Migration is a complex phenomenon with multi-dimensional factors. With ever-expanding data storage and processing capabilities, new data sources (such as social media, mobile call data records, and satellite imaging) are becoming available to study migration from both qualitative and quantitative perspectives. Data Science for Migration and Mobility addresses the needs of both migration scholars who stand to benefit from the analysis of these new sources but lack the computational tools, as well as data scientists who have practical and theoretical knowledge in dealing with these data sources but have no familiarity with the relevant questions of migration research. It describes the main conceptual frameworks, explains techniques of data collection and processing, provides case studies, discusses the strengths and limitations of each data source, and critically discusses the ethical, legal, and privacy-related issues specific to each data source.

This book showcases the different ways in which contemporary forms of data analysis are being used in urban planning and management. It highlights the emerging possibilities that city-regional governance, technology and data have for better planning and urban management - and discusses how you can apply them to your research. Including perspectives from across the globe, it's packed with examples of good practice and helps to demystify the process of using big and open data. Learn about different kinds of emergent data sources and how they are processed, visualised and presented. Understand how spatial analysis and GIS are used in city planning. See examples of how contemporary data analytics methods are being applied in a variety of contexts, such as 'smart' city management and megacities. Aimed at upper undergraduate and postgraduate students studying spatial analysis and planning, this timely text is the perfect companion to enable you to apply data analytics approaches in your research.

The last decade has witnessed various technological advances in life sciences, especially high throughput technologies. These technologies provide a way to perform parallel scientific studies in a very short period of time with low cost. High throughput techniques, mainly, next generation sequencing, microarray and mass spectrometry, have strengthened the omics vision in the last decades (study of complete system) and now resulted in well-developed branches of omics i.e., genomics, transcriptomics, proteomics and metabolomics, which deal with almost every level of central dogma of life. Practice of high throughput techniques throughout the world with different aims and objectives resulted in a voluminous data, which required computational applications, i.e., database, algorithm and software to store, process and get biological interpretation from primary raw data. Researchers from different fields are looking to analyze these raw data for different purposes, but lacking of proper information and knowledge in proper documented form creates different kinds of hurdles and raises the challenges. This book contains thirteen chapters that deal with different computational biology/bioinformatics resources and concepts which are already in practice by the scientific community or can be utilized to handle various aspects of different classes of omics data. It includes different computational concepts, algorithm, resources and recent trends belonging to the four major branches of omics (i.e., genomics, transcriptomics, proteomics and metabolomics), including integrative omics. It will help all scholars who are working in any branch of computational omics and bioinformatics field as well as those who would like to perform research at a systemic biology through computational approaches.

Edited by Terri D. Pigott, Ann Marie Ryan, and Charles Tocci, the purpose of this volume is to present high-quality reviews that examine change to teaching practice from a variety of perspectives and a range of disciplines with an eye toward the enormous scope of the field. Taken as a whole, this volume presents a compelling profile of the core challenges and opportunities facing those engaged in the work of changing teaching practice and those who research these efforts. Divided into four sections, the first section of this volume delves into the history and policy of changing teaching practice, the second set of chapters consider the capacity of teachers to make changes, the third set of chapters review literature examining how to change practice in numerous settings in various ways, and the final section of the volume centers on emerging issues for practice. This volume considers some of the most critical problems facing educators and scholars today: how our history shapes our present-day possibilities, how we develop the capacity of educators to change and improve practice, the innumerable aspects that can be changed, which dimensions of teaching should we prioritize, and what emerging issues will shape this work in the coming years?

This exciting new textbook offers an accessible, business-focused overview of the key theoretical concepts underpinning modern data analytics. It provides engaging and practical advice on using the key software tools, including SAS Visual Analytics, R and DataRobot, that are used in organisations to help make effective data-driven decisions. Combining theory with hands-on practical examples, this essential text includes cutting edge coverage of new areas of interest including social media analytics, design thinking and the ethical implications of using big data. A wealth of learning features including exercises, cases, online resources and data sets help students to develop analytic problem-solving skills. With its management perspective on analytics and its coverage of a range of popular software tools, this is an ideal essential text for upper-level undergraduate, postgraduate and MBA students. It is also ideal for practitioners wanting to understand the broader organisational context of big data analysis and to engage critically with the tools and techniques of business analytics. Accompanying online resources for this title can be found at bloomsburyonlineresources.com/business-analytics. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost.

You can't get anywhere in your statistics course without grasping statistical significance. it's often seen as difficult but is actually a straightforward concept everyone can-and should-understand. Do your results mean something-or not? How can you measure it? Breaking it down into three building blocks, this Little Quick Fix shows students how to master: hypothesis testing normal distribution p values Students will learn how to understand the concept and also how to explain it for maximum effect in their essays and lab reports. Good for results-this is also a secret weapon for critical thinking. Little Quick Fix titles provide quick but authoritative answers to the problems, hurdles, and assessment points students face in the research course, project proposal, or design-whatever their methods learning is. Lively, ultra-modern design; full-colour, each page a tailored design. An hour's read. Easy to dip in and out of with clear navigation enables the reader to find what she needs-quick. Direct written style gets to the point with clear language. Nothing needs to be read twice. No fluff. Learning is reinforced through a 2-minute overview summary; 3-second summaries with super-quick Q&A DIY tasks create a work plan to accomplish a task, do a self-check quiz, solve a problem, get students to what they need to show their supervisor. Checkpoints in each section make sure students are nailing it as they go and support self-directed learning. How do I know I'm done? Each Little Quick Fix wraps up with a final checklist that allows the reader to self-assess they've got what they need to progress, submit, or ace the test or task.

The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.

The vast volume of financial data that exists and the globalisation of financial markets create new challenges for researchers and practitioners in economics and finance. Computational data analysis techniques can contribute significantly within this context, by providing a rigorous analytic framework for decision-making and support, in areas such as financial times series analysis and forecasting, risk assessment, trading, asset management, and pricing. The aim of this edited volume is to present, in a unified context, some recent advances in the field, covering the theory, the methodologies, and the applications of computational data analysis methods in economics and finance. The volume consists of papers published in the fifth volume of the Journal of "Computational Optimization in Economics & Finance" (published by Nova Science Publishers). The contents of this volume cover a wide range of topics, including among others stock market applications, corporate finance, corporate performance, as well as macroeconomic issues.

Data Analytics and Data-based Decision-making are hot topics now. Big Data has entered the common parlance. Many kinds of data are generated by business, social media, machines, and more. Organizations have a choice: they can be buried under the avalanche of data, or they can do something with it to increase competitive advantage. A new field of Data Science is born, and Data Scientist has been called the sexiest job of the decade. Students across a variety of academic departments, including business, computer science, statistics, and engineering are attracted to the idea of discovering new insights and ideas from data. This is a proposal for a short and lucid book on this whole area. It is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms, all within a single semester- or quarter-long course.

Just Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way.

Written for anyone beginning a research project, this introductory book takes you through the process of analysing your data from start to finish. The author sets out an easy-to-use model for coding data in order to break it down into parts, and then to reassemble it to create a meaningful picture of the phenomenon under study. Full of useful advice, the book guides the reader through the last difficult integrating phase of qualitative analysis including diagramming, memoing, thinking aloud, and using one's feelings, and how to incorporate the use of software where appropriate. Ideal for third year undergraduate students, master students, postgraduates and anybody beginning a research project, the book includes examples covering a wide range of subjects - making the book useful for students across the social science disciplines. Hennie Boeije is currently an Associate Professor with the Department of Methodology and Statistics of the Faculty of Social and Behavioural Sciences at Utrecht University, The Netherlands.

"Using Web and Paper Questionnaires for Data-Based Decision Making maintains the same strengths as Thomas?s previous book: it is clearly written, easy to understand, and has plenty of examples and guides for those implementing these ideas. Designed as a cookbook, it superbly enables educators to write, administer, and analyze a survey."

Sandra L. Stein, Professor of Education

Rider University

How effectively are teachers implementing the new literacy program? What do parents think of the proposed homework policy? Is bullying a growing problem? Understanding how to create appropriate questionnaires is essential in making data-based decisions that improve school policies, processes, and procedures.

Using Web and Paper Questionnaires for Data-Based Decision Making is a practical handbook for creating exceptional questionnaires for a variety of purposes, including data-based decision making. Author Susan J. Thomas provides authoritative guidance for planning a survey project, creating a questionnaire, gathering data, and analyzing and communicating the results to a variety of audiences.

Features of this reader-friendly guidebook include

• Sample cover letters, invitations, parental permission forms, and planning templates
• Extensive examples, activities, and checklists
• Web-based questionnaire guidelines
• A step-by-step case study of a successful survey project
• Appendices with further resources and recommended readings

Offering suggestions for successfully using both Web-based and paper-based questionnaires, this practitioner-focused manual summarizes the key steps of successful survey projects and identifies critical success factors for each step.

Designed primarily for principals, district-level administrators, and teachers, this invaluable resource is also suitable for policymakers, state-level administrators, and graduate students in education and social sciences.

"Using Web and Paper Questionnaires for Data-Based Decision Making maintains the same strengths as Thomas?s previous book: it is clearly written, easy to understand, and has plenty of examples and guides for those implementing these ideas. Designed as a cookbook, it superbly enables educators to write, administer, and analyze a survey."

Sandra L. Stein, Professor of Education

Rider University

How effectively are teachers implementing the new literacy program? What do parents think of the proposed homework policy? Is bullying a growing problem? Understanding how to create appropriate questionnaires is essential in making data-based decisions that improve school policies, processes, and procedures.

Using Web and Paper Questionnaires for Data-Based Decision Making is a practical handbook for creating exceptional questionnaires for a variety of purposes, including data-based decision making. Author Susan J. Thomas provides authoritative guidance for planning a survey project, creating a questionnaire, gathering data, and analyzing and communicating the results to a variety of audiences.

Features of this reader-friendly guidebook include

• Sample cover letters, invitations, parental permission forms, and planning templates
• Extensive examples, activities, and checklists
• Web-based questionnaire guidelines
• A step-by-step case study of a successful survey project
• Appendices with further resources and recommended readings

Offering suggestions for successfully using both Web-based and paper-based questionnaires, this practitioner-focused manual summarizes the key steps of successful survey projects and identifies critical success factors for each step.

Designed primarily for principals, district-level administrators, and teachers, this invaluable resource is also suitable for policymakers, state-level administrators, and graduate students in education and social sciences.

"The authors discuss self-administered questionnaires, the content and format of the questionnaire, "user-friendly" questionnaires and response categories, and survey implementation. They offer excellent checklists for deciding whether or not to use a mail questionnaire, for constructing questions and response categories, for minimizing bias, for writing questionnaire specifications, for formatting and finalizing questionnaires, and for motivating respondents and writing cover letters."
--Peter Hernon, Graduate School of Library and Information Science, Simmons College

This accessible introduction to the theory and practice of longitudinal research takes the reader through the strengths and weaknesses of this kind of research, making clear: how to design a longitudinal study; how to collect data most effectively; how to make the best use of statistical techniques; and how to interpret results. Although the book provides a broad overview of the field, the focus is always on the practical issues arising out of longitudinal research. This book supplies the student with all that they need to get started and acts as a manual for dealing with opportunities and pitfalls. It is the ideal primer for this growing area of social research.

What is the most effective way for a researcher to read and analyze life-story materials? Using a new model for the classification of types of readings, Narrative Research shows readers how to read, analyze and interpret life story materials. The authors first introduce readers to four models of reading and interpreting a text: the holistic-content reading, the holistic-form reading, categorical-content reading, and categorical-form reading. They next present two complete narratives so that readers can compare the author?s interpretations against the actual text as well as try to analyze the stories on their own. The subsequent chapters provide readings, interpretations, and analyses of the narrative data from the four models. Each of the examples is highlighted by sharing with the reader some of the processes of the researcher? work, including various considerations, doubts, self-criticism, and dialogues about the narratives. Readers wanting to learn more about life story research will not only learn the tools for doing this analysis, but will be inspired to contribute their own creativity to this developing field.

What is the most effective way for a researcher to read and analyze life-story materials? Using a new model for the classification of types of readings, Narrative Research shows readers how to read, analyze and interpret life story materials. The authors first introduce readers to four models of reading and interpreting a text: the holistic-content reading, the holistic-form reading, categorical-content reading, and categorical-form reading. They next present two complete narratives so that readers can compare the author?s interpretations against the actual text as well as try to analyze the stories on their own. The subsequent chapters provide readings, interpretations, and analyses of the narrative data from the four models. Each of the examples is highlighted by sharing with the reader some of the processes of the researcher? work, including various considerations, doubts, self-criticism, and dialogues about the narratives. Readers wanting to learn more about life story research will not only learn the tools for doing this analysis, but will be inspired to contribute their own creativity to this developing field.

This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Key features * Data science and statistical analysis for the business professional * Numerous instantly familiar real-world use cases * Keys to effective data presentations * Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysis Audience While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science. About the technology Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day Nina Zumel and John Mount are co-founders of Win-Vector LLC, a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.

This is the ideal book to get you up and running with the basics of qualitative data analysis. It breaks everything down into a series of simple steps and introduces the practical tools and techniques you need to turn your transcripts into meaningful research. Using multidisciplinary data from interviews and focus groups Jamie Harding provides clear guidance on how to apply key research skills such as making summaries, identifying similarities, drawing comparisons and using codes. The book sets out real world applicable advice, provides easy to follow best practice and helps you to: * Manage and sort your data * Find your argument and define your conclusions * Answer your research question * Write up your research for assessment and dissemination Clear, pragmatic and honest this book will give you the perfect framework to start understanding your qualitative data and to finish your research project.

Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.

This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners' experiences have taken time to explain their methods, describe examples, and point out new underpinnings for the field. Together, they show how this new field has the potential to dramatically increase learner success through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. Learning analytics is much more than "analyzing learning data"-it is about deeply understanding what learning activities work well, for whom, and when. Learning Analytics in Education provides an essential framework, as well as guidance and examples, for a wide range of professionals interested in the future of learning. If you are already involved in learning analytics, or otherwise trying to use an increasing density of evidence to understand learners' progress, these leading thinkers in the field may give you new insights. If you are engaged in teaching at any level, or training future teachers/faculty for this new, increasingly technology-enhanced learning world, and want some sense of the potential opportunities (and pitfalls) of what technology can bring to your teaching and students, these forward-thinking leaders can spark your imagination. If you are involved in research around uses of technology, improving learning measurements, better ways to use evidence to improve learning, or in more deeply understanding human learning itself, you will find additional ideas and insights from some of the best thinkers in the field here. If you are involved in making administrative or policy decisions about learning, you will find new ideas (and dilemmas) coming your way from inevitable changes in how we design and deliver instruction, how we measure the outcomes, and how we provide feedback to students, teachers, developers, administrators, and policy-makers. For all these players, the trick will be to get the most out of all the new developments to efficiently and effectively improve learning performance, without getting distracted by "shiny" technologies that are disconnected from how human learning and development actually work.

A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques.

According to a wry saying among radiologists, finding a tumour in a mammogram is like finding a snowball in a blizzard. Up to thirty percent of breast-cancer diagnoses are given to those who have no cancer at all. Medicine is subject to far more uncertainty than we commonly acknowledge. While it is portrayed a science, it can sometimes be scarily close to educated guesswork. Covering everything from the efficacy of Prozac to the regular barrage of health advice by the media, Snowball in a Blizzard is a profound meditation on why it's essential that doctors and their patients know what we don't know. The world is more complicated than we like to believe. Informed by years of frontline medical experience and filled with personal reflections, this important book is filled with counter-intuitive revelations about flawed reasoning, helpful guidance and hard-earned insight. It will change the way you view the health of yourself, your loved ones or your patients.