The Data Industry

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The Data Industry

Chunlei Tang
The Data Industry Book PDF

Author : Chunlei Tang
Publisher : John Wiley & Sons
Page : 217 pages
File Size : 47,5 Mb
Release : 2016-06-13
Category : Mathematics
ISBN : 9781119138402

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The Data Industry by Chunlei Tang Pdf

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

Computing with Data

Guy Lebanon,Mohamed El-Geish
Computing with Data Book PDF

Author : Guy Lebanon,Mohamed El-Geish
Publisher : Springer
Page : 576 pages
File Size : 50,7 Mb
Release : 2018-11-28
Category : Computers
ISBN : 9783319981499

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Computing with Data by Guy Lebanon,Mohamed El-Geish Pdf

This book introduces basic computing skills designed for industry professionals without a strong computer science background. Written in an easily accessible manner, and accompanied by a user-friendly website, it serves as a self-study guide to survey data science and data engineering for those who aspire to start a computing career, or expand on their current roles, in areas such as applied statistics, big data, machine learning, data mining, and informatics. The authors draw from their combined experience working at software and social network companies, on big data products at several major online retailers, as well as their experience building big data systems for an AI startup. Spanning from the basic inner workings of a computer to advanced data manipulation techniques, this book opens doors for readers to quickly explore and enhance their computing knowledge. Computing with Data comprises a wide range of computational topics essential for data scientists, analysts, and engineers, providing them with the necessary tools to be successful in any role that involves computing with data. The introduction is self-contained, and chapters progress from basic hardware concepts to operating systems, programming languages, graphing and processing data, testing and programming tools, big data frameworks, and cloud computing. The book is fashioned with several audiences in mind. Readers without a strong educational background in CS--or those who need a refresher--will find the chapters on hardware, operating systems, and programming languages particularly useful. Readers with a strong educational background in CS, but without significant industry background, will find the following chapters especially beneficial: learning R, testing, programming, visualizing and processing data in Python and R, system design for big data, data stores, and software craftsmanship.

Machine Learning and Data Science in the Power Generation Industry

Patrick Bangert
Machine Learning and Data Science in the Power Generation Industry Book PDF

Author : Patrick Bangert
Publisher : Elsevier
Page : 276 pages
File Size : 41,6 Mb
Release : 2021-01-14
Category : Technology & Engineering
ISBN : 9780128226001

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Machine Learning and Data Science in the Power Generation Industry by Patrick Bangert Pdf

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Big Data Applications in Industry 4.0

P. Kaliraj,T. Devi
Big Data Applications in Industry 4 0 Book PDF

Author : P. Kaliraj,T. Devi
Publisher : CRC Press
Page : 446 pages
File Size : 42,6 Mb
Release : 2022-02-10
Category : Computers
ISBN : 9781000537666

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Big Data Applications in Industry 4.0 by P. Kaliraj,T. Devi Pdf

Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development. Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems. Features An introduction to data science and the types of data analytics methods accessible today An overview of data integration concepts, methodologies, and solutions A general framework of forecasting principles and applications, as well as basic forecasting models including naïve, moving average, and exponential smoothing models A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies The application of Industry 4.0 and Big Data in the field of education The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making

Machine Learning and Data Science in the Oil and Gas Industry

Patrick Bangert
Machine Learning and Data Science in the Oil and Gas Industry Book PDF

Author : Patrick Bangert
Publisher : Gulf Professional Publishing
Page : 290 pages
File Size : 52,5 Mb
Release : 2021-03-04
Category : Science
ISBN : 9780128209141

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Machine Learning and Data Science in the Oil and Gas Industry by Patrick Bangert Pdf

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Data Analytics Applied to the Mining Industry

Ali Soofastaei
Data Analytics Applied to the Mining Industry Book PDF

Author : Ali Soofastaei
Publisher : CRC Press
Page : 232 pages
File Size : 50,6 Mb
Release : 2020-11-12
Category : Computers
ISBN : 9780429781766

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Data Analytics Applied to the Mining Industry by Ali Soofastaei Pdf

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

The Data Gaze

David Beer
The Data Gaze Book PDF

Author : David Beer
Publisher : SAGE
Page : 287 pages
File Size : 40,5 Mb
Release : 2018-10-29
Category : Social Science
ISBN : 9781526463197

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The Data Gaze by David Beer Pdf

A significant new way of understanding contemporary capitalism is to understand the intensification and spread of data analytics. This text is about the powerful promises and visions that have led to the expansion of data analytics and data-led forms of social ordering. It is centrally concerned with examining the types of knowledge associated with data analytics and shows that how these analytics are envisioned is central to the emergence and prominence of data at various scales of social life. This text aims to understand the powerful role of the data analytics industry and how this industry facilitates the spread and intensification of data-led processes. As such, The Data Gaze is concerned with understanding how data-led, data-driven and data-reliant forms of capitalism pervade organisational and everyday life. Using a clear theoretical approach derived from Foucault and critical data studies, the text develops the concept of the data gaze and shows how powerful and persuasive it is. It’s an essential and subversive guide to data analytics and data capitalism.

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry

Chkoniya, Valentina
Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Book PDF

Author : Chkoniya, Valentina
Publisher : IGI Global
Page : 653 pages
File Size : 45,9 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781799869863

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Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry by Chkoniya, Valentina Pdf

The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.

The Data Industry

Chunlei Tang
The Data Industry Book PDF

Author : Chunlei Tang
Publisher : John Wiley & Sons
Page : 216 pages
File Size : 48,7 Mb
Release : 2016-05-03
Category : Mathematics
ISBN : 9781119138426

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The Data Industry by Chunlei Tang Pdf

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

IoT-Based Data Analytics for the Healthcare Industry

Sanjay Kumar Singh,Ravi Shankar Singh,Anil Kumar Pandey,Sandeep S Udmale,Ankit Chaudhary
IoT Based Data Analytics for the Healthcare Industry Book PDF

Author : Sanjay Kumar Singh,Ravi Shankar Singh,Anil Kumar Pandey,Sandeep S Udmale,Ankit Chaudhary
Publisher : Academic Press
Page : 342 pages
File Size : 52,6 Mb
Release : 2020-11-07
Category : Technology & Engineering
ISBN : 9780128214763

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IoT-Based Data Analytics for the Healthcare Industry by Sanjay Kumar Singh,Ravi Shankar Singh,Anil Kumar Pandey,Sandeep S Udmale,Ankit Chaudhary Pdf

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization. The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand. Provides state-of-art methods and current trends in data analytics for the healthcare industry Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques Discusses several potential AI techniques developed using IoT for the healthcare industry Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages

Contemporary Research Methods and Data Analytics in the News Industry

Gibbs, William J.
Contemporary Research Methods and Data Analytics in the News Industry Book PDF

Author : Gibbs, William J.
Publisher : IGI Global
Page : 339 pages
File Size : 46,6 Mb
Release : 2015-07-01
Category : Language Arts & Disciplines
ISBN : 9781466685819

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Contemporary Research Methods and Data Analytics in the News Industry by Gibbs, William J. Pdf

The advent of digital technologies has changed the news and publishing industries drastically. While shrinking newsrooms may be a concern for many, journalists and publishing professionals are working to reorient their skills and capabilities to employ technology for the purpose of better understanding and engaging with their audiences. Contemporary Research Methods and Data Analytics in the News Industry highlights the research behind the innovations and emerging practices being implemented within the journalism industry. This crucial, industry-shattering publication focuses on key topics in social media and video streaming as a new form of media communication as well the application of big data and data analytics for collecting information and drawing conclusions about the current and future state of print and digital news. Due to significant insight surrounding the latest applications and technologies affecting the news industry, this publication is a must-have resource for journalists, analysts, news media professionals, social media strategists, researchers, television news producers, and upper-level students in journalism and media studies. This timely industry resource includes key topics on the changing scope of the news and publishing industries including, but not limited to, big data, broadcast journalism, computational journalism, computer-mediated communication, data scraping, digital media, news media, social media, text mining, and user experience.

A Practical Guide to Data Mining for Business and Industry

Andrea Ahlemeyer-Stubbe,Shirley Coleman
A Practical Guide to Data Mining for Business and Industry Book PDF

Author : Andrea Ahlemeyer-Stubbe,Shirley Coleman
Publisher : John Wiley & Sons
Page : 328 pages
File Size : 53,5 Mb
Release : 2014-03-31
Category : Mathematics
ISBN : 9781118763377

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A Practical Guide to Data Mining for Business and Industry by Andrea Ahlemeyer-Stubbe,Shirley Coleman Pdf

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.

Innovative Applications of Big Data in the Railway Industry

Kohli, Shruti,Kumar, A.V. Senthil,Easton, John M.,Roberts, Clive
Innovative Applications of Big Data in the Railway Industry Book PDF

Author : Kohli, Shruti,Kumar, A.V. Senthil,Easton, John M.,Roberts, Clive
Publisher : IGI Global
Page : 395 pages
File Size : 49,6 Mb
Release : 2017-11-30
Category : Computers
ISBN : 9781522531777

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Innovative Applications of Big Data in the Railway Industry by Kohli, Shruti,Kumar, A.V. Senthil,Easton, John M.,Roberts, Clive Pdf

Use of big data has proven to be beneficial within many different industries, especially in the field of engineering; however, infiltration of this type of technology into more traditional heavy industries, such as the railways, has been limited. Innovative Applications of Big Data in the Railway Industry is a pivotal reference source for the latest research findings on the utilization of data sets in the railway industry. Featuring extensive coverage on relevant areas such as driver support systems, railway safety management, and obstacle detection, this publication is an ideal resource for transportation planners, engineers, policymakers, and graduate-level engineering students seeking current research on a specific application of big data and its effects on transportation.

Industry Unbound

Ari Ezra Waldman
Industry Unbound Book PDF

Author : Ari Ezra Waldman
Publisher : Cambridge University Press
Page : 381 pages
File Size : 45,7 Mb
Release : 2021-09-28
Category : Business & Economics
ISBN : 9781108492423

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Industry Unbound by Ari Ezra Waldman Pdf

Privacy law isn't working. Waldman's groundbreaking work explains why, showing how tech companies manipulate us, our behavior, and our law.

Big Data Applications in the Telecommunications Industry

Ouyang, Ye,Hu, Mantian
Big Data Applications in the Telecommunications Industry Book PDF

Author : Ouyang, Ye,Hu, Mantian
Publisher : IGI Global
Page : 216 pages
File Size : 51,5 Mb
Release : 2016-12-28
Category : Computers
ISBN : 9781522517511

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Big Data Applications in the Telecommunications Industry by Ouyang, Ye,Hu, Mantian Pdf

The growing presence of smart phones and smart devices has caused significant changes to wireless networks. With the ubiquity of these technologies, there is now increasingly more available data for mobile operators to utilize. Big Data Applications in the Telecommunications Industry is a comprehensive reference source for the latest scholarly material on the use of data analytics to study wireless networks and examines how these techniques can increase reliability and profitability, as well as network performance and connectivity. Featuring extensive coverage on relevant topics, such as accessibility, traffic data, and customer satisfaction, this publication is ideally designed for engineers, students, professionals, academics, and researchers seeking innovative perspectives on data science and wireless network communications.