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Data Protection for Photographers
by Patrick H. CorriganAll photographers, both amateur and professional, are faced with the important issues of data protection and storage. Without knowledge of the options, tools, and procedures for safe and effective image protection and storage, photographers run the serious risk of losing their image files. This book offers critical information about the best hardware, software, procedures, and practices for capturing, storing, and preserving images and other data. This book explains current data protection and storage technologies in everyday terms. It describes effective procedures for protecting data, from capture to backup and archiving. Descriptions of specific products applicable to Windows, MacOS, and Linux systems are provided.
Data Protection for Software Development and IT: A Practical Introduction
by Ralf KneuperThis book introduces data protection, i.e. the protection of individuals from misuse of their personal data. It provides a special focus on the direct impact on software development, e.g. in the form of functional requirements for software systems resulting from data protection. Chapter 1 provides an initial overview of the basic concepts of data protection and its legal foundations. Chapter 2 then delves deeper into the European General Data Protection Regulation (GDPR), covering in particular its basic concepts, terminology and requirements. Next, the specific implementation and interpretation of GDPR requirements in software and IT are dealt with, starting in chapter 3 with the principles of data protection defined in GDPR and the rights of data subjects in chapter 4. Chapter 5 discusses data transfer between organizations, including the relevant constellations (e.g. through various service providers), the legal framework and its practical implementation. Subsequently, chapter 6 changes the view from individual regulations and their implementation to technical and organizational design of data protection, including its embedding in the software life cycle, while chapter 7 provides an overview of information security and its aspects relevant to data protection. Eventually, chapter 8 deals with data protection for organizations as they are data subjects themselves. The appendices contain the most important excerpts from the Charter of Fundamental Rights of the EU and GDPR in this context, a collection of links to relevant laws and supervisory authorities, as well as a glossary of the most important terms used. The book’s target groups include software developers, IT consultants, requirements analysts, IT operations personnel and project managers in IT projects, but also data protection managers and data protection officers in the context of software development and IT.
Data Protection in a Post-Pandemic Society: Laws, Regulations, Best Practices and Recent Solutions
by Chaminda Hewage Yogachandran Rahulamathavan Deepthi RatnayakeThis book offers the latest research results and predictions in data protection with a special focus on post-pandemic society. This book also includes various case studies and applications on data protection. It includes the Internet of Things (IoT), smart cities, federated learning, Metaverse, cryptography and cybersecurity. Data protection has burst onto the computer security scene due to the increased interest in securing personal data. Data protection is a key aspect of information security where personal and business data need to be protected from unauthorized access and modification. The stolen personal information has been used for many purposes such as ransom, bullying and identity theft. Due to the wider usage of the Internet and social media applications, people make themselves vulnerable by sharing personal data. This book discusses the challenges associated with personal data protection prior, during and post COVID-19 pandemic. Some of these challenges are caused by the technological advancements (e.g. Artificial Intelligence (AI)/Machine Learning (ML) and ChatGPT). In order to preserve the privacy of the data involved, there are novel techniques such as zero knowledge proof, fully homomorphic encryption, multi-party computations are being deployed. The tension between data privacy and data utility drive innovation in this area where numerous start-ups around the world have started receiving funding from government agencies and venture capitalists. This fuels the adoption of privacy-preserving data computation techniques in real application and the field is rapidly evolving. Researchers and students studying/working in data protection and related security fields will find this book useful as a reference.
Data Protection in the Internet (Ius Comparatum - Global Studies in Comparative Law #38)
by Dário Moura Vicente Sofia de Vasconcelos CasimiroThis book identifies and explains the different national approaches to data protection – the legal regulation of the collection, storage, transmission and use of information concerning identified or identifiable individuals – and determines the extent to which they could be harmonised in the foreseeable future. In recent years, data protection has become a major concern in many countries, as well as at supranational and international levels. In fact, the emergence of computing technologies that allow lower-cost processing of increasing amounts of information, associated with the advent and exponential use of the Internet and other communication networks and the widespread liberalization of the trans-border flow of information have enabled the large-scale collection and processing of personal data, not only for scientific or commercial uses, but also for political uses. A growing number of governmental and private organizations now possess and use data processing in order to determine, predict and influence individual behavior in all fields of human activity. This inevitably entails new risks, from the perspective of individual privacy, but also other fundamental rights, such as the right not to be discriminated against, fair competition between commercial enterprises and the proper functioning of democratic institutions. These phenomena have not been ignored from a legal point of view: at the national, supranational and international levels, an increasing number of regulatory instruments – including the European Union’s General Data Protection Regulation applicable as of 25 May 2018 – have been adopted with the purpose of preventing personal data misuse. Nevertheless, distinct national approaches still prevail in this domain, notably those that separate the comprehensive and detailed protective rules adopted in Europe since the 1995 Directive on the processing of personal data from the more fragmented and liberal attitude of American courts and legislators in this respect. In a globalized world, in which personal data can instantly circulate and be used simultaneously in communications networks that are ubiquitous by nature, these different national and regional approaches are a major source of legal conflict.
Data Protection Law: A Comparative Analysis of Asia-Pacific and European Approaches
by Robert Walters Leon Trakman Bruno ZellerThis book provides a comparison and practical guide for academics, students, and the business community of the current data protection laws in selected Asia Pacific countries (Australia, India, Indonesia, Japan Malaysia, Singapore, Thailand) and the European Union.The book shows how over the past three decades the range of economic, political, and social activities that have moved to the internet has increased significantly. This technological transformation has resulted in the collection of personal data, its use and storage across international boundaries at a rate that governments have been unable to keep pace. The book highlights challenges and potential solutions related to data protection issues arising from cross-border problems in which personal data is being considered as intellectual property, within transnational contracts and in anti-trust law. The book also discusses the emerging challenges in protecting personal data and promoting cyber security. The book provides a deeper understanding of the legal risks and frameworks associated with data protection law for local, regional and global academics, students, businesses, industries, legal profession and individuals.
The Data Protection Officer: Profession, Rules, and Role
by Paul LambertThe EU's General Data Protection Regulation created the position of corporate Data Protection Officer (DPO), who is empowered to ensure the organization is compliant with all aspects of the new data protection regime. Organizations must now appoint and designate a DPO. The specific definitions and building blocks of the data protection regime are enhanced by the new General Data Protection Regulation and therefore the DPO will be very active in passing the message and requirements of the new data protection regime throughout the organization. This book explains the roles and responsiblies of the DPO, as well as highlights the potential cost of getting data protection wrong.
Data Protection on the Move: Current Developments in ICT and Privacy/Data Protection (Law, Governance and Technology Series #24)
by Serge Gutwirth Ronald Leenes Paul HertThis volume brings together papers that offer methodologies, conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy and data protection. It is one of the results of the eight annual International Conference on Computers, Privacy, and Data Protection, CPDP 2015, held in Brussels in January 2015. The book explores core concepts, rights and values in (upcoming) data protection regulation and their (in)adequacy in view of developments such as Big and Open Data, including the right to be forgotten, metadata, and anonymity. It discusses privacy promoting methods and tools such as a formal systems modeling methodology, privacy by design in various forms (robotics, anonymous payment), the opportunities and burdens of privacy self management, the differentiating role privacy can play in innovation. The book also discusses EU policies with respect to Big and Open Data and provides advice to policy makers regarding these topics. Also attention is being paid to regulation and its effects, for instance in case of the so-called 'EU-cookie law' and groundbreaking cases, such as Europe v. Facebook. This interdisciplinary book was written during what may turn out to be the final stages of the process of the fundamental revision of the current EU data protection law by the Data Protection Package proposed by the European Commission. It discusses open issues and daring and prospective approaches. It will serve as an insightful resource for readers with an interest in privacy and data protection.
Data Push Apps with HTML5 SSE: Pragmatic Solutions for Real-World Clients
by Darren CookMake sure your website or web application users get content updates right now with minimal latency. This concise guide shows you how to push new data from the server to clients with HTML5 Server-Sent Events (SSE), an exceptional technology that doesn’t require constant polling or user interaction. You’ll learn how to build a real-world SSE application from start to finish that solves a demanding domain problem.You’ll also discover how to increase that application’s desktop and mobile browser support from 60% to 99%, using different fallback solutions. If you’re familiar with HTML, HTTP, and basic JavaScript, you’re ready to get started.Determine whether SSE, WebSockets, or data pull is best for your organizationDevelop a working SSE application complete with backend and frontend solutionsAddress error handling, system recovery, and other issues to make the application production-qualityExplore two fallback solutions for browsers that don’t support SSETackle security issues, including authorization and "disallowed origin"Develop realistic, repeatable data that’s useful in test-driven SSE designLearn SSE protocol elements not covered in the example application
Data Quality: Empowering Businesses with Analytics and AI
by Prashanth SouthekalDiscover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the businessAn essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Data Quality and Trust in Big Data: 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Dubai, UAE, November 12–15, 2018, Revised Selected Papers (Lecture Notes in Computer Science #11235)
by Hakim Hacid Quan Z. Sheng Tetsuya Yoshida Azadeh Sarkheyli Rui ZhouThis book constitutes revised selected papers from the International Workshop on Data Quality and Trust in Big Data, QUAT 2018, which was held in conjunction with the International Conference on Web Information Systems Engineering, WISE 2018, in Dubai, UAE, in November 2018. The 9 papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with novel ideas and solutions related to the problems of exploring, assessing, monitoring, improving, and maintaining the quality of data and trust for Big Data.
Data Quality Engineering in Financial Services: Applying Manufacturing Techniques to Data
by Brian BuzzelliData quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines.You'll get invaluable advice on how to:Evaluate data dimensions and how they apply to different data types and use casesDetermine data quality tolerances for your data quality specificationChoose the points along the data processing pipeline where data quality should be assessed and measuredApply tailored data governance frameworks within a business or technical function or across an organizationPrecisely align data with applications and data processing pipelinesAnd more
Data Quality Fundamentals: A Practitioner's Guide to Building Trustworthy Data Pipelines
by Barr Moses Lior Gavish Molly VorwerckDo your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelinesWrite scripts to make data checks and identify broken pipelines with data observabilityLearn how to set and maintain data SLAs, SLIs, and SLOsDevelop and lead data quality initiatives at your companyLearn how to treat data services and systems with the diligence of production softwareAutomate data lineage graphs across your data ecosystemBuild anomaly detectors for your critical data assets
Data Quality in the Age of AI: Building a foundation for AI strategy and data culture
by Andrew JonesUnlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric cultureKey FeaturesGain a profound understanding of the interplay between data quality and AIExplore strategies to improve data quality with practical implementation and real-world resultsAcquire the skills to measure and evaluate data quality, empowering data-driven decisionsPurchase of the Kindle book includes a free PDF eBookBook DescriptionAs organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What you will learnDiscover actionable steps to establish data quality as the foundation of your data cultureEnhance data quality directly at its source with effective strategies and best practicesElevate data quality standards and enhance data literacy within your organizationIdentify and measure data quality within the datasetAdopt a product mindset to address data quality challengesExplore emerging architectural patterns like data mesh and data contractsAssign roles, responsibilities, and incentives for data generatorsGain insights from real-world case studiesWho this book is forThis report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.
Data Quality Management in the Data Age: Excellence in Data Quality for Enhanced Digital Economic Growth (SpringerBriefs in Service Science)
by Haiyan YuThis book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.
Data Rules: Reinventing the Market Economy (Acting with Technology)
by Jannis Kallinikos Cristina AlaimoA new social science framework for studying the unprecedented social and economic restructuring driven by digital data.Digital data have become the critical frontier where emerging economic practices and organizational forms confront the traditional economic order and its institutions. In Data Rules, Cristina Alaimo and Jannis Kallinikos establish a social science framework for analyzing the unprecedented social and economic restructuring brought about by data. Working at the intersection of information systems and organizational studies, they draw extensively on intellectual currents in sociology, semiotics, cognitive science and technology, and social theory. Making the case for turning &“data-making&” into an area of inquiry of its own, the authors uncover how data are deeply implicated in rewiring the institutions of the market economy.The authors associate digital data with the decentering of organizations. As they point out, centered systems make sense only when firms (and formal organizations more broadly) can keep the external world at arm&’s length and maintain a relative operation independence from it. These patterns no longer hold. Data transform the production of goods and services to an endless series of exchanges and interactions that defeat the functional logics of markets and organizations. The diffusion of platforms and ecosystems is indicative of these broader transformations. Rather than viewing data as simply a force of surveillance and control, the authors place the transformative potential of data at the center of an emerging socioeconomic order that restructures society and its institutions.
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: Best Practices mit Python
by Benjamin M. Abdel-KarimDieses Buch entstand aus der Motivation heraus, eines der ersten deutschsprachigen Nachschlagewerke zu entwickeln, in welchem relativ simple Quellcode-Beispiele enthalten sind, um so Lösungsansätze für die (wiederkehrenden) Programmierprobleme in der Datenanalyse weiterzugeben. Dabei ist dieses Werk nicht uneigennützig verfasst worden. Es enthält Lösungswege für immer wiederkehrende Problemstellungen die ich über meinen täglichen Umgang entwickelt habe Zweifellos gehört das Nachschlagen von Lösungsansätzen in Büchern oder im Internet zur normalen Arbeit eines Programmierers. Allerdings ist diese Suche in der Regel ein unstrukturierter und damit, zumindest teilweise, ein zeitaufwendiger Prozess.Unabhängig davon, ob Sie das Buch als Student, Mitarbeiter oder Gründer lesen, hoffe ich, dass Ihnen dieses Nachschlagewerk ein wertvoller Helfer für die ersten Anfänge sein wird. Ich gehe davon aus, dass jede Person die Grundlagen der Datenanalyse mit Hilfe moderner Programmiersprachen erlernen kann.
Data Science: 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Guilin, China, September 20–23, 2019, Proceedings, Part I (Communications in Computer and Information Science #1058)
by Xiaohui Cheng Weipeng Jing Xianhua Song Zeguang LuThis two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application.
Data Science: Konzepte, Erfahrungen, Fallstudien und Praxis
by Detlev Frick Andreas Gadatsch Jens Kaufmann Birgit Lankes Christoph Quix Andreas Schmidt Uwe SchmitzData Science ist in vielen Organisationen angekommen und oft alltägliche Praxis. Dennoch stehen viele Verantwortliche vor der Herausforderung, sich erstmalig mit konkreten Fragestellungen zu beschäftigen oder laufende Projekte weiterzuentwickeln. Die Spannbreite der Methoden, Werkzeuge und Anwendungsmöglichkeiten ist sehr groß und entwickelt sich kontinuierlich weiter. Die Vielzahl an Publikationen zu Data Science ist spezialisiert und behandelt fokussiert Einzelaspekte. Das vorliegende Werk gibt den Leserinnen und Lesern eine umfassende Orientierung zum Status Quo aus der wissenschaftlichen Perspektive und zahlreiche vertiefende Darstellungen praxisrelevanter Aspekte. Die Inhalte bauen auf den wissenschaftlichen CAS-Zertifikatskursen zu Big Data und Data Science der Hochschule Niederrhein in Kooperation mit der Hochschule Bonn-Rhein-Sieg und der FH Dortmund auf. Sie berücksichtigen wissenschaftliche Grundlagen und Vertiefungen, aber auch konkrete Erfahrungen aus Data Science Projekten. Das Buch greift praxisrelevante Fragen auf wissenschaftlichem Niveau aus Sicht der Rollen eines „Data Strategist“, „Data Architect“ und „Data Analyst“ auf und bindet erprobte Praxiserfahrungen u. a. von Seminarteilnehmern mit ein. Das Buch gibt für Interessierte einen Einblick in die aktuell relevante Vielfalt der Aspekte zu Data Science bzw. Big Data und liefert Hinweise für die praxisnahe Umsetzung.
Data Science: Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22–24, 2017, Proceedings, Part II (Communications in Computer and Information Science #728)
by Qilong Han Weipeng Jing Guanglu Sun Zeguang Lu Beiji Zou Xiaoning PengThis bookconstitutes the refereed proceedings of the First National Conference on BigData Technology and Applications, BDTA 2015, held in Harbin, China, in December2015. The 26revised papers presented were carefully reviewed and selected from numeroussubmissions. The papers address issues such as the storage technology of Big Data;analysis of Big Data and data mining; visualization of Big Data; the parallelcomputing framework under Big Data; the architecture and basic theory of BigData; collection and preprocessing of Big Data; innovative applications in someareas, such as internet of things and cloud computing.
Data Science (The MIT Press Essential Knowledge series)
by John D. Kelleher Brendan TierneyA concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
Data Science: Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22–24, 2017, Proceedings, Part I (Communications in Computer and Information Science #727)
by Min Li Hongzhi Wang Xianhua Song Zeguang Lu Beiji Zou Wei XieWith the ever-growing power to generate, transmit and collect huge amounts of data, information overload is now an imminent problem to mankind. The overwhelming demand for information processing is not just about a better - derstanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects of dataand to help peoplemakeinformed, sensible, andbetter decisions. At present, growing attention has been paid to the study, development and - plication of data mining. As a result there is an urgent need for sophisticated techniques and tools that can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The1stInternationalConferenceonAdvancedDataMiningandApplications (ADMA 2005) aimed to bring together the experts on data mining throughout the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di'erent applied disciplines. The conference attracted 539 online submissions and 63 mailing submissions from 25 di'erent countriesandareas. Allfullpaperswerepeer reviewedbyatleastthreemembers of the Program Committee composed of international experts in data mining ?elds. A total number of 100 papers were accepted for the conference. Amongst them 25 papers were selected as regular papers and 75 papers were selected as short papers, yielding a combined acceptance rate of 17%.
Data Science: Techniques and Intelligent Applications
by Parikshit N Mahalle Ramchandra Mangrulkar Idongesit Williams Pallavi ChavanThis book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science. Key Features • Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science. • Presents predictive outcomes by applying data science techniques to real-life applications. • Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. • Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful.
Data Science: 30th British International Conference on Databases, BICOD 2015, Edinburgh, UK, July 6-8, 2015, Proceedings (Lecture Notes in Computer Science #9147)
by Sebastian ManethThis book constitutes the refereed conference proceedings of the 30th British International Conference on Databases, BICOD 2015 - formerly known as BNCOD (British National Conference on Databases) - held in Edinburgh, UK, in July 2015. The 19 revised full papers, presented together with three invited keynotes and three invited lectures were carefully reviewed and selected from 37 submissions. Special focus of the conference has been "Data Science" and so the papers cover a wide range of topics related to databases and data-centric computation.
Data Science: 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019, Guilin, China, September 20–23, 2019, Proceedings, Part II (Communications in Computer and Information Science #1059)
by Rui Mao Hongzhi Wang Xiaolan Xie Zeguang LuThis two volume set (CCIS 1058 and 1059) constitutes the refereed proceedings of the 5th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2019 held in Guilin, China, in September 2019. The 104 revised full papers presented in these two volumes were carefully reviewed and selected from 395 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including data mining; data base; net work; security; machine learning; bioinformatics; natural language processing; software engineering; graphic images; system; education; application.