers
ISBN : 9781000581072

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Text Analytics by John Atkinson-Abutridy Pdf

Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources. Features: Easy-to-follow step-by-step concepts and methods Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves Practical programming exercises in Python for each chapter Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797

UNSTRUCTURED DATA ANALYTICS USING MACHINE LEARNING

M. Sangeetha
UNSTRUCTURED DATA ANALYTICS USING MACHINE LEARNING Book PDF

Author : M. Sangeetha
Publisher : Unknown
Page : 58 pages
File Size : 54,7 Mb
Release : 2022-02-02
Category : Electronic
ISBN : 9798885913454

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UNSTRUCTURED DATA ANALYTICS USING MACHINE LEARNING by M. Sangeetha Pdf

We can find easily structured data in our database system such as profile records, transaction records, item records. As time goes by, people think about how to handle unstructured like text, image, data satellite, audio, etc. That might give you something useful to make decisions in your business. In this book various unstructured machine learning techniques for data analytics are discussed.

Win with Advanced Business Analytics

Jean-Paul Isson,Jesse Harriott
Win with Advanced Business Analytics Book PDF

Author : Jean-Paul Isson,Jesse Harriott
Publisher : John Wiley & Sons
Page : 416 pages
File Size : 53,5 Mb
Release : 2012-09-25
Category : Business & Economics
ISBN : 9781118417089

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Win with Advanced Business Analytics by Jean-Paul Isson,Jesse Harriott Pdf

Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.

Visual Analytics for Data Scientists

Natalia Andrienko,Gennady Andrienko,Georg Fuchs,Aidan Slingsby,Cagatay Turkay,Stefan Wrobel
Visual Analytics for Data Scientists Book PDF

Author : Natalia Andrienko,Gennady Andrienko,Georg Fuchs,Aidan Slingsby,Cagatay Turkay,Stefan Wrobel
Publisher : Springer Nature
Page : 440 pages
File Size : 49,6 Mb
Release : 2020-08-30
Category : Computers
ISBN : 9783030561468

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Visual Analytics for Data Scientists by Natalia Andrienko,Gennady Andrienko,Georg Fuchs,Aidan Slingsby,Cagatay Turkay,Stefan Wrobel Pdf

This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.

Data Science and Data Analytics

Amit Kumar Tyagi
Data Science and Data Analytics Book PDF

Author : Amit Kumar Tyagi
Publisher : CRC Press
Page : 482 pages
File Size : 44,6 Mb
Release : 2021-09-22
Category : Computers
ISBN : 9781000423198

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Data Science and Data Analytics by Amit Kumar Tyagi Pdf

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

Practical Text Analytics

Steven Struhl
Practical Text Analytics Book PDF

Author : Steven Struhl
Publisher : Kogan Page Publishers
Page : 272 pages
File Size : 48,9 Mb
Release : 2015-07-03
Category : Business & Economics
ISBN : 9780749474027

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Practical Text Analytics by Steven Struhl Pdf

In an age where customer opinion and feedback can have an immediate, major effect upon the success of a business or organization, marketers must have the ability to analyze unstructured data in everything from social media and internet reviews to customer surveys and phone logs. Practical Text Analytics is an essential daily reference resource, providing real-world guidance on the effective application of text analytics. The book presents the analysis process so that it is immediately understood by the marketing professionals who must use it, so they can apply proven concepts and methods correctly and with confidence. By decoding industry terminology and demonstrating practical application of data models once reserved for experts, Practical Text Analytics shows marketers how to frame the right questions, identify key themes and find hidden meaning from unstructured data. Readers will learn to develop powerful new marketing strategies to elevate customer experience, solidify brand value and elevate reputation. Online resources include self-test questions, chapter review Q&A and an Instructor's Manual with text sources and instructions.

Advanced Data Analytics Using Python

Sayan Mukhopadhyay
Advanced Data Analytics Using Python Book PDF

Author : Sayan Mukhopadhyay
Publisher : Apress
Page : 195 pages
File Size : 51,9 Mb
Release : 2018-03-29
Category : Computers
ISBN : 9781484234501

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Advanced Data Analytics Using Python by Sayan Mukhopadhyay Pdf

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.