This book presents and develops the deep data analytics for providing the information needed for successful new product development. Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs.

Corpora are ubiquitous in linguistic research, yet to date, there has been no consensus on how to conceptualize corpus representativeness and collect corpus samples. This pioneering book bridges this gap by introducing a conceptual and methodological framework for corpus design and representativeness. Written by experts in the field, it shows how corpora can be designed and built in a way that is both optimally suited to specific research agendas, and adequately representative of the types of language use in question. It considers questions such as 'what types of texts should be included in the corpus?', and 'how many texts are required?' - highlighting that the degree of representativeness rests on the dual pillars of domain considerations and distribution considerations. The authors introduce, explain, and illustrate all aspects of this corpus representativeness framework in a step-by-step fashion, using examples and activities to help readers develop practical skills in corpus design and evaluation.

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candes.

Every country, every subnational government, and every district has a designated population, and this has a bearing on politics in ways most citizens and policymakers are barely aware of. Population and Politics provides a comprehensive evaluation of the political implications stemming from the size of a political unit - on social cohesion, the number of representatives, overall representativeness, particularism ('pork'), citizen engagement and participation, political trust, electoral contestation, leadership succession, professionalism in government, power concentration in the central apparatus of the state, government intervention, civil conflict, and overall political power. A multimethod approach combines field research in small states and islands with cross-country and within-country data analysis. Population and Politics will be of interest to academics, policymakers, and anyone concerned with decentralization and multilevel governance.

This book is for practitioners and academics who have learned the conventions and rules of data modelling and are looking for a deeper understanding of the discipline. The coverage of theory includes a detailed review of the extensive literature on data modelling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice. The practice component incorporates the largest-ever study of data modelling practitioners, involving over 450 participants in interviews, surveys and data modelling tasks. The results challenge many long-standing held assumptions about data modelling and will be of interest to academics and practitioners alike. Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his "Data Modelling Essentials" a classic in the field. He begins with a question about the nature of data modelling (design or description), and uses it to illuminate such issues as the definition of data modelling, its philosophical underpinnings, inputs and deliverables, the necessary behaviours and skills, the role of creativity, product diversity, quality measures, personal styles, and the differences between experts and novices. "Data Modeling: Theory and Practice" is essential reading for anyone involved in data modelling practice, research, or teaching.

This book gives an up-to-date overview of methodological and data-analytical issues of cross-cultural studies. Written by leading experts in the field, it presents the most important tools for doing cross-cultural research and outlines design considerations, methods, and analytical techniques that can improve ecological validity and help researchers to avoid pitfalls in cross-cultural psychology. By focusing on the relevant research questions that can be tackled with particular methods, it provides practical guidance on how to translate conceptual questions into decisions on study design and statistical techniques. Featuring examples from cognitive and educational assessment, personality, health, and intercultural communication and management, and illustrating key techniques in feature boxes, this concise and accessible guide is essential reading for researchers, graduate students, and professionals who work with culture-comparative data.

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as "What is the distance to the border of the protected area?", "Which points are located close to a road?", "Which fraction of land cover types exist in my study area?" using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

Whilst a great deal of progress has been made in recent decades, concerns persist about the course of the social sciences. Progress in these disciplines is hard to assess and core scientific goals such as discovery, transparency, reproducibility, and cumulation remain frustratingly out of reach. Despite having technical acumen and an array tools at their disposal, today's social scientists may be only slightly better equipped to vanquish error and construct an edifice of truth than their forbears - who conducted analyses with slide rules and wrote up results with typewriters. This volume considers the challenges facing the social sciences, as well as possible solutions. In doing so, we adopt a systemic view of the subject matter. What are the rules and norms governing behavior in the social sciences? What kinds of research, and which sorts of researcher, succeed and fail under the current system? In what ways does this incentive structure serve, or subvert, the goal of scientific progress?

The term "smart city" defines the new urban environment, one that is designed for performance through information and communication technologies. Given that the majority of people across the world will live in urban environments within the next few decades, it's not surprising that massive effort and investment is being placed into efforts to develop strategies and plans for achieving "smart" urban growth. Building Smart Cities: Analytics, ICT, and Design Thinking explains the technology and a methodology known as design thinking for building smart cities. Information and communications technologies form the backbone of smart cities. A comprehensive and robust data analytics program enables the right choices to be made in building these cities. Design thinking helps to create smart cities that are both livable and able to evolve. This book examines all of these components in the context of smart city development and shows how to use them in an integrated manner. Using the principles of design thinking to reframe the problems of the smart city and capture the real needs of people living in a highly efficient urban environment, the book helps city planners and technologists through the following: Presentation of the relevant technologies required for coordinated, efficient cities Exploration of the latent needs of community stakeholders in a culturally appropriate context Discussion of the tested approaches to ideation, design, prototyping, and building or retrofitting smart cities Proposal of a model for a viable smart city project The smart city vision that we can create an optimized society through technology is hypothetical at best and reflects the failed repetition through the ages of equating scientific progress with positive social change. Up until now, despite our best hopes and efforts, technology has yet to bring an end to scarcity or suffering. Technical innovation, instead, can and should be directed in the service of our shared cultural values, especially within the rapidly growing urban milieu. In Building Smart Cities: Analytics, ICT, and Design Thinking, the author discusses the need to focus on creating human-centered approaches to our cities that integrate our human needs and technology to meet our economic, environmental, and existential needs. The book shows how this approach can lead to innovative, livable urban environments that are realizable, practical, and economically and environmentally sustainable.

*What do advanced statistical techniques do? *When is it appropriate to use them? *How are they carried out and reported? There are a variety of statistical techniques used to analyse quantitative data that masters students, advanced undergraduates and researchers in the social sciences are expected to be able to understand and undertake. This book explains these techniques, when it is appropriate to use them, how to carry them out and how to write up the results. Most books which describe these techniques do so at too advanced or technical a level to be readily understood by many students who need to use them. In contrast the following features characterise this book: - concise and accessible introduction to calculating and interpreting advanced statistical techniques - use of a small data set of simple numbers specifically designed to illustrate the nature and manual calculation of the most important statistics in each technique - succinct illustration of writing up the results of these analyses - minimum of mathematical, statistical and technical notation - annotated bibliography and glossary of key concepts Commonly used software is introduced, and instructions are presented for carrying out analyses and interpreting the output using the computer programs of SPSS Release 11 for Windows and a version of LISREL 8.51, which is freely available online. Designed as a textbook for postgraduate and advanced undergraduate courses across the socio-behavioural sciences, this book will also serve as a personal reference for researchers in disciplines such as sociology and psychology.

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

A lively, practical, first-of-its-kind guide to understanding cold, clinical data and harnessing it to tell a persuasive story. __________ How many hours' worth of songs are on your Spotify Wrapped this year? How much is your commute time really worth? How do you work out how likely you are to get Covid based on the official statistics? How do your viewing hours track against the most popular shows on Netflix? Whether you're interested in global problems like climate change, running a business, or just grasping how few people have washed their hands between visiting the bathroom and touching you, this book will help math-lovers and math-haters alike translate the numbers that illuminate our world. Until very recently, most languages had no words for numbers greater than five - anything from six to infinity was known as 'lots'. While the numbers in our world have become increasingly complex, our brains are stuck in the past. Yet the ability to communicate and understand numbers has never mattered more. How can we more effectively translate numbers and stats - so fundamental to the next big idea - to make data come to life? Drawing on years of research into making ideas stick, Chip Heath and Karla Starr outline six critical principles that will give anyone the tools to communicate numbers with more transparency and meaning. Using concepts such as simplicity, concreteness and familiarity, they show us how to transform hard numbers into their most engaging form, allowing us to bring more data, more naturally, into decisions in our schools, our workplaces and our society.

Single system, or single case, design studies are a convenient method for evaluating practice, allowing professionals to track clients' response to treatment and change over time. They also allow researchers to gather data where it might be difficult to conduct a study involving treatment and control groups; in a school setting, or a community mental health agency, for example, random assignment may be impossible, whereas individual student or client progress across time can be more easily monitored.
This pocket guide reviews a wide range of techniques for analyzing single system design data, including visual analysis methods, graphical methods, and statistical methods. From basic visual observation to complex ARIMA statistical models for use with interrupted time series designs, numerous data analysis methods are described and illustrated in this unique and handy book. The author frankly describes limitations and strengths of the data analysis methods so that readers can select an appropriate method and use the results responsibly in order to improve practice and client well-being.
This accessible yet in-depth introduction will serve as a highly practical resource for doctoral students and researchers alike.

Humberto Barreto gives professors a simple way to teach fundamental concepts for any undergraduate macroeconomics course using Microsoft Excel (R) with Excel workbooks and add-ins and videos freely available on his university website. The Excel files are designed to be used by students with any textbook, and have been used many times by the author in his own teaching. Each Excel workbook contains links to short screencasts, around five to ten minutes, that show the cursor and typing as the file is manipulated with narration that walks the student through the steps needed to complete a task. The book shows professors a simple way to present macroeconomic models and incorporate data into their courses.

Is college worth the cost? Should I worry about arsenic in my rice? Can we recycle pollution? Real questions of personal finance, public health, and social policy require sober, data-driven analyses. This unique text provides students with the tools of quantitative reasoning to answer such questions. The text models how to clarify the question, recognize and avoid bias, isolate relevant factors, gather data, and construct numerical analyses for interpretation. Themes and techniques are repeated across chapters, with a progression in mathematical sophistication over the course of the book, which helps the student get comfortable with the process of thinking in numbers. This textbook includes references to source materials and suggested further reading, making it user-friendly for motivated undergraduate students. The many detailed problems and worked solutions in the text and extensive appendices help the reader learn mathematical areas such as algebra, functions, graphs, and probability. End-of-chapter problem material provides practice for students, and suggested projects are provided with each chapter. A solutions manual is available online for instructors.

When would a casino stop a gambler from playing his next hand? How could a company use statistical analysis to blackball you from the job you want? Why should you worry when customer services pay attention to your needs? Beginning with examples of the mathematician who out-predicted wine buffs in determining the best vintages, and the sports scouts who now use statistics rather than intuition to pick winners, Super Crunchers exposes the hidden patterns all around us. No businessperson, academic, student, or consumer (statistically that's everyone) should make another move without getting to grips with thinking-by-numbers - the new way to be smart, savvy and statistically superior.

Drawing on his extensive experience of qualitative research, Douglas Ezzy reviews approaches to data analysis in established research traditions including ethnography, phenomenology and symbolic interactionism, alongside the newer approaches informed by cultural studies and feminism. He explains the difference between inductive, deductive and abductive theory building, provides a guide to computer-assisted analysis and outlines techniques such as journal writing, team meetings and participant reviews.

This is a fundamental reference work for any scientist contemplating using AI for data analysis.

This important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics (medical statistics). This new edition contains an additional two chapters. The first of these discusses fully parametric models for discrete repeated measures data. The second explores statistical models for time-dependent predictors where there may be feedback between the predictor and response variables.

Data Science and Analytics explores the solutions to problems in society, environment and in industry. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. This book explores perspectives on how big data and business analytics are increasingly essential in better decision making. This edited work explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. Data Science and Analytics brings together researchers, engineers and practitioners to encompass a wide and diverse range of topics in a wide range of fields. The book will provide unique insights to researchers, academics and data scientists from a variety of disciplines interested in analyzing and application of big data analytics, as well as data analysts, students and scholars pursuing advanced study in big data.

This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst's time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

"This comprehensive, practical, user-friendly book provides a wealth of data analysis strategies that are essential for any qualitative research. It is a must-have tool book for moving from data analysis to writing for publication!" -Guofang Li, University of British Columbia, Canada Miles, Huberman, and Saldana's Qualitative Data Analysis: A Methods Sourcebook is the authoritative text for analyzing and displaying qualitative research data. The Fourth Edition maintains the analytic rigor of previous editions while showcasing a variety of new visual display models for qualitative inquiry. Graphics are added to the now-classic matrix and network illustrations of the original co-authors. Five chapters have been substantially revised, and the appendix's annotated bibliography includes new titles in research methods. Graduate students and established scholars from all disciplines will find this resource an innovative compendium of ideas for the representation and presentation of qualitative data. As the authors demonstrate, when researchers "think display," their analyses of social life capture the complex and vivid processes of the people and institutions studied.

This book offers an introduction and hands-on examples that demonstrate how Learning Analytics (LA) can be used to enhance digital learning, teaching and training at various levels. While the majority of existing literature on the subject focuses on its application at large corporations, this book develops and showcases approaches that bring LA closer to smaller organizations, and to educational institutions that lack sufficient resources to implement a full-fledged LA infrastructure. In closing, the book introduces a set of software tools for data analytics and visualization, and explains how they can be employed in several LA scenarios.

This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.