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Summary: "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"-- Provided by publisher.

Summary: "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--Page 4 of cover.

Summary: Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security

Summary: There has been a data rush in the past decade brought about by online communication and, in particular, social media (Facebook, Twitter, Youtube, among others), which promises a new age of digital enlightenment. But social data is compromised: it is being seized by specific economic interests, it leads to a fundamental shift in the relationship between research and the public good, and it fosters new forms of control and surveillance. Compromised Data: From Social Media to Big Data explores how we perform critical research within a compromised social data framework. The expert, international lineup of contributors explores the limits and challenges of social data research in order to invent and develop new modes of doing public research. At its core, this collection argues that we are witnessing a fundamental reshaping of the social through social data mining.

Summary: "Your guide to defining an information architecture for emerging trends like Big Data and the Internet of Things"--Page 1 of cover.

Summary: In From Big Data to Big Profits, Russell Walker investigates the use of Big Data to stimulate innovations in operational effectiveness and business growth. Walker examines the nature of Big Data and how businesses can use it to create new monetization opportunities. Using case studies of Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leaders in the use of Big Data, Walker explores how digital platforms such as mobile apps and social networks are changing the nature of customer interactions and the way Big Data is created and used by companies. Such changes, as Walker points out, will require careful consideration of legal and unspoken business practices as they affect consumer privacy. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which he has developed to assess companies for Big Data readiness and provide direction on the steps necessary to get the most from Big Data.

Summary: This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.-- Source other than Library of Congress.

Summary: "Foreword Big data and analytics promise to change virtually every industry and business function over the next decade. Any organization that gets started early with big data can gain a significant competitive edge. Just as early analytical competitors in the "small data" era (including Capital One bank, Progressive Insurance, and Marriott hotels) moved out ahead of their competitors and built a sizable competitive edge, the time is now for firms to seize the big data opportunity. As this book describes, the potential of big data is enabled by ubiquitous computing and data gathering devices; sensors and microprocessors will soon be everywhere. Virtually every mechanical or electronic device can leave a trail that describes its performance, location, or state. These devices, and the people who use them, communicate through the Internet--which leads to another vast data source. When all these bits are combined with those from other media--wireless and wired telephony, cable, satellite, and so forth--the future of data appears even bigger. The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big data project. Big data is undeniably a big deal, but it needs to be put in context"-- Provided by publisher.

Summary: "'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed 'safe operating zone' for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: small classical data; data from medicine, biology, marketing and finance; high-dimensional data from bioinformatics; functional data from proteomics; and simulated data. High-dimension, low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code and problem sets complete the package. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"-- Provided by publisher.

Summary: Get unprecedented access to thousands of databases. It's called Open Data, and it's revolutionizing business. The business leader's guide to using Open Data to analyze patterns and trends, manage risk, solve problems-and seize the competitive edge Two major trends-the exponential growth of digital data and an emerging culture of disclosure and transparency-have converged to create a world where voluminous information about businesses, government, and the population is becoming visible, accessible, and usable. It's called Open Data, and this book helps leaders harness its power to market and grow their companies. Open Data Now gives you the knowledge and tools to take advantage of this phenomenon in its early stages-and beat the competition to leveraging its many benefits. Joel Gurin is an expert on making complex data sets useful in solving consumer problems, analyzing corporate information, and addressing social issues. He has collaborated with leaders in data, technology, and policy in the U.S. and UK governments, including officials in the White House and 10 Downing Street and at more than 20 U.S. federal agencies.

Summary: Big Data is made up of lots of little data: numbers entered into cell phones,addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and anyother activity that leaves a digital trail.

Summary: "A practical guide to leveraging your data to spur innovation and growth. Your business generates reams of data, but what do you do with it? Reporting is only the beginning. Your data holds the key to innovation and growth - you just need the proper analytics. In Big Data, Big Innovation

Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.

Summary: This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book.

Summary: This reprint focuses on data-driven software solutions and their impact on research and development at the academic, industry, business, and government levels to exploit the hidden knowledge and big data that can be offered to cities and citizens in the future. Data-driven software solutions are different from "traditional" software development projects, as the focus of the main development core is on managing the data (e.g., data store and data quality) and designing behavioral models with the aid of artificial intelligence and machine learning techniques. To this end, new life cycles, algorithms, methods, processes, and tools are required. This reprint is centered on the recent trends and advancements in the field of engineering data-intensive software solutions to address the challenges in developing, testing, and maintaining such data-driven systems, with a focus on the application of data-driven solutions to real-life problems and techniques and algorithms addressing the different challenges of data-driven software engineering.

Summary: This book collects selected high-quality papers published in 2018-2020 to inaugurate the "Natural Hazards" Section of the Geosciences journal. The topics encompass: trends in publications at international level in the field of natural hazards research; the role of Big Data in natural disaster management; assessment of seismic risk through the understanding and quantification of its different components; climatic/hydro-meteorological hazards; and finally, the scientific analysis and disaster forensics of recent natural hazard events. The target audience includes not only specialists, but also graduate students who wish to approach the challenging, but also fascinating

Summary: Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.

Summary: Systems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of "omics" and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients' pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system's predictive power to patient-specific profiling.

Summary: Due to innovation in technology, a new type of patient has been created, the e-patient, characterized by the use of electronic communication tools and commitment to participate in their own care. The extent to which the world of digital health has changed during the COVID-19 pandemic has been widely recognized. Remote medicine has become part of the new normal for patients and clinicians, introducing innovative care delivery models that are likely to endure even if the pendulum swings back to some degree in a post-COVID age. The development of digital applications and remote communication technologies for patients with multiple sclerosis has increased rapidly in recent years. For patients, eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. For HCPs, eHealth technology may facilitate the assessment of clinical disability, analysis of lab and imaging data, and remote monitoring of patient symptoms, adverse events, and outcomes. It may allow time optimization and more timely intervention than is possible with scheduled face-to-face visits. The way we measure the impact of MS on daily life has remained relatively unchanged for decades, and is heavily reliant on clinic visits that may only occur once or twice each year.These benefits are important because multiple sclerosis requires ongoing monitoring, assessment, and management.The aim of this Special Issue is to cover the state of knowledge and expertise in the field of eHealth technology applied to multiple sclerosis, from clinical evaluation to patient education.

Summary: This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.

Summary: The essays in this open access volume identify the key ingredients for success in capitalizing on public investments in scientific projects and the development of large-scale research infrastructures. Investment in science - whether in education and training or through public funding for developing new research tools and technologies - is a crucial priority. Authors from big research laboratories/organizations, funding agencies and academia discuss how investing in science can produce societal benefits as well as identifying future challenges for scientists and policy makers. The volume cites different ways to assess the socio-economic impact of Research Infrastructures and their role as hubs of global collaboration, creativity and innovation. It highlights the different benefits stemming from fundamental research at the local, national and global level, while also inviting us to rethink the notion of "benefit" in the 21st century. Public investment is required to maintain the pace of technological and scientific advancements over the next decades. Far from advocating a radical transformation and massive expansion in funding, the authors suggest ways for maintaining a strong foundation of science and research to ensure that we continue to benefit from the outputs. The volume draws inspiration from the first "Economics of Big Science" workshop, held in Brussels in 2019 with the aim of creating a new space for dialogue and interaction between representatives of Big Science organizations, policy makers and academia. It aspires to provide useful reading for policy makers, scientists and students of science, who are increasingly called upon to explain the value of fundamental research and adopt the language and logic of economics when engaging in policy discussions.

Summary: This open access book explores the concept of Industry 4.0, which presents a considerable challenge for the production and service sectors. While digitization initiatives are usually integrated into the central corporate strategy of larger companies, smaller firms often have problems putting Industry 4.0 paradigms into practice. Small and medium-sized enterprises (SMEs) possess neither the human nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. Addressing this obstacle, the international team of authors focuses on the development of smart manufacturing concepts, logistics solutions and managerial models specifically for SMEs. Aiming to provide methodological frameworks and pilot solutions for SMEs during their digital transformation, this innovative and timely book will be of great use to scholars researching technology management, digitization and small business, as well as practitioners within manufacturing companies.

Summary: This book features the manuscripts accepted for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the "Applications in Electronics Pervading Industry, Environment and Society" (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

Summary: This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field.

Summary: The Journal of Risk and Financial Management (JRFM) was inaugurated in 2008 and has successfully continued publishing, with Volume 13 in 2020. Since the journal was established, JRFM has published in excess of 580 topical and interesting theoretical and empirical papers in financial economics, financial econometrics, banking, finance, mathematical finance, statistical finance, accounting, decision sciences, information management, tourism economics and finance, international rankings of journals in financial economics, and bibliometric rankings of journals in cognate disciplines. Papers published in the journal range from novel technical and theoretical papers to innovative empirical contributions. The journal wishes to encourage critical review papers on topical subjects in any of the topics mentioned above in financial economics and in cognate disciplines.

Summary: Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,

Summary: In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.

Summary: The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production-consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today's ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities - smart cities;Smart systems for cultural heritage sustainability.

Summary: Information Storage and Retrieval; Innovation/Technology Management; Computer Applications; Computers and Society; Big data

Summary: En aquest volum del Grup d'Opinió de l'Observatori de Bioètica i Dret, coordinat per les doctores María Casado, Maria Rosa Llàcer i Lídia Buisan, s'analitzen, des de la perspectiva bioètica, els inconvenients de l'explotació i la comercialització de dades dels usuaris de la sanitat pública arran dels problemes detectats en el projecte VISC+ de la Generalitat de Catalunya, tant en relació amb possibles vulneracions dels drets dels ciutadans com amb la manca de transparència i de debat públic informat en una qüestió de tanta importància com és el tràfic de dades personals. El document posa de manifest que la implementació de les tecnologies Big Data en l'àmbit sanitari, associada a una eventual comercialització d'aquestes dades, impacta directament en el nostre sistema sanitari i investigador i afecta de ple a l'àmbit privat dels ciutadans.

Summary: We invite you to read the Special Issue on The Financial Industry 4.0. It is a collection of 13 articles published in a Special Issue of International Journal of Financial Studies (MDPI) in 2020-2022. The main emphasis of this reprint is on The Financial Industry 4.0 to provide insightful understanding about the benefits as well as the challenges that financial institutions are facing under the Industry 4.0 era. The articles in this Special Issue discussed the potential of blockchain technology, the impact of fintech on financial inclusion in developing countries, the role of fintech in the insurance industry, and so on. It highlights the benefits of fintech, such as improved efficiency, accuracy, and customer experience, but also notes the challenges and risks involved, such as data privacy and security concerns. Collaboration between financial institutions, regulators, and technology firms is seen as necessary to promote innovation and ensure the stability and security of the financial system while addressing the challenges and risks associated with fintech.

Summary: Production and consumption activities have determined a weakness of the sustainable real estate economy. The main problems are the subordination of public decision making, which is subjected to pressure from big companies; inefficient appraisal procedures; excessive use of financial leverage in investment projects; the atypical nature of markets; income positions in urban transformations; and the financialization of real estate markets, with widespread negative effects. A delicate role in these complex problems is assigned to real estate appraisal activities, called to make value judgments on real estate goods and investment projects, the prices of which are often formed in atypical real estate markets, giving ever greater importance to sustainable development and transformation issues. This Special Issue is dedicated to developing and disseminating knowledge and innovations related to most recent real estate evaluation methodologies applied in the fields of architecture and civil, building, environmental, and territorial engineering. Suitable works include studies on econometric models, sustainable building management, building costs, risk management and real estate appraisal, mass appraisal methods applied to real estate properties, urban and land economics, transport economics, the application of economics and financial techniques to real estate markets, the economic valuation of real estate investment projects, the economic effects of building transformations or projects on the environment, and sustainable real estate.

Summary: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Summary: Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.

Summary: Sustainable marketing practice is essential for developing a more comprehensive understanding of consumers' purchase decisions in dynamic digital marketing environments. Scholars and practitioners conceive sustainable marketing practices as episodic, predicated on temporal practices in response to emerging digital environments. Consumers are increasingly becoming aware of the ecological issues that their consumption creates in the marketplace. Despite the importance of sustainable practices, when and how sustainability occurs regarding the consumer's purchase decision remains largely unexplored. In part, this is because the practices of sustainability in the emerging computer-mediated marketing environments (CMMEs) are difficult to anticipate and study. Much of what we know about sustainable marketing practice is mainly focused on customer-brand relationships. Prior literature examining sustainable marketing practice through CMMEs remains sparse, despite consistent emphasis on the benefits of sustainable marketing practices in the emerging digital world.

Summary: "Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels"--Back cover.

Summary: A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges https://www.crcpress.com/Swarm-Intelligence-Methods-for-Statistical-Regression/Mohanty/p/book/9781138558182

Summary: Since both IoT and Big Data have a lot of overlap, it is an ideal time to present the recent advances which are taking place at the intersections of both these fields to identify future trends. The book covers the important aspects of Big Data-enabled IoT. The main focus of the book is on the analytical techniques for handling the huge amount of data generated by the IoT. The book is oriented toward those professionals and researchers interested in both of these booming fields. The topics covered in the book will be of interest to computing researchers, practitioners, engineers, and Information Technology professionals working in the highly dynamic field of Big Data-enabled IoT. The book can be viewed as an introduction to the area, as it cover the most important issues, presenting applied research works. The book identifies and shows the research challenges that are yet to be solved. Thus, it can also be used by researchers starting their work in the area

Summary: If you still have a doubt in contemplating the value of big data management and analytics, then this book is for you. It is intended to fill the current gaps in the thinking shift of the data stakeholders by providing elaborative discussions on big data ecosystems, fundamentals, management and analytics in a readable and straight-forward fashion. Business leaders, analysts, data engineers and scholars will be the primary beneficiaries of this pristine book which aims at enriching the journey of big data management and analytics through a very pragmatic approach. Attaining big data business leadership is a challenging task. Nonetheless, it is not only achievable but also potentially the most rewarding. More and more value is demanded from businesses, both internally and externally, to attain competitive edge. The recent surge in big data ecosystem and landscape has left them wondering how to develop the insight, best practices and data leadership. Some industries are acquiring technical skill-set at their workplace to harness big data analytics projects, while some other sectors are focusing on building technological and infrastructural capability by including a portfolio of big data ecosystems. This book proffers big data business leadership model that will enable industries to develop their own big data journey pathways, competencies and competitiveness.

Summary: Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. https://www.apress.com/in/book/9781484228654