For increasingly data-savvy clients, lawyers can no longer give "it depends" answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions. Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as: Mining legal data Computational law Uncovering bias through the use of Big Data Quantifying the quality of legal services Data mining and decision-making Contract analytics and contract standards In addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable. Data-driven decision-making requires firms to think differently about their workflow. Most firms warehouse their files, never to be seen again after the matter closes. Running a data-driven firm requires lawyers and their teams to treat information about the work as part of the service, and to collect, standardize, and analyze matter data from cradle to grave. More than anything, using data in a law practice requires a different mindset about the value of this information. This book helps legal professionals to develop this data-driven mindset.

Data 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.

This book includes high-quality papers presented at the Second International Conference on Data Science and Management (ICDSM 2021), organized by the Gandhi Institute for Education and Technology, Bhubaneswar, from 19 to 20 February 2021. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.

This volume focuses on the ethics of internet and social networking research exploring the challenges faced by researchers making use of social media and big data in their research. The internet, the world wide web and social media - indeed all forms of online communications - are attractive fields of research across a range of disciplines. They offer opportunities for methodological initiatives and innovations in research and easily accessed, massive amounts of primary and secondary data sources. This collection examines the new challenges posed by data generated online, explores how researchers are addressing those ethical challenges, and provides rich case studies of ethical decision making in the digital age.

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.

Learn how to get better answers in map analysis when you use spatial measurements and statistics. Spatial measurements and statistics give you a powerful way to analyze geospatial data, but you don't need to understand complex mathematical theories to apply statistical tools and get meaningful results in your projects. The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, second edition, builds on Volume 1 by taking you to the next step of GIS analysis. Learn to answer such questions as, how are features distributed? What is the pattern created by a set of features? Where can clusters be found? This book introduces readers to basic statistical concepts and some of the most common spatial statistics tasks: measuring distributions, identifying patterns and clusters, and analyzing relationships. Updated with the latest and most useful software tools and revised explanations, each chapter in The Esri Guide to GIS Analysis, Volume 2 is organized to answer basic questions about the topic. Explore how spatial statistical tools can be applied in a range of disciplines, from public health to habitat conservation. Learn how to quantify patterns beyond visualizing them in maps. Examine spatial clusters through an updated chapter on identifying clusters. Use The Esri Guide to GIS Analysis, Volume 2, second edition, to understand the statistical methods and tools that can move your work past mapping and visualization to more quantitative statistical assessment.

Praise forEnvisioning the Survey Interview of the Future

"This book is an excellent introduction to some brave new technologies . . . and their possible impacts on the way surveys might be conducted. Anyone interested in the future of survey methodology should read this book."

Norman M. Bradburn, PhD, National Opinion Research Center, University of Chicago

"Envisioning the Survey Interview of the Future gathers some of the brightest minds in alternative methods of gathering self-report data, with an eye toward the future self-report sample survey. Conrad and Schober, by assembling a group of talented survey researchers and creative inventors of new software-based tools to gather information from human subjects, have created a volume of importance to all interested in imagining future ways of interviewing."

Robert M. Groves, PhD, Survey Research Center, University of Michigan

This collaboration provides extensive insight into the impact of communication technology on survey research

As previously unimaginable communication technologies rapidly become commonplace, survey researchers are presented with both opportunities and obstacles when collecting and interpreting data based on human response. Envisioning the Survey Interview of the Future explores the increasing influence of emerging technologies on the data collection process and, in particular, self-report data collection in interviews, providing the key principles for using these new modes of communication.

With contributions written by leading researchers in the fields of survey methodology and communication technology, this compilation integrates the use of modern technological developments with establishedsocial science theory. The book familiarizes readers with these new modes of communication by discussing the challenges to accuracy, legitimacy, and confidentiality that researchers must anticipate while collecting data, and it also provides tools for adopting new technologies in order to obtain high-quality results with minimal error or bias.

Envisioning the Survey Interview of the Future addresses questions that researchers in survey methodology and communication technology must consider, such as:

How and when should new communication technology be adopted in the interview process?

What are the principles that extend beyond particular technologies?

Why do respondents answer questions from a computer differently than questions from a human interviewer?

How can systems adapt to respondents' thinking and feeling?

What new ethical concerns about privacy and confidentiality are raised from using new communication technologies?

With its multidisciplinary approach, extensive discussion of existing and future technologies, and practical guidelines for adopting new technology, Envisioning the Survey Interview of the Future is an essential resource for survey methodologists, questionnaire designers, and communication technologists in any field that conducts survey research. It also serves as an excellent supplement for courses in research methods at the upper-undergraduate or graduate level.

This book constitutes selected and revised papers from the First Mediterranean Forum - Data Science Conference, MeFDATA 2020, held in Sarajevo, Bosnia and Herzegovina, in October 2020. The 11 papers presented were carefully reviewed and selected from the 26 qualified submissions. The papers are organized in the topical sections on human behaviour and pandemic; applications in medicine; industrial applications; natural language processing.

Fundamentals of Big Data Network Analysis for Research and Industry Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea Presents the methodology of big data analysis using examples from research and industry There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets. Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail. Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis. This book : Explains the basic concepts in understanding big data and filtering meaningful data Presents big data analysis within the networking perspective Features methodology applicable to research and industry Describes in detail the social relationship between big data and its implications Provides insight into identifying patterns and relationships between seemingly unrelated big data Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

Making sense of sports performance data can be a challenging task but is nevertheless an essential part of performance analysis investigations. Focusing on techniques used in the analysis of sport performance, this book introduces the fundamental principles of data analysis, explores the most important tools used in data analysis, and offers guidance on the presentation of results.

The book covers key topics such as:

• The purpose of data analysis, from statistical analysis to algorithmic processing
• Commercial packages for performance and data analysis, including Focus, Sportscode, Dartfish, Prozone, Excel, SPSS and Matlab
• Effective use of statistical procedures in sport performance analysis
• Analysing data from manual notation systems, player tracking systems and computerized match analysis systems
• Creating visually appealing dashboard interfaces for presenting data
• Assessing reliability.

The book includes worked examples from real sport, offering clear guidance to the reader and bringing the subject to life. This book is invaluable reading for any student, researcher or analyst working in sport performance or undertaking a sport-related research project or methods course"

"Data Analysis and Visualization in Genomics and Proteomics" is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approachesThis book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scalePlaces emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems

Recent years have seen a sharp increase in the application of sophisticated statistical modelling methods to sample survey data. Analysis of Survey Data aims to provide a solid basis for the statistical theory underpinning these applications. This book brings together two key statistical traditions, statistical modelling - such as regression analysis - and sample survey methods, as used for sample design and estimation.

• Provides broad coverage of statistical methodology used in the analysis of survey data.

• Discusses the theoretical foundations of this methodology from a range of perspectives.

• Covers of a wide range of techniques, including categorical data analysis, generalized linear models, and longitudinal data analysis.

• Addresses issues of complex sampling and incomplete data.

• Features examples showing how methods are used in practice.

• Each chapter is written by a leading expert in the field.

• Includes an up-to-date bibliography.

Analysis of Survey Data is aimed primarily at statisticians interested in methods of analysing sample survey data. It should also provide a useful reference to many other survey data analysts in the social sciences and in the public and private sectors.

This comprehensive book, rich with applications, offers a quantitative framework for the analysis of the various capture-recapture models for open animal populations, while also addressing associated computational methods. The state of our wildlife populations provides a litmus test for the state of our environment, especially in light of global warming and the increasing pollution of our land, seas, and air. In addition to monitoring our food resources such as fisheries, we need to protect endangered species from the effects of human activities (e.g. rhinos, whales, or encroachments on the habitat of orangutans). Pests must be be controlled, whether insects or viruses, and we need to cope with growing feral populations such as opossums, rabbits, and pigs. Accordingly, we need to obtain information about a given population's dynamics, concerning e.g. mortality, birth, growth, breeding, sex, and migration, and determine whether the respective population is increasing , static, or declining. There are many methods for obtaining population information, but the most useful (and most work-intensive) is generically known as "capture-recapture," where we mark or tag a representative sample of individuals from the population and follow that sample over time using recaptures, resightings, or dead recoveries. Marks can be natural, such as stripes, fin profiles, and even DNA; or artificial, such as spots on insects. Attached tags can, for example, be simple bands or streamers, or more sophisticated variants such as radio and sonic transmitters. To estimate population parameters, sophisticated and complex mathematical models have been devised on the basis of recapture information and computer packages. This book addresses the analysis of such models. It is primarily intended for ecologists and wildlife managers who wish to apply the methods to the types of problems discussed above, though it will also benefit researchers and graduate students in ecology. Familiarity with basic statistical concepts is essential.

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.

Focus on the most important and most often overlooked factor in a successful Tableau project-data. Without a reliable data source, you will not achieve the results you hope for in Tableau. This book does more than teach the mechanics of data preparation. It teaches you: how to look at data in a new way, to recognize the most common issues that hinder analytics, and how to mitigate those factors one by one. Tableau can change the course of business, but the old adage of "garbage in, garbage out" is the hard truth that hides behind every Tableau sales pitch. That amazing sales demo does not work as well with bad data. The unfortunate reality is that almost all data starts out in a less-than-perfect state. Data prep is hard. Traditionally, we were forced into the world of the database where complex ETL (Extract, Transform, Load) operations created by the data team did all the heavy lifting for us. Fortunately, we have moved past those days. With the introduction of the Tableau Data Prep tool you can now handle most of the common Data Prep and cleanup tasks on your own, at your desk, and without the help of the data team. This essential book will guide you through: The layout and important parts of the Tableau Data Prep tool Connecting to data Data quality and consistency The shape of the data. Is the data oriented in columns or rows? How to decide? Why does it matter? What is the level of detail in the source data? Why is that important? Combining source data to bring in more fields and rows Saving the data flow and the results of our data prep work Common cleanup and setup tasks in Tableau Desktop What You Will Learn Recognize data sources that are good candidates for analytics in Tableau Connect to local, server, and cloud-based data sources Profile data to better understand its content and structure Rename fields, adjust data types, group data points, and aggregate numeric data Pivot data Join data from local, server, and cloud-based sources for unified analytics Review the steps and results of each phase of the Data Prep process Output new data sources that can be reviewed in Tableau or any other analytics tool Who This Book Is For Tableau Desktop users who want to: connect to data, profile the data to identify common issues, clean up those issues, join to additional data sources, and save the newly cleaned, joined data so that it can be used more effectively in Tableau

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.

Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. * In-depth discussion of data analysis for scientists and engineers * Coverage of both frequentist and Bayesian approaches to data analysis * Extensive look at analysis techniques for time-series data and images * Detailed exploration of linear and nonlinear modeling of data * Emphasis on error analysis * Instructor's manual (available only to professors)

A nuts-and-bolts guide to conducting your own professional-quality surveys without paying professional fees. How can you gauge public support for a cause or test the market for a product or service? What are the best methods for validating opinions for use in a paper or dissertation? A well-documented survey is the answer. But what if you don't have thousands of dollars to commission one? No problem. How to Conduct Your Own Survey gives you everything you need to do it yourself! Without any prior training, you can learn expert techniques for conducting accurate, low-cost surveys. In step-by-step, down-to-earth language, Priscilla Salant and Don A. Dillman give you the tools you need to:
* Determine which type of survey is best for you
* Estimate the cost of your survey
* Conduct mail, telephone, and face-to-face surveys
* Draw accurate samples
* Write effective questionnaires
* Compile and report results
* Avoid common survey errors
* Find reliable outside assistance
* And much more

"Modeling with Data" fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

"Modeling with Data" will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the product's intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career. Who This Book Is For Early-career data scientists, managers of data scientists, and those interested in entering the field of data science

The entertainment industry has long been dominated by legendary screenwriter William Goldman's "Nobody-Knows-Anything" mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage - the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney's recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to "Nobody-Knows" decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in the entertainment industry. Entertainment Science is poised to inspire fresh new thinking among managers, students of entertainment, and scholars alike. Thorsten Hennig-Thurau and Mark B. Houston - two of our finest scholars in the area of entertainment marketing - have produced a definitive research-based compendium that cuts across various branches of the arts to explain the phenomena that provide consumption experiences to capture the hearts and minds of audiences. Morris B. Holbrook, W. T. Dillard Professor Emeritus of Marketing, Columbia University Entertainment Science is a must-read for everyone working in the entertainment industry today, where the impact of digital and the use of big data can't be ignored anymore. Hennig-Thurau and Houston are the scientific frontrunners of knowledge that the industry urgently needs. Michael Koelmel, media entrepreneur and Honorary Professor of Media Economics at University of Leipzig Entertainment Science's winning combination of creativity, theory, and data analytics offers managers in the creative industries and beyond a novel, compelling, and comprehensive approach to support their decision-making. This ground-breaking book marks the dawn of a new Golden Age of fruitful conversation between entertainment scholars, managers, and artists. Allegre Hadida, Associate Professor in Strategy, University of Cambridge

Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio (R) for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.

Data is humanity's most important new resource. It has the capacity to provide insight into every aspect of our lives, the planet and the universe at large; it changes not only what we know but also how we know it. Exploiting the value of data could improve our existence as much as - if not more than - previous technological revolutions. Yet data without empathy is useless. There is a tendency in data science to forget about the human needs and feelings of the people who make up the data, the people who work with the data, and those expected to understand the results. Without empathy, this precious resource is at best underused, at worst misused. Data: A Guide to Humans will help you understand how to properly exploit data, why this is so important, and how companies and governments are currently using data. It makes a compelling case for empathy as the crucial factor in elevating our understanding of data to something which can make a lasting and essential contribution to your business, your life and maybe even the world.

Climate predictions - and the computer models behind them - play a key role in shaping public opinion and our response to the climate crisis. Some people interpret these predictions as 'prophecies of doom' and some others dismiss them as mere speculation, but the vast majority are only vaguely aware of the science behind them. This book gives a balanced view of the strengths and limitations of climate modeling. It covers historical developments, current challenges, and future trends in the field. The accessible discussion of climate modeling only requires a basic knowledge of science. Uncertainties in climate predictions and their implications for assessing climate risk are analyzed, as are the computational challenges faced by future models. The book concludes by highlighting the dangers of climate 'doomism', while also making clear the value of predictive models, and the severe and very real risks posed by anthropogenic climate change.

NOW IN FULL COLOR! Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate their data findings. This comprehensive how-to guide functions as a set of blueprints-supported by both research and the author's extensive experience with clients in industries all over the world-for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for building the chosen graph in Excel. Now in full color with new examples throughout, the Second Edition includes a revamped chapter on qualitative data, nine new quantitative graph types, new shortcuts in Excel, and an entirely new chapter on Sharing Your Data With the World, which provides advice on using dashboards. New from Stephanie Evergreen! The Data Visualization Sketchbook provides advice on getting started with sketching and offers tips, guidance, and completed sample sketches for a number of reporting formats. Bundle Effective Data Visualization, 2e, and The Data Visualization Sketchbook, using ISBN 978-1-5443-7178-8!