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• Preface
• Chapter 1: Introduction 1.1 Preview 1.2 Notational conventions 1.3 Basic characteristics of a measurement method 1.4 Method comparison studies 1.5 Meaning of agreement 1.6 A measurement error model 1.7 Similarity versus agreement 1.8 A toy example 1.9 Controversies and our view 1.10 Concepts related to agreement 1.11 Role of confidence intervals and hypotheses testing 1.12 Common models for paired measurements data 1.13 The Bland Altman plot 1.14 Common regression approaches 1.15 Inappropriate use of common tests in method comparison studies 1.16 Key steps in the analysis of method comparison data 1.17 Chapter summary 1.18 Bibliographic note Exercises
• Chapter 2: Common Approaches for Measuring Agreement 2.1 Preview 2.2 Introduction 2.3 Mean squared deviation 2.4 Concordance correlation coefficient 2.5 A digression: Tolerance and prediction intervals 2.6 Lin s probability criterion and Bland Altman criterion 2.7 Limits of agreement 2.8 Total deviation index and coverage probability 2.9 Inference on agreement measures 2.10 Chapter summary 2.11 Bibliographic note Exercises
• Chapter 3: A General Approach for Modeling and Inference 3.1 Preview 3.2 Mixed effects models 3.3 A large-sample approach for inference 3.4 Modeling and analysis of method comparison data 3.5 Chapter summary 3.6 Bibliographic note Exercises
• Chapter 4: Paired Measurements Data 4.1 Preview 4.2 Modeling of data 4.3 Evaluation of similarity and agreement 4.4 Case studies 4.5 Chapter summary 4.6 Technical details 4.7 Bibliographic note Exercises
• Chapter 5: Repeated Measurements Data 5.1 Preview 5.2 Introduction 5.3 Displaying data 5.4 Modeling of data 5.5 Evaluation of similarity and agreement 5.6 Evaluation of repeatability 5.7 Case studies 5.8 Chapter summary 5.9 Technical details 5.10 Bibliographic note Exercises
• Chapter 6: Heteroscedastic Data 6.1 Preview 6.2 Introduction 6.3 Variance function models 6.4 Repeated measurements data 6.5 Paired measurements data 6.6 Chapter summary 6.7 Technical details 6.8 Bibliographic note Exercises
• Chapter 7: Data from Multiple Methods 7.1 Preview 7.2 Introduction 7.3 Displaying data 7.4 Example datasets 7.5 Modeling unreplicated data 7.6 Modeling repeated measurements data 7.7 Model fitting and evaluation 7.8 Evaluation of similarity and agreement 7.9 Evaluation of repeatability 7.10 Case studies 7.11 Chapter summary 7.12 Technical details 7.13 Bibliographic note Exercises
• Chapter 8: Data with Covariates 8.1 Preview 8.2 Introduction 8.3 Modeling of data 8.4 Evaluation of similarity, agreement and repeatability 8.5 Case study 8.6 Chapter summary 8.7 Technical details 8.8 Bibliographic note Exercises
• Chapter 9: Longitudinal Data 9.1 Preview 9.2 Introduction 9.3 Modeling of data 9.4 Evaluation of similarity and agreement 9.5 Case study 9.6 Chapter summary 9.7 Technical details 9.8 Bibliographic note Exercises
• Chapter 10: A Nonparametric Approach 10.1 Preview 10.2 Introduction 10.3 The statistical functional approach 10.4 Evaluation of similarity and agreement 10.5 Case studies 10.6 Chapter summary 10.7 Technical details 10.8 Bibliographic note Exercises
• Chapter 11: Sample Size Determination 11.1 Preview 11.2 Introduction 11.3 The sample size methodology 11.4 Case study 11.5 Chapter summary 11.6 Bibliographic note Exercises
• Chapter 12: Categorical Data 12.1 Preview 12.2 Introduction 12.3 Experimental setups and examples 12.4 Cohen s kappa coefficient for dichotomous data 12.5 Kappa type measures for more than two categories 12.6 Case studies 12.7 Models for exploring agreement 12.8 Discussion 12.9 Chapter summary 12.10 Bibliographic note Exercises References Dataset List Index.
• (source: Nielsen Book Data)

Measuring Agreement Methodology and Applications successfully blends the currently available statistical methodologies for agreement evaluation in a unified, coherent, and lucid manner. This up-to-date and comprehensive book describes the theoretical underpinnings of the methodologies and presents case studies using several real data sets to illustrate the application of the methodologies. A perfect reference for statisticians, biostatisticians, clinical chemists, and biomedical scientists.
(source: Nielsen Book Data)

• Part A: Experimental Design
• 1. "Most published findings are false!Â
• 2. How to identify a promising research problem?
• 3. Experimental designs: measures, validity, randomization
• 4. Experimental design: Sampling, bias, hypotheses
• 5. Positive and negative controls Part B: Getting a "feel for your data
• 6. Refresher on basic concepts of probability and statistics
• 7. Data cleansing
• 8. Case studies of data cleansing
• 9. Hypothesis testing
• 10. The "new statisticsÂ
• 11. ANOVA.
• 12. Nonparametric tests
• 13. Other statistical concepts you should know Part C: Data Management
• 14. Recording and reporting experiments
• 15. Data sharing and re-use
• 16. Publishing.
• (source: Nielsen Book Data)

Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented. This book is a valuable source for biomedical and health sciences graduate students and researchers, in general, who are interested in handling data to make their research reproducible and more efficient.
(source: Nielsen Book Data)

• Part I - Technological Foundations and Human Aspect of Knowledge
• 1. Computational Framework 2. Human and Social Aspects 3. Processing, Switching and Communication of Knowledge Part II - Quantitative Aspects of Knowledge 4. Quantization of Knowledge 5. Needs, Resources, Deployment and Gratification
• 6. Nature, Abundance and Needs
• Part III - Knowledge Dynamics 7. Foundations of Knowledge 8. Flow of Knowledge
• 9. Design Constructs of a Knowledge Machine
• 10. Knowledge Science 11. Knowledge Flow in Universities.
• (source: Nielsen Book Data)

This book delivers the scientific and mathematical basis to treat and process knowledge as a quantifiable and dimensioned entity. It provides the units and measures for the value of information contained in a "body of knowledge" that can be measured, processed, enhanced, communicated and preserved. It provides a basis to evaluate the quantity of knowledge acquired by students at various levels and in different universities. The effect of time on the dynamics and flow of knowledge is tied to Internet knowledge banks and provides the basis for designing and building the next generation of novel machine to appear in society. This book ties the basic needs of all human beings to the modern machines that resolve such need based on Internet knowledge banks (KBs) distributed throughout nations and societies. The features of the Intelligent Internet are fully exploited to make a new generation of students and knowledge workers use the knowledge resources elegantly and optimally. It deals with topics and insight into the design and architecture of next-generation computing systems that deal with human and social problems. Processor and Internet technologies that have already revolutionized human lives form the subject matter and the focal point of this book. Information and knowledge on the Internet delivered by next-generation mobile networks form the technical core presented. Human thought processes and adjustments follow the solutions offered by machines.
(source: Nielsen Book Data)

Breathe life into your data by learning how to use D3.js V4 to visualize information About This Book * Create complex visualizations powered by D3.js and open data. * Provides an extensive set of visualizations that explore all the functionality provided by D3.js V4. * Shows how to set up an easy-to-use environment to create stunning visualizations. Who This Book Is For The typical target audience of this book is JavaScript developers, designers, and visual artists who have some basic JavaScript programming knowledge and who now want to master pro-level techniques to create interactive data visualizations using web standards which work on desktop as well as mobile devices. What You Will Learn * Learn how D3.js works to declaratively define visualizations. * Create charts from scratch by using SVG and the D3.js APIs * See how to prepare data for easy visualization using D3.js. * Visualize hierarchical data using chart types provided by D3.js * Explore the different options provided by D3.js to visualize linked data such as graphs. * Spice up your visualizations by adding interactivity and animations. * Learn how to use D3.js to visualize and interact with Geo- and Gis-related information sources. * Create visualization by streaming data over WebSockets In Detail Do you want to make sense of your data? Do you want to create interactive charts, data trees, info-graphics, geospatial charts, and maps efficiently? This book is your ideal choice to master interactive data visualization with D3.js V4. The book includes a number of extensive examples that to help you hone your skills with data visualization. Throughout nine chapters these examples will help you acquire a clear practical understanding of the various techniques, tools and functionality provided by D3.js. You will first setup your D3.JS development environment and learn the basic patterns needed to visualize your data. After that you will learn techniques to optimize different processes such as working with selections; animating data transitions; creating graps and charts, integrating external resources (static as well as streaming); visualizing information on maps; working with colors and scales; utilizing the different D3.js APIs; and much more. The book will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. The extensive examples will include working with complex and realtime data streams, such as seismic data, geospatial data, scientific data, and more. Towards the end of the book, you will learn to add more functionality on top of D3.js by using it with other external libraries and integrating it with Ecmascript 6 and Typescript Style and approach This book will have a real-world, case-study approach, where you will be given data sets from different domains. These data sets will have different visualization goals; some might need 2D or 3D charts, some might need automated workflows, others might require interactive maps. While you fulfill these goals, you will learn different techniques and best practices, which will enable you to perform data visualization tasks on your own.
(source: Nielsen Book Data)

Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets This book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error. This book: Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods. Paul P. Biemer, PhD, is distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA. Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands. Stephanie Eckman, PhD, is fellow at RTI International, USA. Brad Edwards is vice president, director of Field Services, and deputy area director at Westat, USA. Frauke Kreuter, PhD, is professor and director of the Joint Program in Survey Methodology, University of Maryland, USA; professor of statistics and methodology at the University of Mannheim, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research, Germany. Lars E. Lyberg, PhD, is senior advisor at Inizio, Sweden. N. Clyde Tucker, PhD, is principal survey methodologist at the American Institutes for Research, USA. Brady T. West, PhD, is research associate professor in the Survey Research Center, located within the Institute for Social Research at the University of Michigan (U-M), and also serves as statistical consultant on the Consulting for Statistics, Computing and Analytics Research (CSCAR) team at U-M, USA.
(source: Nielsen Book Data)

• Contributors ix Introduction 1 Harald Atmanspacher and Sabine Maasen PART I: CONTEXTUAL BACKGROUNDS Introductory Remarks 9 Harald Atmanspacher Reproducibility, Objectivity, Invariance 13 Holm Tetens Reproducibility between Production and Prognosis 21 Walther ChZimmerli Stability and Replication of Experimental Results: A Historical Perspective 39 Friedrich Steinle Reproducibility of Experiments: Experimenters Regress, Statistical Uncertainty Principle, and the Replication Imperative 65 Harry Collins PART II: STATISTICAL ISSUES Introductory Remarks 83 Harald Atmanspacher Statistical Issues in Reproducibility 87 Werner AStahel Model Selection, Data Distributions and Reproducibility 115 Richard Shiffrin and Suyog Chandramouli Reproducibility from the Perspective of Meta-Analysis 141 Werner Ehm Why Are There so Many Clustering Algorithms, and How Valid Are Their Results? 169 Vladimir Estivill-Castro PART III: PHYSICAL SCIENCES Introductory Remarks 201 Harald Atmanspacher Facilitating Reproducibility in Scientific Computing: Principles and Practice 205 David H Bailey, Jonathan M Borwein, and Victoria Stodden Methodological Issues in the Study of Complex Systems 233 Harald Atmanspacher and Gerda Demmel Rare and Extreme Events 251 Holger Kantz Science under Societal Scrutiny: Reproducibility in Climate Science 269 Georg Feulner PART IV: LIFE SCIENCES Introductory Remarks 287 Harald Atmanspacher From Mice to Men: Translation from Bench to Bedside 291 Marianne Martic-Kehl and P August Schubiger A Continuum of Reproducible Research in Drug Development 315 Gerd Folkers and Sabine Baier Randomness as a Building Block for Reproducibility in Local Cortical Networks 325 Johannes Lengler and Angelika Steger Neural Reuse and in-Principle Limitations on Reproducibility in Cognitive Neuroscience 341 Michael L Anderson On the Difference between Persons and Things Reproducibility in Social Contexts 363 Kai Vogeley PART V: SOCIAL SCIENCES Introductory Remarks 385 Sabine Maasen and Harald Atmanspacher Order Effects in Sequential Judgments and Decisions 391 Zheng Wang and Jerome Busemeyer Reproducibility in the Social Sciences 407 Martin Reinhart Accurate But Not Reproducible? The Possible Worlds of Public Opinion Research 425 Felix Keller Depending on Numbers 447 Theodore M Porter Science between Trust and Control: Non-Reproducibility in Scholarly Publishing 467 Martina Franzen PART VI: WIDER PERSPECTIVES Introductory Remarks 487 Sabine Maasen and Harald Atmanspacher Repetition with a Difference: Reproducibility in Literature Studies 491 Ladina Bezzola Lambert Repetition Impossible: Co-Affection by Mimesis and Self-Mimesis 511 Hinderk Emrich Relevance Criteria for Reproducibility: The Contextual Emergence of Granularity 527 Harald Atmanspacher The Quest for Reproducibility Viewed in the Context of Innovation Societies 541 Sabine Maasen Index 563.
• (source: Nielsen Book Data)

Received an honorable mention by the Prose Award Committee in the Best Textbook in Physical Sciences/Mathematics 2017 category The PROSE Awards draw attention to pioneering works of research and for contributions to the conception, production, and design of landmark works in their fields. Featuring peer-reviewed contributions from noted experts in their fields of research, Reproducibility: Principles, Problems, Practices, and Prospects presents state-of-the-art approaches to reproducibility, the gold standard of sound science, from multi- and interdisciplinary perspectives. Including comprehensive coverage for implementing and reflecting the norm of reproducibility in various pertinent fields of research, the book focuses on how the reproducibility of results is applied, how it may be limited, and how such limitations can be understood or even controlled in the natural sciences, computational sciences, life sciences, social sciences, and studies of science and technology. The book presents many chapters devoted to a variety of methods and techniques, as well as their epistemic and ontological underpinnings, which have been developed to safeguard reproducible research and curtail deficits and failures. The book also investigates the political, historical, and social practices that underlie reproducible research in contemporary science studies, including the difficulties of good scientific practice and the ethos of reproducibility in modern innovation societies. Reproducibility: Principles, Problems, Practices, and Prospects is a guide for researchers who are interested in the general and overarching questions behind the concept of reproducibility; for active scientists who are confronted with practical reproducibility problems in their everyday work; and for economic stakeholders and political decision makers who need to better understand the challenges of reproducibility. In addition, the book is a useful in-depth primer for undergraduate and graduate-level courses in scientific methodology and basic issues in the philosophy and sociology of science from a modern perspective. A comprehensive, insightful treatment of the reproducibility challenges facing science today and of ways in which the scientific community can address them. Kathleen Hall Jamieson, Elizabeth Ware Packard Professor of Communication, University of Pennsylvania How can we make sure that reproducible research remains a key imperative of scientific communication under increasing commercialization, media attention, and publication pressure? This handbook offers the first interdisciplinary and fundamental treatment of this important question. Torsten Hothorn, Professor of Biostatistics, University of Zurich Harald Atmanspacher, PhD, is Associate Fellow and staff member at Collegium Helveticum, ETH and University Zurich and is also President of the Society for Mind-Matter Research. He has pioneered advances in complex dynamical systems research and in a number of topics concerned with the relation between the mental and physical. Sabine Maasen, PhD, is Professor for Sociology of Science and Director of the Munich Center for Technology in Society (TU Munich) and Associate Fellow at Collegium Helveticum (ETH and University Zurich). Her research focuses on the interface of science, technology, and society, notably with respect to neuroscience and its applications.
(source: Nielsen Book Data)

• Preface
• 1. Building Theory from Case Study Research
• 2. Bridging the Chasm between Survey and Case Study Research
• 3. Storytelling Theory and Research
• 4. Creating Visual Narrative Art for Decoding Stories
• 5. Subjective and Confirmatory Personal Introspection
• 6. Overcoming the Illusion of Conscious Will and Self-Fabrication
• 7. Using the Forced Metaphor-Elicitation Technique (FMET) in Subjective Personal Introspections about Self
• 8. Surfacing Executives Interpretations of Self and the Roles of Co-Workers in Enacting Front and Back Stage Strategies
• 9. Personal Exchanges, Social Behavior, Conversation Analysis, and Face-To-Face Talk
• 10. Exchange (Talk) Behavior in Natural Settings: An Exposition of Variable Based Analysis of Case Study - Arch G. Woodside and James L. Taylor 207
• 11. Constructing Thick Descriptions of Marketers' and Buyers' Decision Processes in Business-to-Business Exchange Relationships
• 12. Case Study Research on Means-End Laddering Chains
• 13. Building in Degrees of Freedom Analysis in Case Study Research: Empirical Positivistic Testing of Data to Alternative Theories
• 14. Applying the Long Interview in Case Study Research
• 15. Tipping-Point Modeling in Case Study Research
• 16. Participant Observation Research in Organizational Behavior
• 17. Systems Thinking and System Dynamics Modeling
• 18. Visualizing Matching Generalizing: Case Identification Hypotheses and Case-Level Data Analysis
• 19. Fuzzy Set Social Science and Qualitative Comparative Analysis
• 20. Constructing Cased-Based Macro Models: Cultures' Consequences on Entrepreneurship, Innovation, and Quality-of-Life - Arch G. Woodside, Pedro Mir Bernal and Alicia Coduras 433
• 21. Conclusions: Principles for Doing Case Study Research References Subject Index.
• (source: Nielsen Book Data)

Case Study Research: Core Skills in Using 15 Genres examines the processes involved in conducting case research in a number of genres including participant observation, fuzzy set social science, system dynamics, decision systems analysis, forced metaphor elicitation technique, ethnographic decision tree modeling, mapping strategic thinking, the historical method, storytelling research and conversational analysis. The book reviews and applies the best literature on case study methods from a number of disciplines providing a strong rationale for adopting case study research methods alone or in mixed-methods. This fully revised and updated second edition employs a broad and deep coverage of multiple case study research genres to comprehensively explore the topic.
(source: Nielsen Book Data)

• General science, mathematics, and technology. Introduction ; Societies, publications, and awards ; Numbers ; Mathematics ; Computers
• Physics and chemistry. Energy, motion, and force ; Light, sound, and other waves ; Electricity and magnetism ; Matter ; Chemical elements ; Temperature, measurement, and methodology
• Astronomy and space. Universe ; Stars ; Planets and moons ; Comets and meteorites ; Observation and measurement ; Exploration
• Earth. Air ; Physical characteristics ; Water ; Land ; Volcanoes and earthquakes ; Observation and measurement
• Climate and weather. Temperature ; Air phenomena ; Wind ; Precipitation ; Weather prediction
• Minerals, metals, and other materials. Rocks and minerals ; Metals ; Natural substances ; Man-made products
• Energy. Fossil fuels ; Renewable and alternative energy ; Nuclear energy ; Measures and measurement ; Consumption and conservation
• Environment. Biomes, ecological cycles, and environmental milestones ; Pollution and wastes ; Recycling and conservation ; Extinct and endangered plants and animals
• Biology. Cells ; Viruses, bacteria, protists, and fungi ; DNA, RNA, and chromosomes ; Genetics and evolution ; Classification, laboratory tools, and techniques
• Plant world. Introduction and historical background ; Plant diversity ; Plant structure and function ; Flowers and unusual plants ; Trees and shrubs ; Soil, gardening, and farming
• Animal world. Introduction and historical background ; Animal characteristics and activities ; Sponges, coelenterates, and worms ; Mollusks and echinoderms ; Arthropods : crustaceans, insects and spiders ; Fish, amphibians, and reptiles ; Birds ; Mammals ; Pets
• Human body. Introduction and historical background ; Tissues, organs, and glands ; Bones and muscles ; Skin, hair, and nails ; Blood and circulation ; Nerves and senses ; Digestion ; Reproduction and human development
• Health and medicine. Health hazards and risks ; First aid and poisons ; Diseases, disorders, and other health problems ; Health care ; Diagnostic equipments, tests and techniques ; Drugs and medicines ; Surgery and other treatments.

"Presenting a fun and educational way to explore the wonders of the world of science, this newly updated edition poses and answers 2,200 questions, providing an abundance of original and interesting science facts. Children and adults will uncover some of the most interesting, unusual, and quirky science curiosities such as: Are cell phones dangerous to your health? Is the same strain of yeast used to make different types of beer? What is the cleanest fossil fuel? What is the largest invertebrate? Readers will find this informative and enjoyable resource is chock full of hundreds of intriguing science and technology topics, from the inner workings of the human body and outer space to math, computers, planes, trains, and automobiles"-- Provided by publisher.

• Preface
• Acknowledgements
• Foreword
• About the editors
• Having what it takes / Eldeen Pozniak
• Creating a business plan / Sam Gualardo
• What about the money (financial) / Deborah Roy
• Insurance needs / Diana Stegall
• Legal and regulatory issues / Adele L. Abrams
• Marketing and sales / Linda Tapp
• Networking / Katharine Hart
• Client retention : how to develop successful client-consultant relationships / Katharine Hart
• Ethics and other professional issues / Daniel J. Snyder
• After the first year / Donna S. Pearson
• Making it work on your own / Robert N. Andres, Jeffery Camplin, Paul Gantt, J. Terrence Grism, Stephen High, John P. Lesegranich, Chris Ross, Debby Shewitz, and Dave K. Smith
• Suggested readings
• Index.

• Intro0duction xxv Part ONE: Creating the Responsive Web
• 1 The Mobile Web 3 How We Got Here 4 The Web's History 5 The Rise of Mobile Devices 8 The Mobile Tipping Point 10 The Mobile Web Today 13 Mobile Web Considerations 13 Benefits of the Mobile Web 15 Building for the Most Users 17 Mobile Web Design 17 The Way To Build Today 18 A Taste of Progressive Enhancement 20 Summary 23
• 2 Responsive Web Design Tenets 25 The Gist 26 Building for the Limited User 27 Building for Every User 28 Adaptive vs. Responsive 29 The Broad Adaptive 29 Limiting Your Adaptive Design Definition 33 Desktop-First vs. Mobile-First 36 Four Principles 38 Be Universal 39 Be Flexible 43 Be Economical 46 Be Specific 47 Seven Points of Focus 49 Measure 50 Flow 51 Nesting 54 Endpoints 57 Breakpoints 59 Typography 63 Images 66 Summary 69
• 3 Working with a Flexible Grid 71 The Gist 72 What Is a Grid System? 72 Flexible Units 75 The Meticulous Client 75 Moving to Relative Units 79 Using a Grid System 81 Another Project 82 Branching Out 93 Creating a Reusable, Flexible Grid (in Five Easy Steps) 93 Unit 95 Gutter 95 X-Count 95 Columns 96 Rows 96 Putting It All Together 97 Summary 98 Try It 98
• 4 Designing Responsive Layouts with CSS 101 The Gist 102 A Brief History of CSS 102 You've Come a Long Way, Baby 103 Responsive Layout Design 118 The Media Query 118 Starting from the Bottom 127 The Reusable Responsive Grid 144 Building a Reusable Grid 145 Building a Responsive Grid 147 Summary 148
• 5 Enhancing with JavaScript 151 Using JavaScript 152 JavaScript Enables Rich Interactivity 153 Remember Progressive Enhancement 155 Sensible Defaults 157 Responsive JavaScript 163 Server-Side Feature Detection 164 Building from CSS 164 JavaScript's matchMedia Object 166 Limber Up 168 AJAX 168 Perceived Performance 172 Summary 182 Try It 182 Part Two: Creating Responsive Data Visualization
• 6 Data Design: An Abridged History 187 Learning From Data 188 Data, Information, and Knowledge 189 Size Matters 191 Data's Data 192 The Big Pile 193 Extracting Meaning 193 Communicating Meaning 195 What You Get from the Web 210 Interactivity 211 A Taste of Responsive 212 Summary 214
• 7 Responsive Data Visualization Tenets 217 Designing Content-First 218 Still Just the Web 219 Distilling Data 220 Revisiting Responsive Design Principles 229 Be Universal 230 Be Flexible 232 Be Economical 235 Be Specific 240 Seven Points of Focus 241 Measure 242 Flow 245 End points 247 Breakpoints 249 Grouping Points 252 Images 253 Layering 256 Responding to Data 258 Know Your Audience 259 Know Your Data 260 Summary 261
• 8 Thinking Small 263 Designing for the Smallest Canvas: No Canvas 264 Building for a Blind User 266 The Good API 269 The API-First Team 278 The Tiny Canvas 279 The Fitness App 279 Layering 283 Enhancing Efficiently 285 Growing Up 286 First, Change Everything 287 They're Just Numbers 290 Summary 291
• 9 Asset Dependence 293 Dynamic Data 294 Dipping Your Toe In 294 Data versus Screen 298 Reacting to Data Changes 301 Tying Visualization to Screens 308 Grouping Again 309 Summary 312 Try It 313
• 10 Code-Driven Visualization 315 Unknown Inputs and Outputs 316 To the User 317 To the Data 317 Putting It Together 320 Using D3.js 320 Getting Started with D3.js 321 Data and the DOM 348 Building Responsive Data Visualization for the Web 354 Client Responsiveness 355 Data Responsiveness 361 The Bigger Picture 363 Rational Defaults 363 Driving Design with Data 371 Clear, Not Clever 372 Summary 372 Try It 373
• 11 Bui1ding the Future-Friendly Web 375 The Future of Data Design 376 Growing Stockpiles of Data 377 Setting a Standard for Data Markup 378 Embracing Uncertainty 379 Where Complexity Lies Today 380 The Future-Friendly Manifesto 381 Responsible Web Design 385 Building API-first 386 Respecting Progressive Enhancement 386 Innovating 388 Standardizing 388 Summary 389 Part Three: Additional Resources A Resources 393 Responsive Data Visualization 394 Grids 394 Infographic Design 394 Responsive Design 395 D3.js 395 More Resources Online 396 Index 397.
• (source: Nielsen Book Data)

Unchain your data from the desktop with responsive visualizations Building Responsive Data Visualization for the Web is a handbook for any front-end development team needing a framework for integrating responsive web design into the current workflow. Written by a leading industry expert and design lead at Starbase Go, this book provides a wealth of information and practical guidance from the perspective of a real-world designer. You'll walk through the process of building data visualizations responsively as you learn best practices that build upon responsive web design principles, and get the hands-on practice you need with exercises, examples, and source code provided in every chapter. These strategies are designed to be implemented by teams large and small, with varying skill sets, so you can apply these concepts and skills to your project right away. Responsive web design is the practice of building a website to suit base browser capability, then adding features that enhance the experience based on the user's device's capabilities. Applying these ideas to data produces visualizations that always look as if they were designed specifically for the device through which they are viewed. This book shows you how to incorporate these principles into your current practices, with highly practical hands-on training. Examine the hard data surrounding responsive design Master best practices with hands-on exercises Learn data-based document manipulation using D3.js Adapt your current strategies to responsive workflows Data is growing exponentially, and the need to visualize it in any context has become crucial. Traditional visualizations allow important data to become lost when viewed on a small screen, and the web traffic speaks for itself - viewers repeatedly demonstrate their preference for responsive design. If you're ready to create more accessible, take-anywhere visualizations, Building Responsive Data Visualization for the Web is your tailor-made solution.
(source: Nielsen Book Data)

• CONTRIBUTORS xv FOREWORD xvii PREFACE xix THE EDITORS xxix PART I STRATEGIES FOR SUCCESS IN THE DIGITAL-DATA REVOLUTION 1
• 1. The Digital-Data Challenge 5 Malcolm Atkinson and Mark Parsons 1.1 The Digital Revolution / 5 1.2 Changing How We Think and Behave / 6 1.3 Moving Adroitly in this Fast-Changing Field / 8 1.4 Digital-Data Challenges Exist Everywhere / 8 1.5 Changing How We Work / 9 1.6 Divide and Conquer Offers the Solution / 10 1.7 Engineering Data-to-Knowledge Highways / 12 References / 13
• 2. The Digital-Data Revolution 15 Malcolm Atkinson 2.1 Data, Information, and Knowledge / 16 2.2 Increasing Volumes and Diversity of Data / 18 2.3 Changing the Ways We Work with Data / 28 References / 33
• 3. The Data-Intensive Survival Guide 37 Malcolm Atkinson 3.1 Introduction: Challenges and Strategy / 38 3.2 Three Categories of Expert / 39 3.3 The Data-Intensive Architecture / 41 3.4 An Operational Data-Intensive System / 42 3.5 Introducing DISPEL / 44 3.6 A Simple DISPEL Example / 45 3.7 Supporting Data-Intensive Experts / 47 3.8 DISPEL in the Context of Contemporary Systems / 48 3.9 Datascopes / 51 3.10 Ramps for Incremental Engagement / 54 3.11 Readers' Guide to the Rest of This Book / 56 References / 58
• 4. Data-Intensive Thinking with DISPEL 61 Malcolm Atkinson 4.1 Processing Elements / 62 4.2 Connections / 64 4.3 Data Streams and Structure / 65 4.4 Functions / 66 4.5 The Three-Level Type System / 72 4.6 Registry, Libraries, and Descriptions / 81 4.7 Achieving Data-Intensive Performance / 86 4.8 Reliability and Control / 108 4.9 The Data-to-Knowledge Highway / 116 References / 121 PART II DATA-INTENSIVE KNOWLEDGE DISCOVERY 123
• 5. Data-Intensive Analysis 127 Oscar Corcho and Jano van Hemert 5.1 Knowledge Discovery in Telco Inc. / 128 5.2 Understanding Customers to Prevent Churn / 130 5.3 Preventing Churn Across Multiple Companies / 134 5.4 Understanding Customers by Combining Heterogeneous Public and Private Data / 137 5.5 Conclusions / 144 References / 145
• 6. Problem Solving in Data-Intensive Knowledge Discovery 147 Oscar Corcho and Jano van Hemert 6.1 The Conventional Life Cycle of Knowledge Discovery / 148 6.2 Knowledge Discovery Over Heterogeneous Data Sources / 155 6.3 Knowledge Discovery from Private and Public, Structured and Nonstructured Data / 158 6.4 Conclusions / 162 References / 162
• 7. Data-Intensive Components and Usage Patterns 165 Oscar Corcho 7.1 Data Source Access and Transformation Components / 166 7.2 Data Integration Components / 172 7.3 Data Preparation and Processing Components / 173 7.4 Data-Mining Components / 174 7.5 Visualization and Knowledge Delivery Components / 176 References / 178
• 8. Sharing and Reuse in Knowledge Discovery 181 Oscar Corcho 8.1 Strategies for Sharing and Reuse / 182 8.2 Data Analysis Ontologies for Data Analysis Experts / 185 8.3 Generic Ontologies for Metadata Generation / 188 8.4 Domain Ontologies for Domain Experts / 189 8.5 Conclusions / 190 References / 191 PART III DATA-INTENSIVE ENGINEERING 193
• 9. Platforms for Data-Intensive Analysis 197 David Snelling 9.1 The Hourglass Reprise / 198 9.2 The Motivation for a Platform / 200 9.3 Realization / 201 References / 201
• 10. Definition of the DISPEL Language 203 Paul Martin and Gagarine Yaikhom 10.1 A Simple Example / 204 10.2 Processing Elements / 205 10.3 Data Streams / 213 10.4 Type System / 217 10.5 Registration / 222 10.6 Packaging / 224 10.7 Workflow Submission / 225 10.8 Examples of DISPEL / 227 10.9 Summary / 235 References / 236
• 11. DISPEL Development 237 Adrian Mouat and David Snelling 11.1 The Development Landscape / 237 11.2 Data-Intensive Workbenches / 239 11.3 Data-Intensive Component Libraries / 247 11.4 Summary / 248 References / 248
• 12. DISPEL Enactment 251 Chee Sun Liew, Amrey Krause, and David Snelling 12.1 Overview of DISPEL Enactment / 251 12.2 DISPEL Language Processing / 253 12.3 DISPEL Optimization / 255 12.4 DISPEL Deployment / 266 12.5 DISPEL Execution and Control / 268 References / 273 PART IV DATA-INTENSIVE APPLICATION EXPERIENCE 275
• 13. The Application Foundations of DISPEL 277 Rob Baxter 13.1 Characteristics of Data-Intensive Applications / 277 13.2 Evaluating Application Performance / 280 13.3 Reviewing the Data-Intensive Strategy / 283
• 14. Analytical Platform for Customer Relationship Management 287 Maciej Jarka and Mark Parsons 14.1 Data Analysis in the Telecoms Business / 288 14.2 Analytical Customer Relationship Management / 289 14.3 Scenario
• 1: Churn Prediction / 291 14.4 Scenario
• 2: Cross Selling / 293 14.5 Exploiting the Models and Rules / 296 14.6 Summary: Lessons Learned / 299 References / 299
• 15. Environmental Risk Management 301 Ladislav Hluch'y, Ondrej Habala, Viet Tran, and Branislav ! Simo 15.1 Environmental Modeling / 302 15.2 Cascading Simulation Models / 303 15.3 Environmental Data Sources and Their Management / 305 15.4 Scenario
• 1: ORAVA / 309 15.5 Scenario
• 2: RADAR / 313 15.6 Scenario
• 3: SVP / 318 15.7 New Technologies for Environmental Data Mining / 321 15.8 Summary: Lessons Learned / 323 References / 325
• 16. Analyzing Gene Expression Imaging Data in Developmental Biology 327 Liangxiu Han, Jano van Hemert, Ian Overton, Paolo Besana, and Richard Baldock 16.1 Understanding Biological Function / 328 16.2 Gene Image Annotation / 330 16.3 Automated Annotation of Gene Expression Images / 331 16.4 Exploitation and Future Work / 341 16.5 Summary / 345 References / 346
• 17. Data-Intensive Seismology: Research Horizons 353 Michelle Galea, Andreas Rietbrock, Alessandro Spinuso, and Luca Trani 17.1 Introduction / 354 17.2 Seismic Ambient Noise Processing / 356 17.3 Solution Implementation / 358 17.4 Evaluation / 369 17.5 Further Work / 372 17.6 Conclusions / 373 References / 375 PART V DATA-INTENSIVE BEACONS OF SUCCESS 377
• 18. Data-Intensive Methods in Astronomy 381 Thomas D. Kitching, Robert G. Mann, Laura E. Valkonen, Mark S. Holliman, Alastair Hume, and Keith T. Noddle 18.1 Introduction / 381 18.2 The Virtual Observatory / 382 18.3 Data-Intensive Photometric Classification of Quasars / 383 18.4 Probing the Dark Universe with Weak Gravitational Lensing / 387 18.5 Future Research Issues / 392 18.6 Conclusions / 392 References / 393
• 19. The World at One's Fingertips: Interactive Interpretation of Environmental Data 395 Jon Blower, Keith Haines, and Alastair Gemmell 19.1 Introduction / 395 19.2 The Current State of the Art / 397 19.3 The Technical Landscape / 401 19.4 Interactive Visualization / 403 19.5 From Visualization to Intercomparison / 406 19.6 Future Development: The Environmental Cloud / 409 19.7 Conclusions / 411 References / 412
• 20. Data-Driven Research in the Humanities--the DARIAH Research Infrastructure 417 Andreas Aschenbrenner, Tobias Blanke, Christiane Fritze, andWolfgang Pempe 20.1 Introduction / 417 20.2 The Tradition of Digital Humanities / 420 20.3 Humanities Research Data / 422 20.4 Use Case / 426 20.5 Conclusion and Future Development / 429 References / 430
• 21. Analysis of Large and Complex Engineering and Transport Data 431 Jim Austin 21.1 Introduction / 431 21.2 Applications and Challenges / 432 21.3 The Methods Used / 434 21.4 Future Developments / 438 21.5 Conclusions / 439 References / 440
• 22. Estimating Species Distributions--Across Space, Through Time, and with Features of the Environment 441 Steve Kelling, Daniel Fink, Wesley Hochachka, Ken Rosenberg, Robert Cook, Theodoros Damoulas, Claudio Silva, and William Michener 22.1 Introduction / 442 22.2 Data Discovery, Access, and Synthesis / 443 22.3 Model Development / 448 22.4 Managing Computational Requirements / 449 22.5 Exploring and Visualizing Model Results / 450 22.6 Analysis Results / 452 22.7 Conclusion / 454 References / 456 PART VI THE DATA-INTENSIVE FUTURE 459
• 23. Data-Intensive Trends 461 Malcolm Atkinson and Paolo Besana 23.1 Reprise / 461 23.2 Data-Intensive Applications / 469 References / 476
• 24. Data-Rich Futures 477 Malcolm Atkinson 24.1 Future Data Infrastructure / 478 24.2 Future Data Economy / 485 24.3 Future Data Society and Professionalism / 489 References / 494 Appendix A: Glossary 499 Michelle Galea and Malcolm Atkinson Appendix B: DISPEL Reference Manual 507 Paul Martin Appendix C: Component Definitions 531 Malcolm Atkinson and Chee Sun Liew INDEX 537.
• (source: Nielsen Book Data)

• CONTRIBUTORS xv FOREWORD xvii PREFACE xix THE EDITORS xxix PART I STRATEGIES FOR SUCCESS IN THE DIGITAL-DATA REVOLUTION 1
• 1. The Digital-Data Challenge 5 Malcolm Atkinson and Mark Parsons 1.1 The Digital Revolution 5 1.2 Changing How We Think and Behave 6 1.3 Moving Adroitly in this Fast-Changing Field 8 1.4 Digital-Data Challenges Exist Everywhere 8 1.5 Changing How We Work 9 1.6 Divide and Conquer Offers the Solution 10 1.7 Engineering Data-to-Knowledge Highways 12
• 2. The Digital-Data Revolution 15 Malcolm Atkinson 2.1 Data, Information, and Knowledge 16 2.2 Increasing Volumes and Diversity of Data 18 2.3 Changing the Ways We Work with Data 28
• 3. The Data-Intensive Survival Guide 37 Malcolm Atkinson 3.1 Introduction: Challenges and Strategy 38 3.2 Three Categories of Expert 39 3.3 The Data-Intensive Architecture 41 3.4 An Operational Data-Intensive System 42 3.5 Introducing DISPEL 44 3.6 A Simple DISPEL Example 45 3.7 Supporting Data-Intensive Experts 47 3.8 DISPEL in the Context of Contemporary Systems 48 3.9 Datascopes 51 3.10 Ramps for Incremental Engagement 54 3.11 Readers' Guide to the Rest of This Book 56
• 4. Data-Intensive Thinking with DISPEL 61 Malcolm Atkinson 4.1 Processing Elements 62 4.2 Connections 64 4.3 Data Streams and Structure 65 4.4 Functions 66 4.5 The Three-Level Type System 72 4.6 Registry, Libraries, and Descriptions 81 4.7 Achieving Data-Intensive Performance 86 4.8 Reliability and Control 108 4.9 The Data-to-Knowledge Highway 116 PART II DATA-INTENSIVE KNOWLEDGE DISCOVERY 123
• 5. Data-Intensive Analysis 127 Oscar Corcho and Jano van Hemert 5.1 Knowledge Discovery in Telco Inc. 128 5.2 Understanding Customers to Prevent Churn 130 5.3 Preventing Churn Across Multiple Companies 134 5.4 Understanding Customers by Combining Heterogeneous Public and Private Data 137 5.5 Conclusions 144
• 6. Problem Solving in Data-Intensive Knowledge Discovery 147 Oscar Corcho and Jano van Hemert 6.1 The Conventional Life Cycle of Knowledge Discovery 148 6.2 Knowledge Discovery Over Heterogeneous Data Sources 155 6.3 Knowledge Discovery from Private and Public, Structured and Nonstructured Data 158 6.4 Conclusions 162
• 7. Data-Intensive Components and Usage Patterns 165 Oscar Corcho 7.1 Data Source Access and Transformation Components 166 7.2 Data Integration Components 172 7.3 Data Preparation and Processing Components 173 7.4 Data-Mining Components 174 7.5 Visualization and Knowledge Delivery Components 176
• 8. Sharing and Reuse in Knowledge Discovery 181 Oscar Corcho 8.1 Strategies for Sharing and Reuse 182 8.2 Data Analysis Ontologies for Data Analysis Experts 185 8.3 Generic Ontologies for Metadata Generation 188 8.4 Domain Ontologies for Domain Experts 189 8.5 Conclusions 190 PART III DATA-INTENSIVE ENGINEERING 193
• 9. Platforms for Data-Intensive Analysis 197 David Snelling 9.1 The Hourglass Reprise 198 9.2 The Motivation for a Platform 200 9.3 Realization 201
• 10. Definition of the DISPEL Language 203 Paul Martin and Gagarine Yaikhom 10.1 A Simple Example 204 10.2 Processing Elements 205 10.3 Data Streams 213 10.4 Type System 217 10.5 Registration 222 10.6 Packaging 224 10.7 Workflow Submission 225 10.8 Examples of DISPEL 227 10.9 Summary 235
• 11. DISPEL Development 237 Adrian Mouat and David Snelling 11.1 The Development Landscape 237 11.2 Data-Intensive Workbenches 239 11.3 Data-Intensive Component Libraries 247 11.4 Summary 248
• 12. DISPEL Enactment 251 Chee Sun Liew, Amrey Krause, and David Snelling 12.1 Overview of DISPEL Enactment 251 12.2 DISPEL Language Processing 253 12.3 DISPEL Optimization 255 12.4 DISPEL Deployment 266 12.5 DISPEL Execution and Control 268 PART IV DATA-INTENSIVE APPLICATION EXPERIENCE 275
• 13. The Application Foundations of DISPEL 277 Rob Baxter 13.1 Characteristics of Data-Intensive Applications 277 13.2 Evaluating Application Performance 280 13.3 Reviewing the Data-Intensive Strategy 283
• 14. Analytical Platform for Customer Relationship Management 287 Maciej Jarka and Mark Parsons 14.1 Data Analysis in the Telecoms Business 288 14.2 Analytical Customer Relationship Management 289 14.3 Scenario
• 1: Churn Prediction 291 14.4 Scenario
• 2: Cross Selling 293 14.5 Exploiting the Models and Rules 296 14.6 Summary: Lessons Learned 299
• 15. Environmental Risk Management 301 Ladislav Hluchy, Ondrej Habala, Viet Tran, and Branislav Simo 15.1 Environmental Modeling 302 15.2 Cascading Simulation Models 303 15.3 Environmental Data Sources and Their Management 305 15.4 Scenario
• 1: ORAVA 309 15.5 Scenario
• 2: RADAR 313 15.6 Scenario
• 3: SVP 318 15.7 New Technologies for Environmental Data Mining 321 15.8 Summary: Lessons Learned 323
• 16. Analyzing Gene Expression Imaging Data in Developmental Biology 327 Liangxiu Han, Jano van Hemert, Ian Overton, Paolo Besana, and Richard Baldock 16.1 Understanding Biological Function 328 16.2 Gene Image Annotation 330 16.3 Automated Annotation of Gene Expression Images 331 16.4 Exploitation and Future Work 341 16.5 Summary 345
• 17. Data-Intensive Seismology: Research Horizons 353 Michelle Galea, Andreas Rietbrock, Alessandro Spinuso, and Luca Trani 17.1 Introduction 354 17.2 Seismic Ambient Noise Processing 356 17.3 Solution Implementation 358 17.4 Evaluation 369 17.5 Further Work 372 17.6 Conclusions 373 PART V DATA-INTENSIVE BEACONS OF SUCCESS 377
• 18. Data-Intensive Methods in Astronomy 381 Thomas D. Kitching, Robert G. Mann, Laura E. Valkonen, Mark S. Holliman, Alastair Hume, and Keith T. Noddle 18.1 Introduction 381 18.2 The Virtual Observatory 382 18.3 Data-Intensive Photometric Classification of Quasars 383 18.4 Probing the Dark Universe with Weak Gravitational Lensing 387 18.5 Future Research Issues 392 18.6 Conclusions 392
• 19. The World at One's Fingertips: Interactive Interpretation of Environmental Data 395 Jon Blower, Keith Haines, and Alastair Gemmell 19.1 Introduction 395 19.2 The Current State of the Art 397 19.3 The Technical Landscape 401 19.4 Interactive Visualization 403 19.5 From Visualization to Intercomparison 406 19.6 Future Development: The Environmental Cloud 409 19.7 Conclusions 411
• 20. Data-Driven Research in the Humanities--the DARIAH Research Infrastructure 417 Andreas Aschenbrenner, Tobias Blanke, Christiane Fritze, andWolfgang Pempe 20.1 Introduction 417 20.2 The Tradition of Digital Humanities 420 20.3 Humanities Research Data 422 20.4 Use Case 426 20.5 Conclusion and Future Development 429
• 21. Analysis of Large and Complex Engineering and Transport Data 431 Jim Austin 21.1 Introduction 431 21.2 Applications and Challenges 432 21.3 The Methods Used 434 21.4 Future Developments 438 21.5 Conclusions 439 References 440
• 22. Estimating Species Distributions--Across Space, Through Time, and with Features of the Environment 441 Steve Kelling, Daniel Fink, Wesley Hochachka, Ken Rosenberg, Robert Cook, Theodoros Damoulas, Claudio Silva, and William Michener 22.1 Introduction 442 22.2 Data Discovery, Access, and Synthesis 443 22.3 Model Development 448 22.4 Managing Computational Requirements 449 22.5 Exploring and Visualizing Model Results 450 22.6 Analysis Results 452 22.7 Conclusion 454 PART VI THE DATA-INTENSIVE FUTURE 459
• 23. Data-Intensive Trends 461 Malcolm Atkinson and Paolo Besana 23.1 Reprise 461 23.2 Data-Intensive Applications 469
• 24. Data-Rich Futures 477 Malcolm Atkinson 24.1 Future Data Infrastructure 478 24.2 Future Data Economy 485 24.3 Future Data Society and Professionalism 489 References 494 Appendix A: Glossary 499 Michelle Galea and Malcolm Atkinson Appendix B: DISPEL Reference Manual 507 Paul Martin Appendix C: Component Definitions 531 Malcolm Atkinson and Chee Sun Liew INDEX 537.
• (source: Nielsen Book Data)

This book presents the most up-to-date opportunities and challenges emerging in knowledge discovery, helping readers develop the technical skills to design and develop data-intensive methods and processes. Offering an introduction to the current R and D efforts worldwide, the book includes examples and case studies with strategies for addressing a wide variety of data-intensive challenges. The book includes a discussion of the DISPEL language, its development, enactment, and applications as well as data-intensive beacons of success, focusing on methods in astronomy, interactive interpretation of environment data, and data-driven research in humanities. A must-have resource for researchers in industry, governmental organizations, and academia.
(source: Nielsen Book Data)

• About the author vii INTRODUCTION 1 PART I CONSULTING FUNDAMENTALS 3
• Chapter 1: What is Consulting? 5
• Chapter 2: Preparing to Consult 25
• Chapter 3: Establishing Credibility 37
• Chapter 4: Managing Client Meetings 47 PART II CASE STUDIES 61 Case Study 1: Exploring a New Consulting Opportunity 63 Case Study 2: Presenting a Solution Approach 85 Case Study 3: Scoping a Study 101 PART III Additional Topics 117
• Chapter 5: Proposing a Consulting Service 119
• Chapter 6: Delivering a Consulting Service 133
• Chapter 7: Client Interactions and Related Obstacles 173
• Chapter 8: The Skill of Advising 193 Index 215.
• (source: Nielsen Book Data)

Delivers the essential practical skills needed to consult and make sharp, well prepared interactions in a wide range of business situations This comprehensive handbook covers the fundamental skills and attitudes required by successful consultants from novice to practitioner level, irrespective of their specialist area. It untangles the key variables present in any consulting service and introduces practical ways to improve their effectiveness based upon the author's experience of helping consulting organisations to develop and excel in the marketplace. The book explores consulting from the ground up' steering away from theory and focusing instead on practical application, providing a solid platform upon which to build further domain-specific competence. The Consultant's Handbook provides: * An understanding of the key variables that can be addressed in order to improve one's own consulting performance * A set of simple practices that can be implemented with immediate benefit to the reader * Practical insight into day-to-day real life consulting interactions * Confidence to implement the new ideas and approaches.
(source: Nielsen Book Data)

• Acknowledgments ix Introduction xiii Section I: Operational Challenges ... and Solutions
• Chapter 1: The Founder as the Sun 3
• Chapter 2: Replacing the Sun: A New Solar System 15
• Chapter 3: Ordering Off the Menu 33
• Chapter 4: Setting a Vision, Together 43 Section II: Financial Challenges ... and Solutions
• Chapter 5: It s Always About the Money 63
• Chapter 6: Splitting the Pies: Defining What Is Enough 79
• Chapter 7: Let s Make a Deal 99 Section III: Emotional Challenges ... and Solutions
• Chapter 8: It s Not Just an Office 117
• Chapter 9: Breaking Inertia 135
• Chapter 10: Derailed 147
• Chapter 11: Building a Legacy 165 Some Final Words 173 Bibliography 177 Supplemental Material 179 Interviewee Biographies 189 About the Authors 201 Index 205.
• (source: Nielsen Book Data)

An insightful look at leadership transition from the successor's perspective Success and Succession examines the leadership transition process from the successor's point of view, and outlines the considerations and strategies that lead to a better future for the business. With a focus on practical planning and execution, this insightful guide provides insight into the strategies that smooth the transition and help the new leadership make better business decisions. You'll learn when and how to start planning, who you need on your team, and the obstacles you should anticipate along the way. You'll learn to navigate the uncertainty the process entails, and how to identify opportunities for reciprocal understanding and adopt workable approaches for successful resolution of a multitude of transition issues. Interviews with those at various stages of transition highlight the real-world application of these ideas, and give you an inside look at what worked, what didn't, and what they wish they had thought of. The transition of leadership in an independent, non-public professional service business can be emotional and difficult for everyone. This book gives you a framework for smoothing the process and driving the best possible future of the business. * Consider the complexities of succession and transition * Balance conflicting dynamics of outgoing and incoming leadership * Plan for operational, financial, and emotional obstacles * Develop and execute a winning strategy for long term success The transition from founder to successor is far from an academic exercise, and is not linear. Answers are hard to find, and the ebb and flow of the process requires patience, creativity, and willingness to try again. Success and Succession provides a unique strategy for success, from the perspective of incoming leadership.
(source: Nielsen Book Data)

• Chapter 1. RRI and governance theory
• Chapter 2 Genealogy of the theory of governance
• Chapter 3 The reflexive theory of governance issues
• Chapter 4 The strong points of a reflexive governance theory
• Chapter 5 For the RRI reflexive governance: a sixth pillar?
• Chapter 6 The intellectual intervention in society as the key to the RRI reflexive governance.
• (source: Nielsen Book Data)

The governance theories that have developed over the past twenty years offer a new framework to consider and examine the collective conditions of a "Responsible Research and Innovation - RRI" linked up with the policy challenges of a society in transition in all its modes of regulation. Governance is often the poor relation of the reflections on the RRI. Yet, reflexive governance contains a model centered on the collective construction of responsibility in a perspective of accumulation of new knowledge resulting from the organization of partnerships between researchers and practitioners (transdisciplinary actions). This book will recall the genesis of the reflexive point of view in the context of the development of the theory of governance. It will then develop the strengths of the model and finally, will show the fruitfulness of its application to the field of the RRI.
(source: Nielsen Book Data)

Understand, evaluate, and visualize data About This Book * Learn basic steps of data analysis and how to use Python and its packages * A step-by-step guide to predictive modeling including tips, tricks, and best practices * Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn * Get acquainted with NumPy and use arrays and array-oriented computing in data analysis * Process and analyze data using the time-series capabilities of Pandas * Understand the statistical and mathematical concepts behind predictive analytics algorithms * Data visualization with Matplotlib * Interactive plotting with NumPy, Scipy, and MKL functions * Build financial models using Monte-Carlo simulations * Create directed graphs and multi-graphs * Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization-predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: ? Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan ? Learning Predictive Analytics with Python, Ashish Kumar ? Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization.
(source: Nielsen Book Data)

Quick solutions to common programming problems with the latest features of C# 7.0, .NET Core 1.1, and Visual Studio 2017 About This Book * Easy-to-follow recipes to get you up-and-running with the new features of C# 7 and .NET Core 1.1 * Practical solutions to assist you with microservices and serverless computing in C# * Explore the new Visual Studio environment and write more secure code in it Who This Book Is For The book will appeal to C# and .NET developers who have a basic familiarity with C# and the Visual Studio 2015 environment What You Will Learn * Writing better and less code to achieve the same result as in previous versions of C# * Working with analyzers in Visual Studio * Working with files, streams, and serialization * Writing high-performant code in C# and understanding multi-threading * Demystifying the Rx library using Reactive extensions * Exploring .Net Core 1.1 and ASP.NET MVC * Securing your applications and learning new debugging techniques * Designing and building a microservice architecture * Using Azure and AWS for serverless computing with C# In Detail C# has recently been open-sourced and C# 7 comes with a host of new features for building powerful, cross-platform applications. This book will be your solution to some common programming problems that you come across with C# and will also help you get started with .NET Core 1.1. Through a recipe-based approach, this book will help you overcome common programming challenges and get your applications ready to face the modern world. We start by running you through new features in C# 7, such as tuples, pattern matching, and so on, giving you hands-on experience with them. Moving forward, you will work with generics and the OOP features in C#. You will then move on to more advanced topics, such as reactive extensions, Regex, code analyzers, and asynchronous programming. This book will also cover new, cross-platform .NET Core 1.1 features and teach you how to utilize .NET Core on macOS. Then, we will explore microservices as well as serverless computing and how these benefit modern developers. Finally, you will learn what you can do with Visual Studio 2017 to put mobile application development across multiple platforms within the reach of any developer. Style and approach A unique recipe-based guide that will help you gain a solid understanding of the new concepts in C# 7.0 and Visual Studio 2017.
(source: Nielsen Book Data)

• foreword ix acknowledgments xi about the author xiii introduction 1
• chapter 1 the importance of context 19
• chapter 2 choosing an effective visual 35
• chapter 3 clutter is your enemy! 71
• chapter 4 focus your audience s attention 99
• chapter 5 think like a designer 127
• chapter 6 dissecting model visuals 151
• chapter 7 lessons in storytelling 165
• chapter 8 pulling it all together 187
• chapter 9 case studies 207
• chapter 10 final thoughts 241 bibliography 257 index 261.
• (source: Nielsen Book Data)

Don't simply show your data tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: * Understand the importance of context and audience * Determine the appropriate type of graph for your situation * Recognize and eliminate the clutter clouding your information * Direct your audience's attention to the most important parts of your data * Think like a designer and utilize concepts of design in data visualization * Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data Storytelling with Data will give you the skills and power to tell it!.
(source: Nielsen Book Data)