Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.

Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

This book constitutes the refereed proceedings of the 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, held in Gurgaon, India, in October 2017. The 66 revised full papers presented were carefully reviewed and selected from 329 submissions. The papers are organized in topical sections on big data analysis, data centric programming, next generation computing, social and web analytics, security in data science analytics.

This book clarifies based on latest findings and research what one needs to know about marketing and sales automation, how to manage projects to implement them, select and implement tools, and what results can be achieved. It also outlines what can be expected in the future such as the automation of corporate communication and Human Resources. The range of topics spans from the creation of a valid data base in the context of applied AI for realizing predictive intelligence and the effects of data regulations such as the European General Data Protection Regulation (GDPR) when addressing customers and prospects to recommendations for selecting and implementing the necessary IT systems. Experts also report on their experiences in regard to Conversion-rate-optimization (CRO) and provide tips and assistance on how to optimize and ensure the highest RoI for marketing and sales automation. A special focus will be placed on the dovetailing of marketing and sales and the management of the customer journey as well as the improvement of the customer experience.

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods – such as blockchain, big data, artificial intelligence, and cloud computing – so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.

Forty years of progress in the fields of gas chromatography and data collection have culminated in flavoromics. This is a combination of chemometrics and metabolomics. Essentially, it is the non-targeted way of rapidly collecting a significant amount of data from a wide range of sample populations and using the data to study complicated topics. Now that we have the required tools, we can carry out high-throughput trace investigations that incorporate both gustatory and olfactory signals. Flavoromics: An Integrated Approach to Flavor and Sensory Assessment describes the tools to do high-throughput, trace analyses that represent both taste and olfaction stimuli. It explains how today's single sample research will generate thousands of data points, which are loaded into sophisticated statistical analysis algorithms to establish what stimuli are responsible for flavor. This cutting-edge equipment will enable us to create flavorings and perfumes that are more realistic and superior. Key Features: Includes a detailed section on data handling/mining Section 4 describes a broad overview of different food matrices Points out the integration of flavoromics with advanced separation methods, data management, statistical modeling, and variable selection This book represents a revolutionary tool waiting to help make better, truer to life flavorings and fragrances.

This book provides valuable information on effective, state-of-the-art techniques and approaches for governments, students, researchers, practitioners, entrepreneurs and teachers in the field of artificial intelligence (AI). The book explains the data and AI, types and properties of data, the relation between AI algorithms and data, what makes data AI ready, steps of data pre-processing, data quality, data storage and data platforms. Therefore, this book will be interested by AI practitioners, academics, researchers, and lecturers in computer science, artificial intelligence, machine learning and data sciences.

Power plays a central role in business and management. But what is power exactly, and what are key elements of this concept? Defining power as relative ability, this book discusses structures of power, individual power, the exercise of power, strategy, and collective power. While discussing these key components, ideas of important thinkers about power, from Plato to Foucault, Weber to Lukes, Machiavelli to Kahneman, Sun to Kotter, and Barnard to Clegg, are discussed and interpretively categorized into a toolbox of conceptual elements – what Blumer referred to as sensitizing concepts. This toolbox of sensitizing concepts allows the selection of those elements of the concept of power that provide the most constructive and effective practical understanding in particular situations. The core message behind the discussion is that knowledge of key components of the concept of power is empowering. It is empowering to learn about aspects of structures of power, individual power, the exercise of power, strategy, and collective power. Understanding such conceptual components empowers students, researchers, practitioners, and other readers to use their understanding in interpreting, theorizing about, and dealing with the complexities of power in their particular situations – without tying them to any preconceived general theories about power.