Data capture and analysis Books

Book SynopsisThe general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.Table of Contents Advances in Psychometric Methods for Uncovering Latent Structure and Cognitive Processes Improving Attribute Classification Accuracy in High Dimensional Data: A Four-Step Latent Regression Approach A Dynamic Generalized Mixed Effect Model for Intensive Binary Temporal-Spatio Data From an Eye-Tracking Technique Application of Network Analysis in Understanding Collaborative Problem Solving Processes and Skills IRTree Modeling of Cognitive Processes Based on Outcome and Intermediate Data Prerequisite Structure Finding Using the Conjunctive Root Causes Model A Graphical and Generalized Linear Model Approach to Latent Variable Modeling Modeling Hint Requests, Response Times, and Response Accuracy in Adaptive Learning Systems Identifying Observable Outcomes in Game-Based Assessments A Regime-Switching (RS) Framework for Formulating Multi-Phase Linear and Nonlinear Growth Curve Models About the Editors.

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Book SynopsisIn the digital age, data is the new currency. However, amassing heaps of data means nothing if itdoesn't lead to actionable insights. It's not enough to just present numbers; to truly resonate withan audience, data needs a narrative. "Data Storytelling and Translation" bridges the chasm between numbers and narratives. Learn the intricacies of translating raw data into compelling stories that captivate, inform, and inspire action. The book covers proven frameworks for converting data into compelling narratives, strategies to tailor data stories to different audiences, techniques to avoid common pitfalls and biases in data representation, the balance between aesthetics and accuracy in data visualization, and uses real-world case studies illustrating the power of effective data storytelling. Whether you're a data scientist, business analyst, student or a decision-maker, this book offers the tools to articulate the true value of your data.Table of Contents 1: The Age of the Data Translator 2: All Decisions Start with People 3: Start with Good Questions and Great Listening 4: Being Fluent in the Language of Data 5: Identify, Understand, and Frame Problems 6: Simplifying Insights Through Metrics and Objectives 7: Painting Your Data Story 8: Leveraging Visuals to Share Insights and Compel Action 9: Leveraging Dashboards in Your Communication 10: Communicating Your Data Story Epilogue Index

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Book SynopsisPython 3 and Data Visualization offers readers a deep dive into the world of Python 3 programming and the art of data visualization. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, seamlessly leading into the world of data visualization using prominent libraries such as Matplotlib. Chapter 6 immerses the reader in Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. The appendix covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. The book also includes companion files with numerous Python code samples and figures. From foundational Python concepts to the intricacies of data visualization, this book serves as a comprehensive resource for both beginners and seasoned professionals.FEATURES: Covers numerous tools for mastering visualization including NumPy, Pandas, SQL, Matplotlib, and Seaborn Includes an introductory chapter on Python 3 basics Features companion files with numerous Python code samples and figures Table of Contents 1: Introduction to Python 3 2: NumPyand Data Visualization 3: Pandas and Data Visualization 4: Pandas and SQL 5:Matplotlib for Data Visualization 6: Seaborn for Data Visualization Appendix:SVG and D3

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Book SynopsisThis text provides an overview of the principles and practices involved in managing and operating data centers. It covers topics such as data center design, infrastructure management, virtualization, cloud computing, and security. The book is intended for IT professionals and data center managers who are responsible for the operation and maintenance of data centers. It provides valuable insights and best practices for optimizing data center performance, reliability, and efficiency.Table of Contents Chapter 1 Introduction to Data Center Management Chapter 2 Data Center Topologies and Network Architecture Chapter 3 Security and Compliance in Data Protection Chapter 4 Monitoring and Management Tools Chapter 5 Virtualization and Cloud Computing Chapter 6 Importance of Power and Cooling Management Chapter 7 Challenges in Data Center Management Chapter 8 Future Trends in Data Center Management

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Book SynopsisIt is almost universally accepted that requirements documents for new or enhanced IT systems by business analysts should include a ‘data model’ to represent the information that has to be handled by the system. Starting from first principles, this book will help business analysts to develop the skills required to construct data models through comprehensive coverage of entity relationship and class modelling, in line with the BCS Data Analysis syllabus. In addition to covering the topics in the syllabus, the book also includes extra information of interest including data model quality and taking a requirement model into database design.Trade Review'“Modelling Business Information” by Keith Gordon, is aimed at those who are new to business analysis or information modelling. Keith draws on a wealth of experience in information management, both as a practitioner, and as a lecturer with the Open University in his writing. The first six chapters provide an accessible and clear foundation in the topic covering the reasons for developing information models, the basic elements of entity-relationship diagrams, how to develop an information model from basic information requirements, and finally how to normalise existing data. I particularly like that it uses two graphical notations, the Barker-Ellis notation, noted for its readability, and the ubiquitous Unified Modelling Language notation, which helps to demonstrate that there are different notations that entity-relationship models can be developed in. This first part of the book also takes care to cover the syllabus for the Data Analysis certificate that is part of the scheme for the BCS Advanced International Diploma in Business Analysis. The second part of the book covers a range of more advanced topics from naming conventions and yet more entity-relationship model notations, to considerations of quality in information models, corporate data models, modelling for business intelligence applications, and finally goes on to look at data and database topics including an overview of SQL, and moving to database design and optimisation. Overall, the book provides an excellent grounding in the full range of topics related to information modelling.' -- Matthew West * Director *‘Anyone interested in a thoughtful, well-done text on how to do high-quality business analytical data modelling should definitely proceed with this book.’ -- David Hay * CEO *'“Modelling Business Information” provides an introduction to data modeling, to the nomenclature used by common modeling techniques, and to techniques for representing common patterns. This is a useful book for business analysts who are creating the information model as well as for business and IT users who need to understand a data model.' -- Keith W. Hare * Senior Consultant *'Keith Gordon’s wonderfully compact yet thorough introduction to business-friendly information modelling is a terrific contribution to the field. Globally, there’s a surge of interest in data modelling as a powerful tool for improving communication, especially with professionals who used to think business-oriented entity-relationship modelling didn't need to be in their tool kits. Business analysts, Agile developers, data scientists, big data specialists, and other professionals will all benefit from Keith’s work.' -- Alec Sharp * Senior Consultant, Clariteq *'As the roles of Data and Business Analysts become more intertwined, this book is timely in its publication. Businesses often fail to recognise information is a key resource and are confused by how it is presented or overwhelmed its complexity during use. Keith brings to the forefront of the readers mind the importance of communicating and analysing the relationship between Business, Information, Systems and Data, and the value in developing models cooperatively, gaining "consensus, not perfection“ from stakeholders. Simple everyday examples and analogies to support the readers understanding and make the subject more relatable are used. I enjoyed reading the book and completing the exercises. An excellent learning aid for Analysts who are new to modelling or need reminding of good practice.' -- Katie Walsh * Business Analyst and Mentor *Table of ContentsIntroduction Part 1: The Basics Chapter 1: Why business analysts should model information Chapter 2: Modelling the things of interest to the business and the relationships between them Chapter 3: Modelling more complex relationships Chapter 4: Drawing and validating data model diagrams Chapter 5: Recording information about things Chapter 6: Rationalising data using normalisation Part 2: Supplementary Material Chapter 7: Other modelling notations Chapter 8: The naming of artefacts on information models Chapter 9: Information model quality Chapter 10: Corporate information and data models Chapter 11: Data and databases Chapter 12: Business intelligence Chapter 13: Advances in SQL (or why business analysts should not be in the weeds) Chapter 14: Taking a requirements information model into database design Appendix A: Table of equivalences Appendix B: Bibliography Appendix C: Solutions to the exercises

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Book SynopsisThere is increasing pressure to protect computer networks against unauthorized intrusion, and some work in this area is concerned with engineering systems that are robust to attack. However, no system can be made invulnerable. Data Analysis for Network Cyber-Security focuses on monitoring and analyzing network traffic data, with the intention of preventing, or quickly identifying, malicious activity.Such work involves the intersection of statistics, data mining and computer science. Fundamentally, network traffic is relational, embodying a link between devices. As such, graph analysis approaches are a natural candidate. However, such methods do not scale well to the demands of real problems, and the critical aspect of the timing of communications events is not accounted for in these approaches.This book gathers papers from leading researchers to provide both background to the problems and a description of cutting-edge methodology. The contributors are from diverse institutions and areas of expertise and were brought together at a workshop held at the University of Bristol in March 2013 to address the issues of network cyber security. The workshop was supported by the Heilbronn Institute for Mathematical Research.Table of ContentsForeword (Geoff Robinson); Introduction (Niall Adams); Inference for Graphs and Networks (Benjamin P Olding and Patrick J Wolfe); Rapid Detection of Attacks by Quickest Changepoint Detection Methods (Alexander G Tartakovsky); Statistical Detection of Intruders Within Computer Networks Using Scan Statistics (Joshua Neil, Curtis Storlie, Curtis Hash and Alex Brugh.); Characterizing Dynamic Group Behavior in Social Networks for Cybernetics (Sumeet Dua and Pradeep Chowriappa); Several Approaches for Detecting Anomalies in Network Traffic Data (Celine Levy-Leduc); Monitoring a Device in a Communication Network (Nicholas A Heard and Melissa Turcotte).

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Book SynopsisThe growing importance of data science, and the increasing role of information professionals in the management and use of data, are brought together in Practical Data Science for Information Professionals to provide a practical introduction specifically designed for information professionals.Data science has a wide range of applications within the information profession, from working alongside researchers in the discovery of new knowledge, to the application of business analytics for the smoother running of a library or library services. Practical Data Science for Information Professionals provides an accessible introduction to data science, using detailed examples and analysis on real data sets to explore the basics of the subject.This book will be of interest to all types of libraries around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, the book aims to reduce barriers for readers to use the lessons learned within.Trade Review'If libraries and librarians are to be serious about the ‘I’ in LIS, then analysing data to find meaning for our customers will be a core component of the service offering. David Stuart’s book is an excellent entry point to the discipline.' -- Ian McCallum * Journal of the Australian Library and Information Association *Table of Contents1. What is an Ontology? 2. Ontologies and the Semantic Web 3. Existing Ontologies 4. Adopting Ontologies 5. Building Ontologies 6. Interrogating Ontologies 7. The Future of Ontologies and the Information Professional

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Book SynopsisPractical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining.As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand:· the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use.Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.Trade Review'If libraries and librarians are to be serious about the ‘I’ in LIS, then analysing data to find meaning for our customers will be a core component of the service offering. David Stuart’s book is an excellent entry point to the discipline.' -- Ian McCallum * Journal of the Australian Library and Information Association *Table of ContentsContentsFigures Tables Boxes Preface 1 What is data science? Data, information, knowledge, wisdom Data everywhere The data deserts Data science The potential of data science From research data services to data science in libraries Programming in libraries Programming in this book The structure of this book 2 Little data, big data Big data Data formats Standalone files Application programming interfaces Unstructured data Data sources Data licences 3 The process of data science Modelling the data science process Frame the problem Collect data Transform and clean data Analyse data Visualise and communicate data Frame a new problem 4 Tools for data analysis Finding tools Software for data science Programming for data science 5 Clustering and social network analysis Network graphs Graph terminology Network matrix Visualisation Network analysis 6 Predictions and forecasts Predictions and forecasts beyond data science Predictions in a world of (limited) data Predicting and forecasting for information professionals Statistical methodologies 7 Text analysis and mining Text analysis and mining, and information professionals Natural language processing Keywords and n-grams 8 The future of data science and information professionalsEight challenges to data scienceTen steps to data science librarianship The final word: playReferences Appendix – Programming concepts for data science Variables, data types and other classes Import libraries Functions and methods Loops and conditionals Final words of advice Further reading Index

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Book SynopsisCamera trapping is a powerful and now widely used tool in scientific research on wildlife ecology and management. It provides a unique opportunity for collecting knowledge, investigating the presence of animals, or recording and studying behaviour. Its visual nature makes it easy to successfully convey findings to a wide audience. This book provides a much-needed guide to the sound use of camera trapping for the most common ecological applications to wildlife research. Each phase involved in the use of camera trapping is covered: - Selecting the right camera type - Set-up and field deployment of your camera trap - Defining the sampling design: presence/absence, species inventory, abundance; occupancy at species level; capture-mark-recapture for density estimation; behavioural studies; community-level analysis - Data storage, management and analysis for your research topic, with illustrative examples for using R and Excel - Using camera trapping for monitoring, conservation and public engagement. Each chapter in this edited volume is essential reading for students, scientists, ecologists, educators and professionals involved in wildlife research or management.Trade Review...a thorough, concise handbook on how to design and conduct a study involving camera traps. It would be very useful for both under- and post-graduates and for those, like me, who are new to the subject, so I thoroughly recommend it for a university library and for anyone who is considering using camera traps as a component of a study. -- Siân Waters, Barbary Macaque Awareness & Conservation * Primate Eye *If you are surveying in a systematic way through trail cameras you will need to structure the sampling and analyse the results in methodical ways. It is here that a recent book from Pelagic Publishing, Camera Trapping for Wildlife Research, provides much use. With a scholarly approach and abundant references, the book has detailed advice on camera trapping for faunal inventories, occupancy studies, capture-recapture methods, and behavioural studies. The book excels in its detail on survey design, sampling design, and data management. There is an extended case study of Eurasian lynx abundance and density estimation in the NW Swiss Alps, while the behavioural studies section looks at Eurasian lynx scent marking as well as the tree rubbing behaviour of brown bears. -- Rick Minter * ECOS *An in-depth overview of the logistics of studies that use camera traps and provides numerous real-world examples of analyzing data collected by camera traps using contemporary approaches. I believe that the book is a must for wildlife researchers considering the use of camera traps. -- Adam Duarte, Department of Fisheries and Wildlife, Oregon State University * Journal of Wildlife Management *It is well-written, and its few images are well chosen to illustrate and clarify relevant concepts. The structure is sensible, taking the reader from introductory chapters about camera types, deployment and survey design through to more in-depth chapters describing how this information can be analysed and interpreted. -- Mark Wilson * BTO About Birds *As Professor Luigi Boitani states in his foreword, "This book is exactly what all field biologists need to have to learn about the current state of development of the technique". Based on decades of direct experience, well before the arrival of the modern digital camera trap, the book covers almost all the facets of using "photographic trapping" to obtain data on wildlife. The entire text is written with a direct approach, taking into account the real-world problems (and their solutions, that the Authors devised in several years of practice) occurring to anyone using camera trapping, from trapping scheme design to data analysis, not excluding new developments such as large-scale monitoring and citizen science. The impressive, thorough coverage of so many different topics has been achieved thanks to the active participation of other contributors (Jorge A. Ahumada, Eric Fergus, Danilo Foresti, Johanna Hurtado Astaiza, James MacCarthy, Paul Meek, Badru Mugerwa, Timothy G. O'Brien, Daniel Spitale and Simone Tenan, to name a few), that shared their direct experience in the field. Notwithstanding the practical approach, in each case (and in particiular in the chapters dealing with experimental design and data analysis applications) the theoretical background is just there, briefly recapitulated in a way useful to beginners as an introduction to more in-depth references, but also useful to the expert, as a beneficial refresher. * Hystrix - Italian Journal of Mammalogy *Table of Contents1. Introduction 2. Camera features related to specific ecological applications 3. Field deployment of camera traps 4. Camera trap data management and interoperability 5. Presence/absence and species inventory 6. Species-level occupancy analysis 7. Capture–recapture methods for density estimation 8. Behavioural studies 9. Community-level occupancy analysis 10. Camera trapping as a monitoring tool at national and global levels 11. Camera traps and public engagement Appendices Glossary Index

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Book SynopsisThe modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.Table of Contents1. A brief introduction to R 2. Basic math 3. Introduction to R objects 4. Making and importing data objects 5. Managing and exporting data objects 6. R object types and their properties 7. Working with data objects 8. Manipulating data objects 9. Summarizing data 10. Tabulation 11. Graphics: basic charts 12. Graphics: adding to plots 13. Graphics: advanced methods 14. Analyze data: statistical analyses 15. Programming tools Appendix Index

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Book SynopsisWe can learn a lot from previous examples of citizen science and especially where data was collected just out of curiosity, only to be used many years later. Looking at citizen science projects can help us appreciate the public understanding of science in a broader context and encourage wider participation.

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Book SynopsisThe different facets of the sharing economy offer numerous opportunities for businesses ? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data. This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.Table of ContentsPreface xi Introduction xiii Part 1. The Sharing Economy or the Emergence of a New Business Model 1 Chapter 1. The Sharing Economy: A Concept Under Construction 3 1.1. Introduction 3 1.2. From simple sharing to the sharing economy 5 1.2.1. The genesis of the sharing economy and the break with “consumer” society 5 1.2.2. The sharing economy: which economy? 8 1.3. The foundations of the sharing economy 10 1.3.1. Peer-to-peer (P2P): a revolution in computer networks 10 1.3.2. The gift: the abstract aspect of the sharing economy 13 1.3.3. The service economy and the offer of use 18 1.4. Conclusion 24 Chapter 2. An Opportunity for the Business World 25 2.1. Introduction 25 2.2. Prosumption: a new sharing economy trend for the consumer 27 2.3. Poverty: a target in the spotlight of the shared economy 29 2.4. Controversies on economic opportunities of the sharing economy 31 2.5. Conclusion 37 Chapter 3. Risks and Issues of the Sharing Economy 39 3.1. Introduction 39 3.2. Uberization: a white grain or just a summer breeze? 40 3.3. The sharing economy: a disruptive model 43 3.4. Major issues of the sharing economy 47 3.5. Conclusion 50 Chapter 4. Digital Platforms and the Sharing Mechanism 51 4.1. Introduction 51 4.2. Digital platforms: “What growth!” 52 4.3. Digital platforms or technology at the service of the economy 54 4.4. From the sharing economy to the sharing platform economy 57 4.5. Conclusion 59 Part 2. Big Data Analytics at the Service of the Sharing Economy 61 Chapter 5. Beyond the Word “Big”: The Changes 63 5.1. Introduction 63 5.2. The 3 Vs and much more: volume, variety, velocity 64 5.2.1. Volume 65 5.2.2. The variety 66 5.2.3. Velocity 67 5.2.4. What else? 68 5.3. The growth of computing and storage capacities 69 5.3.1. Big Data versus Big Computing 70 5.3.2. Big Data storage 71 5.3.3. Updating Moore’s Law 73 5.4. Business context change in the era of Big Data 74 5.4.1. The decision-making process and the dynamics of value creation 75 5.4.2. The emergence of new data-driven business models 77 5.5. Conclusion 78 Chapter 6. The Art of Analytics 81 6.1. Introduction 81 6.2. From simple analysis to Big Data analytics 82 6.2.1. Descriptive analysis: learning from past behavior to influence future outcomes 84 6.2.2. Predictive analysis: analyzing data to predict future outcomes 84 6.2.3. Prescriptive analysis: recommending one or more action plan(s) 85 6.2.4. From descriptive analysis to prescriptive analysis: an example 87 6.3. The process of Big Data analytics: from the data source to its analysis 88 6.3.1. Definition of objectives and requirements 90 6.3.2. Data collection 91 6.3.3. Data preparation 92 6.3.4. Exploration and interpretation 94 6.3.5. Modeling 95 6.3.6. Deployment 97 6.4. Conclusion 97 Chapter 7. Data and Platforms in the Sharing Context 99 7.1. Introduction 99 7.2. Pioneers in Big Data 101 7.2.1. Big Data on Walmart’s shelves 101 7.2.2. The Big Data behind Netflix’s success story 102 7.2.3. The Amazon version of Big Data 103 7.2.4. Big data and social networks: the case of Facebook 104 7.2.5. IBM and data analysis in the health sector 105 7.3. Data, essential for sharing 106 7.3.1. Data and platforms at the heart of the sharing economy 108 7.3.2. The data of sharing economy companies 110 7.3.3. Privacy and data security in a sharing economy 111 7.3.4. Open Data and platform data sharing 114 7.4. Conclusion 116 Chapter 8. Big Data Analytics Applied to the Sharing Economy 119 8.1. Introduction 119 8.2. Big Data and Machine Learning algorithms serving the sharing economy 121 8.2.1. Machine Learning algorithms 122 8.2.2. Algorithmic applications in the sharing economy context 124 8.3. Big Data technologies: the sharing economy companies’ toolbox 125 8.3.1. The appearance of a new concept and the creation of new technologies 127 8.4. Big Data on the agenda of sharing economy companies 130 8.4.1. Uber 131 8.4.2. Airbnb 132 8.4.3. BlaBlaCar 133 8.4.4. Lyft 134 8.4.5. Yelp 135 8.4.6. Other cases 137 8.5. Conclusion 139 Part 3. The Sharing Economy? Not Without Big Data Algorithms 141 Chapter 9. Linear Regression 143 9.1. Introduction 143 9.2. Linear regression: an advanced analysis algorithm 144 9.2.1. How are regression problems identified? 145 9.2.2. The linear regression model 146 9.2.3. Minimizing modeling error 148 9.3. Other regression methods 149 9.3.1. Logistic regression 150 9.3.2. Additional regression models: regularized regression 151 9.4. Building your first predictive model: a use case 152 9.4.1. What variables help set a rental price on Airbnb? 152 9.5. Conclusion 169 Chapter 10. Classification Algorithms 171 10.1. Introduction 171 10.2. A tour of classification algorithms 172 10.2.1. Decision trees 172 10.2.2. Naïve Bayes 175 10.2.3. Support Vector Machine (SVM) 177 10.2.4. Other classification algorithms 179 10.3. Modeling Airbnb prices with classification algorithms 183 10.3.1. The work that’s already been done: overview 184 10.3.2. Models based on trees: decision tree versus Random Forest 185 10.3.3. Price prediction with kNN 190 10.4. Conclusion 193 Chapter 11. Cluster Analysis 195 11.1. Introduction 195 11.2. Cluster analysis: general framework 196 11.2.1. Cluster analysis applications 197 11.2.2. The clustering algorithm and the similarity measure 198 11.3. Grouping similar objects using k-means 200 11.3.1. The k-means algorithm 201 11.3.2. Determine the number of clusters 203 11.4. Hierarchical classification 205 11.4.1. The hierarchical model approach 206 11.4.2. Dendrograms 207 11.5. Discovering hidden structures with clustering algorithms 208 11.5.1. Illustration of the classification of prices based on different characteristics using the k-means algorithm 209 11.5.2. Identify the number of clusters k 210 11.6. Conclusion 213 Conclusion 215 References 217 Index 233

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Book SynopsisData science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences. Table of ContentsPreface xi Part 1. Symbolic Data 1 Chapter 1. Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework 3Edwin DIDAY 1.1. Introduction 4 1.2. Introduction to Symbolic Data Analysis 6 1.2.1. What are complex data? 6 1.2.2. What are “classes” and “class of complex data”? 7 1.2.3. Which kind of class variability? 7 1.2.4. What are “symbolic variables” and “symbolic data tables”? 7 1.2.5. Symbolic Data Analysis (SDA) 9 1.3. Symbolic data tables from Dynamic Clustering Method and EM 10 1.3.1. The “dynamical clustering method” (DCM) 10 1.3.2. Examples of DCM applications 10 1.3.3. Clustering methods by mixture decomposition 12 1.3.4. Symbolic data tables from clustering 13 1.3.5. A general way to compare results of clustering methods by the “explanatory power” of their associated symbolic data table 15 1.3.6. Quality criteria of classes and variables based on the cells of the symbolic data table containing intervals or inferred distributions 15 1.4. Criteria for ranking individuals, classes and their bar chart descriptive symbolic variables 16 1.4.1. A theoretical framework for SDA 16 1.4.2. Characterization of a category and a class by a measure of discordance 18 1.4.3. Link between a characterization by the criteria W and the standard Tf-Idf 19 1.4.4. Ranking the individuals, the symbolic variables and the classes of a bar chart symbolic data table 21 1.5. Two directions of research 23 1.5.1. Parametrization of concordance and discordance criteria 23 1.5.2. Improving the explanatory power of any machine learning tool by a filtering process 25 1.6. Conclusion 27 1.7. References 28 Chapter 2. Likelihood in the Symbolic Context 31Richard EMILION and Edwin DIDAY 2.1. Introduction 31 2.2. Probabilistic setting 32 2.2.1. Description variable and class variable 32 2.2.2. Conditional distributions 33 2.2.3. Symbolic variables 33 2.2.4. Examples 35 2.2.5. Probability measures on (ℂ, C), likelihood 37 2.3. Parametric models for p = 1 38 2.3.1. LDA model 38 2.3.2. BLS method 41 2.3.3. Interval-valued variables 42 2.3.4. Probability vectors and histogram-valued variables 42 2.4. Nonparametric estimation for p = 1 45 2.4.1. Multihistograms and multivariate polygons 45 2.4.2. Dirichlet kernel mixtures 45 2.4.3. Dirichlet Process Mixture (DPM) 45 2.5. Density models for p ≥ 2 46 2.6. Conclusion 46 2.7. References 47 Chapter 3. Dimension Reduction and Visualization of Symbolic Interval-Valued Data Using Sliced Inverse Regression 49Han-Ming WU, Chiun-How KAO and Chun-houh CHEN 3.1. Introduction 49 3.2. PCA for interval-valued data and the sliced inverse regression 51 3.2.1. PCA for interval-valued data 51 3.2.2. Classic SIR 52 3.3. SIR for interval-valued data 53 3.3.1. Quantification approaches 54 3.3.2. Distributional approaches 56 3.4. Projections and visualization in DR subspace 58 3.4.1. Linear combinations of intervals 58 3.4.2. The graphical representation of the projected intervals in the 2D DR subspace 59 3.5. Some computational issues 61 3.5.1. Standardization of interval-valued data 61 3.5.2. The slicing schemes for iSIR 62 3.5.3. The evaluation of DR components 62 3.6. Simulation studies 63 3.6.1. Scenario 1: aggregated data 63 3.6.2. Scenario 2: data based on interval arithmetic 63 3.6.3. Results 64 3.7. A real data example: face recognition data 65 3.8. Conclusion and discussion 73 3.9. References 74 Chapter 4. On the “Complexity” of Social Reality. Some Reflections About the Use of Symbolic Data Analysis in Social Sciences 79Frédéric LEBARON 4.1. Introduction 79 4.2. Social sciences facing “complexity” 80 4.2.1. The total social fact, a designation of “complexity” in social sciences 80 4.2.2. Two families of answers 80 4.2.3. The contemporary deepening of the two approaches, “reductionist” and “encompassing” 81 4.2.4. Issues of scale and heterogeneity 82 4.3. Symbolic data analysis in the social sciences: an example 83 4.3.1. Symbolic data analysis 83 4.3.2. An exploratory case study on European data 83 4.3.3. A sociological interpretation 94 4.4. Conclusion 95 4.5. References 96 Part 2. Complex Data 99 Chapter 5. A Spatial Dependence Measure and Prediction of Georeferenced Data Streams Summarized by Histograms 101Rosanna VERDE and Antonio BALZANELLA 5.1. Introduction 101 5.2. Processing setup 103 5.3. Main definitions 104 5.4. Online summarization of a data stream through CluStream for Histogram data 106 5.5. Spatial dependence monitoring: a variogram for histogram data 107 5.6. Ordinary kriging for histogram data 110 5.7. Experimental results on real data 112 5.8. Conclusion 116 5.9. References 116 Chapter 6. Incremental Calculation Framework for Complex Data 119Huiwen WANG, Yuan WEI and Siyang WANG 6.1. Introduction 119 6.2. Basic data 122 6.2.1. The basic data space 122 6.2.2. Sample covariance matrix 123 6.3. Incremental calculation of complex data 124 6.3.1. Transformation of complex data 124 6.3.2. Online decomposition of covariance matrix 125 6.3.3. Adopted algorithms 128 6.4. Simulation studies 131 6.4.1. Functional linear regression 131 6.4.2. Compositional PCA 133 6.5. Conclusion 135 6.6. Acknowledgment 135 6.7. References 135 Part 3. Network Data 139 Chapter 7. Recommender Systems and Attributed Networks 141Françoise FOGELMAN-SOULIÉ, Lanxiang MEI, Jianyu ZHANG, Yiming LI, Wen GE, Yinglan LI and Qiaofei YE 7.1. Introduction 141 7.2. Recommender systems 142 7.2.1. Data used 143 7.2.2. Model-based collaborative filtering 145 7.2.3. Neighborhood-based collaborative filtering 145 7.2.4. Hybrid models 148 7.3. Social networks 150 7.3.1. Non-independence 150 7.3.2. Definition of a social network 150 7.3.3. Properties of social networks 151 7.3.4. Bipartite networks 152 7.3.5. Multilayer networks 153 7.4. Using social networks for recommendation 154 7.4.1. Social filtering 154 7.4.2. Extension to use attributes 155 7.4.3. Remarks 156 7.5. Experiments 156 7.5.1. Performance evaluation 156 7.5.2. Datasets 157 7.5.3. Analysis of one-mode projected networks 158 7.5.4. Models evaluated 160 7.5.5. Results 160 7.6. Perspectives 163 7.7. References 163 Chapter 8. Attributed Networks Partitioning Based on Modularity Optimization 169David COMBE, Christine LARGERON, Baptiste JEUDY, Françoise FOGELMAN-SOULIÉ and Jing WANG 8.1. Introduction 169 8.2. Related work 171 8.3. Inertia based modularity 172 8.4. I-Louvain 174 8.5. Incremental computation of the modularity gain 176 8.6. Evaluation of I-Louvain method 179 8.6.1. Performance of I-Louvain on artificial datasets 179 8.6.2. Run-time of I-Louvain 180 8.7. Conclusion 181 8.8. References 182 Part 4. Clustering 187 Chapter 9. A Novel Clustering Method with Automatic Weighting of Tables and Variables 189Rodrigo C. DE ARAÚJO, Francisco DE ASSIS TENORIO DE CARVALHO and Yves LECHEVALLIER 9.1. Introduction 189 9.2. Related Work 190 9.3. Definitions, notations and objective 191 9.3.1. Choice of distances 192 9.3.2. Criterion W measures the homogeneity of the partition P on the set of tables 193 9.3.3. Optimization of the criterion W 195 9.4. Hard clustering with automated weighting of tables and variables 196 9.4.1. Clustering algorithms MND–W and MND–WT 196 9.5. Applications: UCI data sets 201 9.5.1. Application I: Iris plant 201 9.5.2. Application II: multi-features dataset 204 9.6. Conclusion 206 9.7. References 206 Chapter 10. Clustering and Generalized ANOVA for Symbolic Data Constructed from Open Data 209Simona KORENJAK-ČERNE, Nataša KEJŽAR and Vladimir BATAGELJ 10.1. Introduction 209 10.2. Data description based on discrete (membership) distributions 210 10.3. Clustering 212 10.3.1. TIMSS – study of teaching approaches 215 10.3.2. Clustering countries based on age–sex distributions of their populations 217 10.4. Generalized ANOVA 221 10.5. Conclusion 225 10.6. References 226 List of Authors 229 Index 233

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Book SynopsisBIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.Table of ContentsPreface xiYannis DIMOTIKALIS, Alex KARAGRIGORIOU, Christina PARPOULA and Christos H. SKIADAS Part 1. Computational Data Analysis 1 Chapter 1. A Variant of Updating PageRank in Evolving Tree Graphs 3Benard ABOLA, Pitos Seleka BIGANDA, Christopher ENGSTRÖM, John Magero MANGO, Godwin KAKUBA and Sergei SILVESTROV 1.1. Introduction 3 1.2. Notations and definitions 5 1.3. Updating the transition matrix 5 1.4. Updating the PageRank of a tree graph 10 1.4.1. Updating the PageRank of tree graph when a batch of edges changes 12 1.4.2. An example of updating the PageRank of a tree 15 1.5. Maintaining the levels of vertices in a changing tree graph 17 1.6. Conclusion 21 1.7. Acknowledgments 21 1.8. References 21 Chapter 2. Nonlinearly Perturbed Markov Chains and Information Networks 23Benard ABOLA, Pitos Seleka BIGANDA, Sergei SILVESTROV, Dmitrii SILVESTROV, Christopher ENGSTRÖM, John Magero MANGO and Godwin KAKUBA 2.1. Introduction 23 2.2. Stationary distributions for Markov chains with damping component 26 2.2.1. Stationary distributions for Markov chains with damping component 26 2.2.2. The stationary distribution of the Markov chain X0,n 28 2.3. A perturbation analysis for stationary distributions of Markov chains with damping component 29 2.3.1. Continuity property for stationary probabilities 29 2.3.2. Rate of convergence for stationary distributions 29 2.3.3. Asymptotic expansions for stationary distributions 30 2.3.4. Results of numerical experiments 32 2.4. Coupling and ergodic theorems for perturbed Markov chains with damping component 39 2.4.1. Coupling for regularly perturbed Markov chains with damping component 39 2.4.2. Coupling for singularly perturbed Markov chains with damping component 41 2.4.3. Ergodic theorems for perturbed Markov chains with damping component in the triangular array mode 42 2.4.4. Numerical examples 43 2.5. Acknowledgments 51 2.6. References 51 Chapter 3. PageRank and Perturbed Markov Chains 57Pitos Seleka BIGANDA, Benard ABOLA, Christopher ENGSTRÖM, Sergei SILVESTROV, Godwin KAKUBA and John Magero MANGO 3.1. Introduction 57 3.2. PageRank of the first-order perturbed Markov chain 59 3.3. PageRank of the second-order perturbed Markov chain 60 3.4. Rates of convergence of Page Ranks of first- and second-order perturbed Markovchains 70 3.5. Conclusion 72 3.6. Acknowledgments 72 3.7. References 72 Chapter 4. Doubly Robust Data-driven Distributionally Robust Optimization 75Jose BLANCHET, Yang KANG, Fan ZHANG, Fei HE and Zhangyi HU 4.1. Introduction 75 4.2. DD-DRO, optimal transport and supervised machine learning 79 4.2.1. Optimal transport distances and discrepancies 80 4.3. Data-driven selection of optimal transport cost function 81 4.3.1. Data-driven cost functions via metric learning procedures 81 4.4. Robust optimization for metric learning 83 4.4.1. Robust optimization for relative metric learning 83 4.4.2. Robust optimization for absolute metric learning 86 4.5. Numerical experiments 88 4.6. Discussion and conclusion 89 4.7. References 89 Chapter 5. A Comparison of Graph Centrality Measures Based on Lazy Random Walks 91Collins ANGUZU, Christopher ENGSTRÖM and Sergei SILVESTROV 5.1. Introduction 91 5.1.1. Notations and abbreviations 93 5.1.2. Linear systems and the Neumann series 94 5.2. Review on some centrality measures 95 5.2.1. Degree centrality 95 5.2.2. Katz status and β-centralities 95 5.2.3. Eigenvector and cumulative nomination centralities 96 5.2.4. Alpha centrality 97 5.2.5. PageRank centrality 98 5.2.6. Summary of the centrality measures as steady state, shifted and power series 99 5.3. Generalizations of centrality measures 99 5.3.1. Priors to centrality measures 99 5.3.2. Lazy variants of centrality measures 100 5.3.3. Lazy α-centrality 100 5.3.4. Lazy Katz centrality 102 5.3.5. Lazy cumulative nomination centrality 103 5.4. Experimental results 104 5.5. Discussion 106 5.6. Conclusion 109 5.7. Acknowledgments 109 5.8. References 110 Chapter 6. Error Detection in Sequential Laser Sensor Input 113Gwenael GATTO and Olympia HADJILIADIS 6.1. Introduction 113 6.2. Data description 114 6.3. Algorithms 116 6.3.1. Algorithm for consecutive changes in mean 118 6.3.2. Algorithm for burst detection 120 6.4. Results 125 6.5. Acknowledgments 127 6.6. References 127 Chapter 7. Diagnostics and Visualization of Point Process Models for Event Times on a Social Network 129Jing WU, Anna L. SMITH and Tian ZHENG 7.1. Introduction 129 7.2. Background 131 7.2.1. Univariate point processes 131 7.2.2. Network point processes 132 7.3. Model checking for time heterogeneity 134 7.3.1. Time rescaling theorem 134 7.3.2. Residual process 136 7.4. Model checking for network heterogeneity and structure 138 7.4.1. Kolmogorov–Smirnov test 138 7.4.2. Structure score based on the Pearson residual matrix 141 7.5. Summary 143 7.6. Acknowledgments 144 7.7. References 144 Part 2. Data Analysis Methods and Tools 147 Chapter 8. Exploring the Distribution of Conditional Quantile Estimates: An Application to Specific Costs of Pig Production in the European Union 149Dominique DESBOIS 8.1. Introduction 150 8.2. Conceptual framework and methodological aspects 150 8.2.1. The empirical model for estimating the specific production costs 151 8.2.2. The procedures for estimating and testing conditional quantiles 152 8.2.3. Symbolic PCA of the specific cost distributions 154 8.2.4. Symbolic clustering analysis of the specific cost distributions 162 8.3. Results 165 8.3.1. The SO-PCA of specific cost estimates 167 8.3.2. The divisive hierarchy of specific cost estimates 170 8.4. Conclusion 171 8.5. References 172 Chapter 9. Maximization Problem Subject to Constraint of Availability in Semi-Markov Model of Operation 175Franciszek GRABSKI 9.1. Introduction 175 9.2. Semi-Markov decision process 176 9.3. Semi-Markov decision model of operation 177 9.3.1. Description and assumptions 177 9.3.2. Model construction 177 9.4. Optimization problem 178 9.4.1. Linear programming method 179 9.5. Numerical example 182 9.6. Conclusion 184 9.7. References 185 Chapter 10. The Impact of Multicollinearity on Big Data Multivariate Analysis Modeling 187Kimon NTOTSIS and Alex KARAGRIGORIOU 10.1. Introduction 187 10.2. Multicollinearity 188 10.3. Dimension reduction techniques 191 10.3.1. Beale et al 192 10.3.2. Principal component analysis 192 10.4. Application 194 10.4.1. The modeling of PPE 194 10.4.2. Concluding remarks 200 10.5. Acknowledgments 200 10.6. References 200 Chapter 11. Weak Signals in High-Dimensional Poisson Regression Models 203Orawan REANGSEPHET, Supranee LISAWADI and Syed Ejaz AHMED 11.1. Introduction 203 11.2. Statistical background 204 11.3. Methodologies 205 11.3.1. Predictor screening methods 205 11.3.2. Post-screening parameter estimation methods 206 11.4. Numerical studies 208 11.4.1. Simulation settings and performance criteria 208 11.4.2. Results 209 11.5. Conclusion 217 11.6. Acknowledgments 218 11.7. References 218 Chapter 12. Groundwater Level Forecasting for Water Resource Management 221Andrea ZIRULIA, Alessio BARBAGLI and Enrico GUASTALDI 12.1. Introduction 221 12.2. Materials and methods 222 12.2.1. Study area 222 12.2.2. Forecast method 222 12.3. Results 224 12.4. Conclusion 230 12.5. References 230 Chapter 13. Phase I Non-parametric Control Charts for Individual Observations: A Selective Review and Some Results 233Christina PARPOULA 13.1. Introduction 234 13.1.1. Background 234 13.1.2. Univariate non-parametric process monitoring 235 13.2. Problem formulation 237 13.3. A comparative study 239 13.3.1. The existing methodologies 239 13.3.2. Simulation settings 240 13.3.3. Simulation-study results 242 13.4. Concluding remarks 247 13.5. References 247 Chapter 14. On Divergence and Dissimilarity Measures for Multiple Time Series 249Konstantinos MAKRIS, Alex KARAGRIGORIOU and Ilia VONTA 14.1. Introduction 249 14.2. Classical measures 250 14.3. Divergence measures 252 14.4. Dissimilarity measures for ordered data 254 14.4.1. Standard dissimilarity measures 254 14.4.2. Advanced dissimilarity measures 256 14.5. Conclusion 259 14.6. References 259 List of Authors 261 Index 265

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Book SynopsisNew Methods of Market Research and Analysis prepares readers for the new reality posed by big data and marketing analytics. While connecting to traditional research approaches such as surveys and focus groups, this book shows how new technologies and new analytical capabilities are rapidly changing the way marketers obtain and process their information. In particular, the prevalence of big data systems always monitoring key performance indicators, trends toward more research using observation or observation and communication together, new technologies such as mobile, apps, geo-locators, and others, as well as the deep analytics allowed by cheap data processing and storage are all covered and placed in context. Scott Erickson goes beyond the buzzwords to provide relevant explanations of the meaning and impact of both big data and analytics, placing them in context with traditional marketing research. His engaging subject matter focuses on the practical aspects of big data concepts, precisely defining and illustrating key concepts and providing illuminating real world examples. This approachable style enables marketers to understand what data scientists are doing with big data systems and analytics, giving them a taste of the capabilities of contemporary statistical software and its practical applications.This book can be used as a supplement to a traditional marketing research text or on its own. It will serve as a key reference for graduate students and advanced undergraduates in marketing research, marketing analytics, or business intelligence courses as well as marketing professionals looking to stay up to date with current trends and have them explained in a context they understand.Trade Review'This is a wonderfully well-written, highly readable, book that covers the rapidly changing and increasingly complex landscape of data-driven marketing in depth. The distance traveled from ''Mad Men-esque'' focus groups to sophisticated inferential analyses of vast data arrays is very well captured. Erickson's use of real-life, and very current, examples, to frame critical issues and explain key concepts and details is remarkable. The reader often feels as if he/she is virtual member of a marketing analytics workgroup working on problems for firms like Tesco, Bloomberg, Lego, LiveAnalytics, Amazon, and others.' --Charles R. Christian, former Director of Employee Analytics, Johnson & JohnsonTable of ContentsContents: 1. Big Data and Marketing Analytics 2. Exploratory Research Design 3. Descriptive Research Design 4. Causal Research Design 5. Other Topics in Research and Analytics 6. Analytics 1: Big Data 7. Analytics 2: Marketing Analytics Index

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Book SynopsisData has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills. Whether you're an entrepreneur wanting to boost your business, a jobseeker looking for that employable edge, or simply hoping to make the most of your current career, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analysing your data, to visualizing and communicating your insights. It now contains exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies including Amazon, LinkedIn and Mike's Hard Lemonade Co, as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills will help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path.Trade Review"The most comprehensive book I have seen for those wanting to get into data science - what Harvard Business Review called 'the sexiest job of the 21st century'." * Ben Taylor, Chief AI Evangelist, DataRobot *"Kirill Eremenko's book skilfully unravels the mysteries behind all the popular analytics tools and techniques, as well as many of the algorithms that power intelligent systems. I would recommend it to anyone who wants to pursue a career in data science. " * Dan Shiebler, Senior Machine Learning Engineer, Twitter Cortex *"Kirill Eremenko has come up with an amazing, unique way of making data science simple. From novices to the most experienced, anyone wanting to learn about data science will benefit from this book. Kirill covers everything from what data is and how to wrangle it, to helping you develop your own data analysis process, to effectively communicating with data. This book has it all! " * Andy Kriebel, Head Coach, The Information Lab Data School *"Eremenko is an established voice in the field, and his book is a must-read for anyone with an interest in using data science for business. Crammed with advice, Confident Data Skills provides the means to broaden one's horizons through data." * Michael Segala, CEO and Co-Founder, SFL Scientific *"Terrific. Eremenko has a knack for rendering complex theories in clear, elegant prose. Instructive and spirited, it will help you think - not only about the world around you but also about yourself." * Damian Mingle, Chief Data Scientist, Intermedix *Table of Contents Chapter - 00: Introduction; Section - ONE: "What is it?" key principles; Chapter - 01: Defining data; Chapter - 02: How data fulfils our needs; Chapter - 03: AI and our Future; Section - TWO: "When and where can I get it?" data gathering and analysis; Chapter - 04: Identify the problem; Chapter - 05: Data preparation; Chapter - 06: Data analysis (part I); Chapter - 07: Data analysis (part II); Section - THREE: "How can I present it?" communicating data; Chapter - 08: Data visualization; Chapter - 09: Data presentation; Chapter - 10: Your career in data science

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Book SynopsisWith 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.

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Book SynopsisSave time analyzing volumes of data using best practices to extract, model, and create insights from your dataKey Features Master best practices in data modeling with Tableau Prep Builder and Tableau Desktop Apply Tableau Server and Cloud to create and extend data models Build organizational data models based on data and content governance best practices Book DescriptionTableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google's Looker). That doesn't mean Tableau doesn't have enterprise data governance; it is both robust and very flexible. This book will help you build a data-driven organization with the proper use of Tableau governance models.Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you will learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You'll also explore the components of Tableau Server and Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you'll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will get into the entire Tableau stack and get the techniques required to build the right level of governance into Tableau data models for the right use cases.By the end of this Tableau book, you'll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.What you will learn Showcase Tableau published data sources and embedded connections Apply Ask Data in data cataloging and natural language query Exhibit features of Tableau Prep Builder with hands-on exercises Model data with Tableau Desktop through examples Formulate a governed data strategy using Tableau Server and Cloud Optimize data models for Ask and Explain Data Who this book is forThis book is for data analysts and business analysts who are looking to expand their data skills, offering a broad foundation to build better data models in Tableau for easier analysis and better query performance.It will also benefit individuals responsible for making trusted and secure data available to their organization through Tableau, such as data stewards and others who work to take enterprise data and make it more accessible to business analysts.Table of ContentsTable of Contents Introducing Data Modeling in Tableau Licensing Considerations and Types of Data Models Data Preparation with Tableau Prep Builder Data Modeling Functions with Tableau Prep Builder Advanced Modeling Functions in Tableau Prep Builder Data Output from Tableau Prep Builder Connecting to Data in Tableau Desktop Building Data Models Using Relationships Building Data Models at the Physical Level Sharing and Extending Tableau Data Models Securing Data Data Modeling Considerations for Ask Data and Explain Data Data Management with Tableau Prep Conductor Scheduling Extract Refreshes Data Modeling Strategies by Audience and Use Case

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Book SynopsisBig Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.Table of Contents Chapter 1: Big data analytics for security intelligence Chapter 2: Zero attraction data selective adaptive filtering algorithm for big data applications Chapter 3: Secure routing in software defined networking and Internet of Things for big data Chapter 4: Efficient ciphertext-policy attribute-based signcryption for secure big data storage in cloud Chapter 5: Privacy-preserving techniques in big data Chapter 6: Big data and behaviour analytics Chapter 7: Analyzing events for traffic prediction on IoT data streams in a smart city scenario Chapter 8: Gender-based classification on e-commerce big data Chapter 9: On recommender systems with big data Chapter 10: Analytics in e-commerce at scale Chapter 11: Big data regression via parallelized radial basis function neural network in Apache Spark Chapter 12: Visual sentiment analysis of bank customer complaints using parallel self-organizing maps Chapter 13: Wavelet neural network for big data analytics in banking via GPU Chapter 14: Stock market movement prediction using evolving spiking neural networks Chapter 15: Parallel hierarchical clustering of big text corpora Chapter 16: Contract-driven financial reporting: building automated analytics pipelines with algorithmic contracts, Big Data and Distributed Ledger technology Overall conclusions

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Book SynopsisBig Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.Table of Contents Chapter 1: The impact of Big Data on databases Chapter 2: Big data processing frameworks and architectures: a survey Chapter 3: The role of data lake in big data analytics: recent developments and challenges Chapter 4: Query optimization strategies for big data Chapter 5: Toward real-time data processing: an advanced approach in big data analytics Chapter 6: A survey on data stream analytics Chapter 7: Architectures of big data analytics: scaling out data mining algorithms using Hadoop-MapReduce and Spark Chapter 8: A review of fog and edge computing with big data analytics Chapter 9: Fog computing framework for Big Data processing using cluster management in a resource-constraint environment Chapter 10: Role of artificial intelligence and big data in accelerating accessibility for persons with disabilities Overall conclusions

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Book SynopsisThis book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data. Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency. The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach. This book is a popularized reference for students looking to work with spatialized data, but who do not have the advanced statistical theoretical basics.Table of ContentsACKNOWLEDGMENTS ix PREFACE xi CHAPTER 1. ECONOMETRICS AND SPATIAL DIMENSIONS 1 1.1. Introduction 1 1.2. The types of data 6 1.2.1. Cross-sectional data 7 1.2.2. Time series 8 1.2.3. Spatio-temporal data 9 1.3. Spatial econometrics 11 1.3.1. A picture is worth a thousand words 13 1.3.2. The structure of the databases of spatial microdata 15 1.4. History of spatial econometrics 16 1.5. Conclusion 21 CHAPTER 2. STRUCTURING SPATIAL RELATIONS 29 2.1. Introduction 29 2.2. The spatial representation of data 30 2.3. The distance matrix 34 2.4. Spatial weights matrices 37 2.4.1. Connectivity relations 40 2.4.2. Relations of inverse distance 42 2.4.3. Relations based on the inverse (or negative) exponential 45 2.4.4. Relations based on Gaussian transformation 47 2.4.5. The other spatial relation 47 2.4.6. One choice in particular? 48 2.4.7. To start 49 2.5. Standardization of the spatial weights matrix 50 2.6. Some examples 51 2.7. Advantages/disadvantages of micro-data 55 2.8. Conclusion 56 CHAPTER 3. SPATIAL AUTOCORRELATION 59 3.1. Introduction 59 3.2. Statistics of global spatial autocorrelation 65 3.2.1. Moran’s I statistic 68 3.2.2. Another way of testing significance 72 3.2.3. Advantages of Moran’s I statistic in modeling 74 3.2.4. Moran’s I for determining the optimal form of W 75 3.3. Local spatial autocorrelation 77 3.3.1. The LISA indices 79 3.4. Some numerical examples of the detection tests 86 3.5. Conclusion 89 CHAPTER 4. SPATIAL ECONOMETRIC MODELS 93 4.1. Introduction 93 4.2. Linear regression models 95 4.2.1. The different multiple linear regression model types 99 4.3. Link between spatial and temporal models 102 4.3.1. Temporal autoregressive models 103 4.3.2. Spatial autoregressive models 110 4.4. Spatial autocorrelation sources 115 4.4.1. Spatial externalities 117 4.4.2. Spillover effect 119 4.4.3. Omission of variables or spatial heterogeneity 123 4.4.4. Mixed effects 127 4.5. Statistical tests 129 4.5.1. LM tests in spatial econometrics 134 4.6. Conclusion 140 CHAPTER 5. SPATIO-TEMPORAL MODELING 145 5.1. Introduction 145 5.2. The impact of the two dimensions on the structure of the links: structuring of spatio-temporal links 148 5.3. Spatial representation of spatio-temporal data 150 5.4. Graphic representation of the spatial data generating processes pooled over time 154 5.5. Impacts on the shape of the weights matrix 159 5.6. The structuring of temporal links: a temporal weights matrix 162 5.7. Creation of spatio-temporal weights matrices 167 5.8. Applications of autocorrelation tests and of autoregressive models 170 5.9. Some spatio-temporal applications 172 5.10. Conclusion 173 CONCLUSION 177 GLOSSARY 185 APPENDIX 189 BIBLIOGRAPHY 215 INDEX 227

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Book SynopsisEt si on tentait d'ouvrir la boite noire du raisonnement grammatical des élèves ? que trouve-t-on dans les tiroirs des élèves, derrière les étiquettes noms , verbe ou encore déterminant ? ces termes sont-ils aussi intuitifs que la grammaire scolaire semble le croire ?S'inscrivant dans le champ des recherches concernant les représentations des élèves sur la langue, cette étude exploratoire utilise un dispositif didactique particulier, le tri de mots à visée grammaticale, afin de recueillir des données lors du suivi longitudinal d'une classe située en zone d'éducation prioritaire, sur une durée de deux années, en CE2 et CM1 (élèves âgés de 8 à 10 ans). Le corpus constitué par les traces écrites produites par les élèves a fait l'objet d'un traitement statistique afin d'en permettre la lisibilité. Les outils d'analyse linguistiques et didactiques utilisés ont permis de conforter des hypothèses déjà émises, mais aussi de dégager des tendances nouvelles concernant l'acquisition des classes grammaticales par les élèves de l'école élémentaire.Les résultats exposés portent sur les savoirs et savoir-faire des élèves, mais aussi sur l'éclairage que ces conceptions apportent sur les systèmes d'explication de la langue. Entre grammaire scolaire et linguistique, il s'agit de mieux comprendre le point de vue des élèves sur la langue afin d'ouvrir des pistes de réflexions didactiques.

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