A well thought out, fit-for-purpose data strategy is vital to modern data-driven businesses. This book is your essential guide to planning, developing and implementing such a strategy, presenting a framework which takes you from data strategy definition to successful strategy delivery and execution with support and engagement from stakeholders. Key topics include data-driven business transformation, change enablers, benefits realisation and measurement.

Der Begriff "Open-Source-Software" wurde vor knapp zehn Jahren gepragt und hat inzwischen eine feste Position im IT-Markt erobert. Heute wird Open-Source-Software von praktisch allen im IT-Markt vertretenen Unternehmen entwickelt. In dem vorliegenden Buch werden folgende Fragen erortert: Wie wirkt sich die zunehmende Verbreitung und Nutzung von Open-Source-Software und offenen Standards auf Wettbewerb aus? Konnen wirtschaftspolitische Massnahmen die Vorteile eines offenen Wettbewerbs und die Nutzung neuer Innovationspotenziale unterstutzen? Es wird ein Beitrag geleistet zum okonomischen Verstandnis der Konzepte von Open-Source-Software und offenen Standards. Theoretische Analyse und wirtschaftspolitische Praxis werden miteinander verbunden. Anhand eines internationalen Vergleichs werden verschiedene nationale Open-Source-Strategien analysiert, bewertet und weiterer Handlungsbedarf angezeigt mit Vorschlagen, wie Wettbewerb auf Softwaremarkten offener gestaltet werden kann."

Data is fundamentally changing the nature of businesses and organisations and the mechanisms for delivering products and services. This book is a practical guide to developing strategy and policy for data governance, in line with the developing ISO 38505 governance of data standards. It will assist an organisation wanting to become more of a data driven business by explaining how to assess the value, risks and constraints associated with collecting, using and distributing data.

Das vorliegende Buch bietet erstmalig eine fundierte Gesamtdarstellung uber den Aufbau, den Betrieb und die Funktionsweise von SAP auf Linux. Dabei werden die grundlegenden Konzepte, die Systemarchitektur und deren Implementierung von SAP auf Linux von langjahrigen Kennern der Materie praxisnah, fundiert und technisch detailliert dargestellt.

Dem erfahrenen Linux-Fachmann wird damit das notige Rustzeug an die Hand gegeben, um einen soliden Einstieg in die SAP-Welt zu finden, und der SAP-Administrator findet zuverlassige Informationen, um ein SAP-System auf Linux sicher installieren und optimal betreiben zu konnen.

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To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

There has been intense excitement in recent years around activities labeled "data science," "big data," and "analytics." However, the lack of clarity around these terms and, particularly, around the skill sets and capabilities of their practitioners has led to inefficient communication between "data scientists" and the organizations requiring their services. This lack of clarity has frequently led to missed opportunities. To address this issue, we surveyed several hundred practitioners via the Web to explore the varieties of skills, experiences, and viewpoints in the emerging data science community. We used dimensionality reduction techniques to divide potential data scientists into five categories based on their self-ranked skill sets (Statistics, Math/Operations Research, Business, Programming, and Machine Learning/Big Data), and four categories based on their self-identification (Data Researchers, Data Businesspeople, Data Engineers, and Data Creatives). Further examining the respondents based on their division into these categories provided additional insights into the types of professional activities, educational background, and even scale of data used by different types of Data Scientists. In this report, we combine our results with insights and data from others to provide a better understanding of the diversity of practitioners, and to argue for the value of clearer communication around roles, teams, and careers.

In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in economic analysis, decision-making and business planning. This textbook, "Exploratory Data Analysis in Business and Economics", aims to familiarise students of economics and business as well as practitioners in firms with the basic principles, techniques, and applications of descriptive statistics and data analysis. Drawing on practical examples from business settings, it demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA, thus treating all of the topics typically covered in a university course on descriptive statistics. The German edition of this textbook is one of the "bestsellers" on the German market for literature in statistics.

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.

Quantitative Intelligence Analysis describes the model-based method of intelligence analysis that represents the analyst's mental models of a subject, as well as the analyst's reasoning process exposing what the analyst believes about the subject, and how they arrived at those beliefs and converged on analytic judgments. It includes: *Specific methods of explicitly representing the analyst's mental models as computational models; *dynamic simulations and interactive analytic games; *the structure of an analyst's mental model and the theoretical basis for capturing and representing the tacit knowledge of these models explicitly as computational models detailed description of the use of these models in rigorous, structured analysis of difficult targets; *model illustrations and simulation descriptions; *the role of models in support of collection and operations; *case studies that illustrate a wide range of intelligence problems; *And a recommended curriculum for technical analysts.

Este libro forma parte del proyecto Transformacion funcional de la literatura infantil y juvenil en la sociedad multimedia. Aplicacion de un modelo teorico de critica a las adaptaciones audiovisuales en espanol de las obras infantiles inglesas y alemanas y tiene un doble objetivo: por una parte, analizar como se adaptaron obras de literatura inglesa y alemana al medio audiovisual y como los filmes ingleses y alemanes se trasvasaron al espanol peninsular y, por otra, estudiar la calidad de los libros infantiles - y de sus traducciones al espanol -, que surgen a partir de estos productos audiovisuales. El analisis de las adaptaciones audiovisuales incluye tanto criterios tecnicos como traductologicos, y el estudio de los libros derivados se lleva a cabo siguiendo criterios literarios y traductologicos, en el caso de los analisis de las traducciones de estos productos.

Predator-prey interactions are ubiquitous, govern the flow of energy up trophic levels, and strongly influence the structure of ecological systems. They are typically quantified using the functional response - the relationship between a predator's foraging rate and the availability of food. As such, the functional response is central to how all ecological communities function - since all communities contain foragers - and a principal driver of the abundance, diversity, and dynamics of ecological communities. The functional response also reflects all the behaviors, traits, and strategies that predators use to hunt prey and that prey use to evade predation. It is thus both a clear reflection of past evolution, including predator-prey arms races, and a major force driving the future evolution of both predator and prey. Despite their importance, there have been remarkably few attempts to synthesize or even briefly review functional responses. This novel and accessible book fills this gap, clearly demonstrating their crucial role as the link between individuals, evolution, and community properties, representing a highly-integrated and measurable aspect of ecological function. It provides a clear entry point for students, a refresher for more advanced researchers, and a motivator for future research. Predator Ecology is an advanced textbook suitable for graduate students and researchers in ecology and evolutionary biology seeking a broad, up-to-date, and authoritative coverage of the field. It will also be of relevance and use to mathematical ecologists, wildlife biologists, and anyone interested in predator-prey interactions.

Environmental science (ecology, conservation, and resource management) is an increasingly quantitative field. A well-trained ecologist now needs to evaluate evidence generated from complex quantitative methods, and to apply these methods in their own research. Yet the existing books and academic coursework are not adequately serving most of the potential audience - instead they cater to the specialists who wish to focus on either mathematical or statistical aspects, and overwhelmingly appeal to those who already have confidence in their quantitative skills. At the same time, many texts lack an explicit emphasis on the epistemology of quantitative techniques. That is, how do we gain understanding about the real world from models that are so vastly simplified? This accessible textbook introduces quantitative ecology in a manner that aims to confront these limitations and thereby appeal to a far wider audience. It presents material in an informal, approachable, and encouraging manner that welcomes readers with any degree of confidence and prior training. It covers foundational topics in both mathematical and statistical ecology before describing how to implement these concepts to choose, use, and analyse models, providing guidance and worked examples in both spreadsheet format and R. The emphasis throughout is on the skilful interpretation of models to answer questions about the natural world. Introduction to Quantitative Ecology is suitable for advanced undergraduate students and incoming graduate students, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world ecology, conservation, and resource management scenarios.

Demography is everywhere in our lives: from birth to death. Indeed, the universal currencies of survival, development, reproduction, and recruitment shape the performance of all species, from microbes to humans. The number of techniques for demographic data acquisition and analyses across the entire tree of life (microbes, fungi, plants, and animals) has drastically increased in recent decades. These developments have been partially facilitated by the advent of technologies such as GIS and drones, as well as analytical methods including Bayesian statistics and high-throughput molecular analyses. However, despite the universality of demography and the significant research potential that could emerge from unifying: (i) questions across taxa, (ii) data collection protocols, and (iii) analytical tools, demographic methods to date have remained taxonomically siloed and methodologically disintegrated. This is the first book to attempt a truly unified approach to demography and population ecology in order to address a wide range of questions in ecology, evolution, and conservation biology across the entire spectrum of life. This novel book provides the reader with the fundamentals of data collection, model construction, analyses, and interpretation across a wide repertoire of demographic techniques and protocols. It introduces the novice demographer to a broad range of demographic methods, including abundance-based models, life tables, matrix population models, integral projection models, integrated population models, individual based models, and more. Through the careful integration of data collection methods, analytical approaches, and applications, clearly guided throughout with fully reproducible R scripts, the book provides an up-to-date and authoritative overview of the most popular and effective demographic tools. Demographic Methods across the Tree of Life is aimed at graduate students and professional researchers in the fields of demography, ecology, animal behaviour, genetics, evolutionary biology, mathematical biology, and wildlife management.

The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced. Whether at the undergraduate, graduate, or post-graduate level, this book provides the tools needed to properly analyze your data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner, ensuring that readers come away with the knowledge of which analyses they should use and when they should use them. The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the 'go-to' stats program throughout the life-sciences. Furthermore, by using a single, real-world dataset throughout the book, readers are encouraged to become deeply familiar with an imperfect but realistic set of data. Indeed, early chapters are specifically designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria. This new edition includes the latest advances in R and related software and has been thoroughly "road-tested" over the last decade to create a proven textbook that teaches linear and generalized linear model analysis to students of ecology, evolution, and environmental studies (including worked analyses of data sets relevant to all three disciplines). While R is used throughout, the focus remains firmly on statistical analysis. The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution and environmental studies.

Plan, design, develop, and manage robust Power BI solutions to generate meaningful insights and make data-driven decisions. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Master the latest dashboarding and reporting features of Microsoft Power BI Combine data from multiple sources, create stunning visualizations and publish Power BI apps to thousands of users Get the most out of Microsoft Power BI with real-world use cases and examples Book DescriptionMastering Microsoft Power BI, Second Edition, provides an advanced understanding of Power BI to get the most out of your data and maximize business intelligence. This updated edition walks through each essential phase and component of Power BI, and explores the latest, most impactful Power BI features. Using best practices and working code examples, you will connect to data sources, shape and enhance source data, and develop analytical data models. You will also learn how to apply custom visuals, implement new DAX commands and paginated SSRS-style reports, manage application workspaces and metadata, and understand how content can be staged and securely distributed via Power BI apps. Furthermore, you will explore top report and interactive dashboard design practices using features such as bookmarks and the Power KPI visual, alongside the latest capabilities of Power BI mobile applications and self-service BI techniques. Additionally, important management and administration topics are covered, including application lifecycle management via Power BI pipelines, the on-premises data gateway, and Power BI Premium capacity. By the end of this Power BI book, you will be confident in creating sustainable and impactful charts, tables, reports, and dashboards with any kind of data using Microsoft Power BI. What you will learn Build efficient data retrieval and transformation processes with the Power Query M language and dataflows Design scalable, user-friendly DirectQuery, import, and composite data models Create basic and advanced DAX measures Add ArcGIS Maps to create interesting data stories Build pixel-perfect paginated reports Discover the capabilities of Power BI mobile applications Manage and monitor a Power BI environment as a Power BI administrator Scale up a Power BI solution for an enterprise via Power BI Premium capacity Who this book is forBusiness Intelligence professionals and intermediate Power BI users looking to master Power BI for all their data visualization and dashboarding needs will find this book useful. An understanding of basic BI concepts is required and some familiarity with Microsoft Power BI will be helpful to make the most out of this book.

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.

Measuring the abundance of individuals and the diversity of species are core components of most ecological research projects and conservation monitoring. This book brings together in one place, for the first time, the methods used to estimate the abundance of individuals in nature. The statistical basis of each method is detailed along with practical considerations for survey design and data collection. Methods are illustrated using data ranging from Alaskan shrubs to Yellowstone grizzly bears, not forgetting Costa Rican ants and Prince Edward Island lobsters. Where necessary, example code for use with the open source software R is supplied. When appropriate, reference is made to other widely used programs. After opening with a brief synopsis of relevant statistical methods, the first section deals with the abundance of stationary items such as trees, shrubs, coral, etc. Following a discussion of the use of quadrats and transects in the contexts of forestry sampling and the assessment of plant cover, there are chapters addressing line-intercept sampling, the use of nearest-neighbour distances, and variable sized plots. The second section deals with individuals that move, such as birds, mammals, reptiles, fish, etc. Approaches discussed include double-observer sampling, removal sampling, capture-recapture methods and distance sampling. The final section deals with the measurement of species richness; species diversity; species-abundance distributions; and other aspects of diversity such as evenness, similarity, turnover and rarity. This is an essential reference for anyone involved in advanced undergraduate or postgraduate ecological research and teaching, or those planning and carrying out data analysis as part of conservation survey and monitoring programmes.

There's growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing data streams at scale has been difficult to do well--until now. This practical book delivers a deep introduction to Apache Flink, a highly innovative open source stream processor with a surprising range of capabilities. Authors Ellen Friedman and Kostas Tzoumas show technical and nontechnical readers alike how Flink is engineered to overcome significant tradeoffs that have limited the effectiveness of other approaches to stream processing. You'll also learn how Flink has the ability to handle both stream and batch data processing with one technology. * Learn the consequences of not doing streaming well--in retail and marketing, IoT, telecom, and banking and finance* Explore how to design data architecture to gain the best advantage from stream processing* Get an overview of Flink's capabilities and features, along with examples of how companies use Flink, including in production* Take a technical dive into Flink, and learn how it handles time and stateful computation* Examine how Flink processes both streaming (unbounded) and batch (bounded) data without sacrificing performance

CCTV for Wildlife Monitoring is a handbook on the use of CCTV in nature watching, conservation and ecological research. CCTV offers a unique ability to monitor wildlife in real time, stream video to the web, capture imagery of fast-moving species or cold animals such as wet otters or fish and maintain monitoring over long periods of time in a diverse array of habitats. Wildlife watchers can take advantage of a huge range of CCTV cameras, recording devices and accessories developed for use in non-wildlife applications. CCTV allows intimate study of animal behaviour not possible with other technologies. With expert experience in engineering, photography and wildlife, Susan Young describes CCTV equipment and techniques, giving readers the confidence to tackle what initially may seem technically challenging. The book enables the reader to navigate the technical aspects of recording: basic analogue, high definition HD-TVI and IP cameras, portable CCTV, digital video recorders (DVR) and video processing by focusing on practical applications. No prior knowledge of CCTV is required - step-by-step information is provided to get anyone started recording wildlife. In-depth methods for recording foxes, badger, deer, otters, small mammals and fish are also included, and the book makes comparisons with trail cameras where appropriate. Examples of recorded footage illustrate the book along with detailed diagrams on camera set-ups and links to accompanying videos on YouTube. Case-studies show real projects, both the equipment used and the results. This book will be of interest to amateur naturalists wishing to have a window into the private world of wildlife, ecological consultants monitoring protected species and research scientists studying animal behaviour.

In dem Standardwerk der Informationsverarbeitung werden nicht nur die elektroakustischen und nachrichtentechnischen Grundlagen dargestellt, auch die Sprache als menschliche Kommunikationsform wird aus linguistischer und physiologischer Perspektive beschrieben. Berucksichtigt wird dabei neben der Theorie stets die Anwendung auf dem neuesten Stand der Technik. Die 2.Auflage bietet neue Abschnitte zu den Grundzugen der Signalanalyse und zu Sprachdialogsystemen. Audiobeispiele und multimediale Vortragselemente zum Download auf extras.springer.com.

Dieses neuartige Lehrbuch gibt den Studierenden der neuen Masterstudiengange in zwolf Lern- und Arbeitsmodulen die Grundlage fur einen sicheren Studien- und Prufungserfolg. Der Studierende wird nicht mit Wissen zugeschuttet, sondern kann sich auf der Grundlage einer sehr guten Ubersicht alle relevanten Themengebiete selbststandig erarbeiten. Die Module sind alle gleichermassen aufgebaut. Ausgehend von bestehendem Grundwissen werden exemplarisch Aufgabenstellungen vorgestellt, die vom Leser sukzessive gelost werden. Tests, Projektarbeiten, Fallstudien und weitere Aufgaben unterstutzen den Selbstlern-Effekt. Das Buch zeichnet sich durch ein hohes Mass an Verstandlichkeit und Praxisnahe aus. Ein Buch, das den Leser an die Hand nimmt, ohne ihn zu bevormunde