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Data + Journalism: A Story-Driven Approach to Learning Data Reporting
by Mike Reilley Samantha SunneTaking a hands-on and holistic approach to data, Data + Journalism provides a complete guide to reporting data-driven stories. This book offers insights into data journalism from a global perspective, including datasets and interviews with data journalists from countries around the world. Emphasized by examples drawn from frequently updated sets of open data posted by authoritative sources like the FBI, Eurostat and the US Census Bureau, the authors take a deep dive into data journalism’s "heavy lifting" – searching for, scraping and cleaning data. Combined with exercises, video training supplements and lists of tools and resources at the end of each chapter, readers will learn not just how to crunch numbers but also how to put a human face to data, resulting in compelling, story-driven news stories based on solid analysis. Written by two experienced journalists and data journalism teachers, Data + Journalism is essential reading for students, instructors and early career professionals seeking a comprehensive introduction to data journalism skills.
Data Analysis and Optimization for Engineering and Computing Problems: Proceedings of the 3rd EAI International Conference on Computer Science and Engineering and Health Services (EAI/Springer Innovations in Communication and Computing)
by Pandian Vasant Igor Litvinchev Jose Antonio Marmolejo-Saucedo Roman Rodriguez-Aguilar Felix Martinez-RiosThis book presents the proceedings of The EAI International Conference on Computer Science: Applications in Engineering and Health Services (COMPSE 2019). The conference highlighted the latest research innovations and applications of algorithms designed for optimization applications within the fields of Science, Computer Science, Engineering, Information Technology, Management, Finance and Economics and Health Systems. Focusing on a variety of methods and systems as well as practical examples, this conference is a significant resource for post graduate-level students, decision makers, and researchers in both public and private sectors who are seeking research-based methods for modelling uncertain and unpredictable real-world problems.
Data Analytics for Internet of Things Infrastructure (Internet of Things)
by Rohit Sharma Yan Zhang Gwanggil JeonThis book provides techniques for the deployment of semantic technologies in data analysis along with the latest applications across the field such as Internet of Things (IoT). The authors focus on the use of the IoT and big data in business intelligence, data management, Hadoop, machine learning, cloud, smart cities, etc. They discuss how the generation of big data by IoT has ruptured the existing data processing capacity of IoT and recommends the adoption of data analytics to strengthen solutions. The book addresses the challenges in designing the web based IoT system, provides a comparative analysis of different advanced approaches in industries, and contains an analysis of databases to provide expert systems. The book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of IoT and big data analytics.
Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)
by Sk Hafizul Islam Debabrata Samanta Sanjay ChakrabortyThis book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.
Data Communication and Networks: Proceedings of GUCON 2019 (Advances in Intelligent Systems and Computing #1049)
by Lakhmi C. Jain George A. Tsihrintzis Valentina E. Balas Dilip Kumar SharmaThis book gathers selected high-quality papers presented at the International Conference on Computing, Power and Communication Technologies 2019 (GUCON 2019), organized by Galgotias University, India, in September 2019. The content is divided into three sections – data mining and big data analysis, communication technologies, and cloud computing and computer networks. In-depth discussions of various issues within these broad areas provide an intriguing and insightful reference guide for researchers, engineers and students alike.
Data Communications Network Auditing
by Bruce GriffisThis book contains product specific information based on Cisco router command line interface, and IBM's Net view. It is designed to help us understand the "parts and pieces" of communications and determine how components fit together, and what they look like on your bill.
Data Engineering and Communication Technology: Proceedings of 3rd ICDECT-2K19 (Advances in Intelligent Systems and Computing #1079)
by Roman Senkerik K. Srujan Raju Satya Prasad Lanka V. RajagopalThis book includes selected papers presented at the 3rd International Conference on Data Engineering and Communication Technology (ICDECT-2K19), held at Stanley College of Engineering and Technology for Women, Hyderabad, from 15 to 16 March 2019. It features advanced, multidisciplinary research towards the design of smart computing, information systems, and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence, and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment, and industry.
Data Engineering and Communication Technology: Proceedings of ICDECT 2020 (Lecture Notes on Data Engineering and Communications Technologies #63)
by K. Srujan Raju Boby George K. Ashoka Reddy B. Rama DeviThis book includes selected papers presented at the 4th International Conference on Data Engineering and Communication Technology (ICDECT 2020), held at Kakatiya Institute of Technology & Science, Warangal, India, during 25–26 September 2020. It features advanced, multidisciplinary research towards the design of smart computing, information systems and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment and industry.
Data Journalism and the COVID-19 Disruption (Routledge Research in Journalism)
by Jingrong TongData Journalism and the COVID‑19 Disruption offers an international, multidisciplinary account of how and to what extent the COVID‑19 pandemic has been a blessing for data journalism.Bringing together insights into current developments in data journalism during (and since the onset of) the COVID‑19 pandemic from world‑leading data journalism practitioners and academics, this book draws on case studies and examples from different countries to critically reflect on emerging data journalism practices during the pandemic and their sustainability and implications for journalism and newsroom work in the post‑pandemic era. The chapters document changes in the practice and integration of data journalism into newsrooms and the 24/7 news cycle after the unexpected onset of the pandemic and explore how newsrooms and journalists are coping with the sudden and immense demand for data journalism and related challenges. This book also scrutinises the implications for understanding the roles played by newsroom structure and operation, the uncertain nature of data, and the relationship between journalism and other social entities such as audiences and the state in journalism’s development through times of crisis.Offering a timely contribution to the discussions on how data journalism evolved during a time of crisis, this volume will appeal to scholars and students of data journalism, journalism practice, media and communication studies, and media industry studies.
Data Journalism in the Global South (Palgrave Studies in Journalism and the Global South)
by Saba Bebawi Bruce Mutsvairo Eddy Borges-ReyThis volume seeks to analyse the emerging wave of data journalism in the Global South. It does so by examining trends, developments and opportunities for data journalism in the aforementioned contexts. Whilst studies in this specific form of journalism are increasing in numbers and significance, there remains a dearth of literature on data journalism in less developed regions of the world. By demonstrating an interest in data journalism across countries including Chile, Argentina, the Philippines, South Africa and Iran, among others, this volume contributes to multifaceted transnational debates on journalism, and is a crucial reference text for anyone interested in data journalism in the ‘developing’ world. Drawing on a range of voices from different fields and nations, sharing empirical and theoretical experiences, the volume aims to initiate a global dialogue among journalism practitioners, researchers and students.
Data Literacy: A User′s Guide
by David L. HerzogA practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User′s Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today’s world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.
Data Literacy: A User′s Guide
by David L. HerzogA practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User′s Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today’s world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.
Data Management, Analytics and Innovation: Proceedings Of Icdmai 2018, Volume 2 (Advances In Intelligent Systems and Computing #839)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThe volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2018, Volume 1 (Advances in Intelligent Systems and Computing #808)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThe book presents the latest, high-quality, technical contributions and research findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It discusses state-of-the-art topics as well as the challenges and solutions for future development. It includes original and previously unpublished international research work highlighting research domains from different perspectives. This book is mainly intended for researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 1 (Advances in Intelligent Systems and Computing #1042)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 2 (Advances in Intelligent Systems and Computing #1016)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 1 (Advances in Intelligent Systems and Computing #1174)
by Valentina Emilia Balas Amlan Chakrabarti Neha Sharma Jan MartinovicThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 2 (Advances in Intelligent Systems and Computing #1175)
by Valentina Emilia Balas Amlan Chakrabarti Neha Sharma Jan MartinovicThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Mining Methods for the Content Analyst: An Introduction to the Computational Analysis of Content (Routledge Communication Series)
by Kalev LeetaruWith continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike. Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.
Data Scheduling and Transmission Strategies in Asymmetric Telecommunication Environments
by Abhishek Roy Navrati SaxenaThis book presents a framework for a new hybrid scheduling strategy for heterogeneous, asymmetric telecommunication environments. It discusses comparative advantages and disadvantages of push, pull, and hybrid transmission strategies, together with practical consideration and mathematical reasoning.
Data Science and Artificial Intelligence for Digital Healthcare: Communications Technologies for Epidemic Models (Signals and Communication Technology)
by Fionn Murtagh Marcello Trovati Mohammed Atiquzzaman Pradeep Kumar Singh Mohsen FaridThis book explores current research and development in the area of digital healthcare using recent technologies such as data science and artificial intelligence. The authors discuss how data science, AI, and mobile technologies provide the fundamental backbone to digital healthcare, presenting each technology separately as well covering integrated solutions. The book also focuses on the integration of different multi-disciplinary approaches along with examples and case studies. In order to identify the challenges with security and privacy issues, relevant block chain technologies are identified and discussed. Social aspects related to digital solutions and platforms for healthcare are also discussed and analyzed. The book aims to present high quality, technical contributions in the field of mobile digital healthcare using technologies such as AI, deep learning, IoT and distributed cloud computing.
Data Science and Communication: Proceedings of ICTDsC 2023 (Studies in Autonomic, Data-driven and Industrial Computing)
by João Manuel R. S. Tavares Joel J. P. C. Rodrigues Debajyoti Misra Debasmriti BhattacherjeeThe book presents selected papers from the International Conference on Data Science and Communication (ICTDsC 2023) organized by the Department of Electronics and Communication Engineering and Department of Engineering Science and Humanities (DESH) Siliguri Institute of Technology, India during 23 – 24 March 2023 in Siliguri, India. The book covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cyber security and forensic, network and mobile security, advanced computing, cloud computing, quantum computing, electronics system, Internet of Things, robotics and automations, blockchain and software technology, and digital technologies for future.
Data Science and Information Security: First International Artificial Intelligence Conference, IAIC 2023, Nanjing, China, November 25–27, 2023, Revised Selected Papers, Part II (Communications in Computer and Information Science #2059)
by Yi Pan Hai Jin Jianfeng LuThis 3-volume set, CCIS 2058-2060 constitutes the First International Conference, on Artificial Intelligence, IAIC 2023, held in Nanjing, China, in November 2023. The 85 full papers presented were carefully reviewed and selected from 428 submissions. The papers are clustered in parts on: Artificial Intelligence and Machine Learning; Data Security and information Security; Computer Networks and IoT. The papers present recent research and developments in artificial intelligence and its applications in machine learning, natural language processing, computer vision, robotics, and ethical considerations.
Data Science and Internet of Things: Research and Applications at the Intersection of DS and IoT (Internet of Things)
by Giancarlo Fortino Antonio Liotta Raffaele Gravina Alessandro LongheuThis book focuses on the combination of IoT and data science, in particular how methods, algorithms, and tools from data science can effectively support IoT. The authors show how data science methodologies, techniques and tools, can translate data into information, enabling the effectiveness and usefulness of new services offered by IoT stakeholders. The authors posit that if IoT is indeed the infrastructure of the future, data structure is the key that can lead to a significant improvement of human life. The book aims to present innovative IoT applications as well as ongoing research that exploit modern data science approaches. Readers are offered issues and challenges in a cross-disciplinary scenario that involves both IoT and data science fields. The book features contributions from academics, researchers, and professionals from both fields.
Data Science and Network Engineering: Proceedings ICDSNE 2024 (Lecture Notes in Networks and Systems #1165)
by David Taniar Suyel Namasudra Nirmalya Kar Sarat Kumar PatraThis book includes research papers presented at the International Conference on Data Science and Network Engineering (ICDSNE 2024) organized by the Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India, during July 12–13, 2024. It includes research works from researchers, academicians, business executives, and industry professionals for solving real-life problems by using the advancements and applications of data science and network engineering. This book covers many advanced topics, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), computer networks, blockchain, security and privacy, Internet of things (IoT), cloud computing, big data, supply chain management, and many more. Different sections of this book are highly beneficial for the researchers, who are working in the field of data science and network engineering.