A Practical Guide to Exploratory Data Analysis and Data Mining
Author: Glenn J. Myatt
Publisher: John Wiley & Sons
Praise for the First Edition “...a well-written book on data analysis anddata mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practicalstatistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of DataI: A Practical Guide to Exploratory Data Analysis and Data Mining,Second Edition focuses on basic data analysis approaches thatare necessary to make timely and accurate decisions in a diverserange of projects. Based on the authors’ practical experiencein implementing data analysis and data mining, the new editionprovides clear explanations that guide readers from almost everyfield of study. In order to facilitate the needed steps when handling a dataanalysis or data mining project, a step-by-step approach aidsprofessionals in carefully analyzing data and implementing results,leading to the development of smarter business decisions. The toolsto summarize and interpret data in order to master data analysisare integrated throughout, and the Second Edition alsofeatures: Updated exercises for both manual and computer-aidedimplementation with accompanying worked examples New appendices with coverage on the freely availableTraceis™ software, including tutorials using data from avariety of disciplines such as the social sciences, engineering,and finance New topical coverage on multiple linear regression and logisticregression to provide a range of widely used and transparentapproaches Additional real-world examples of data preparation to establisha practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory DataAnalysis and Data Mining, Second Edition is an excellentreference for researchers and professionals who need to achieveeffective decision making from data. The Second Edition isalso an ideal textbook for undergraduate and graduate-level coursesin data analysis and data mining and is appropriate forcross-disciplinary courses found within computer science andengineering departments.
A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications
Author: Glenn J. Myatt
Publisher: John Wiley & Sons
A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.
A Practical Guide to Designing Interactive Data Visualizations
Author: Glenn J. Myatt
Publisher: John Wiley & Sons
Focuses on insights, approaches, and techniques that areessential to designing interactive graphics and visualizations Making Sense of Data III: A Practical Guide to DesigningInteractive Data Visualizations explores a diverse range ofdisciplines to explain how meaning from graphical representationsis extracted. Additionally, the book describes the best approachfor designing and implementing interactive graphics andvisualizations that play a central role in data exploration anddecision-support systems. Beginning with an introduction to visual perception, MakingSense of Data III features a brief history on the use ofvisualization in data exploration and an outline of the designprocess. Subsequent chapters explore the following key areas: Cognitive and Visual Systems describes how various drawings,maps, and diagrams known as external representations are understoodand used to extend the mind's capabilities Graphics Representations introduces semiotic theory anddiscusses the seminal work of cartographer Jacques Bertin and thegrammar of graphics as developed by Leland Wilkinson Designing Visual Interactions discusses the four stages ofdesign process—analysis, design, prototyping, andevaluation—and covers the important principles and strategiesfor designing visual interfaces, information visualizations, anddata graphics Hands-on: Creative Interactive Visualizations with Protovisprovides an in-depth explanation of the capabilities of theProtovis toolkit and leads readers through the creation of a seriesof visualizations and graphics The final chapter includes step-by-step examples that illustratethe implementation of the discussed methods, and a series ofexercises are provided to assist in learning the Protovis language.A related website features the source code for the presentedsoftware as well as examples and solutions for selectexercises. Featuring research in psychology, vision science, statistics,and interaction design, Making Sense of Data III is anindispensable book for courses on data analysis and data mining atthe upper-undergraduate and graduate levels. The book also servesas a valuable reference for computational statisticians, softwareengineers, researchers, and professionals of any discipline whowould like to understand how the mind processes graphicalrepresentations.
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining by Glenn J. Myatt (978-0-470-07471-8), Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications by Glenn J. Myatt and Wayne P. Johnson (978-0-470-22280-5), and Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations by Glenn J. Myatt and Wayne P. Johnson (978-0-470-53649-0)
"What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use verbal, numerical, graphic, or combined codes, and when to use qualitative, quantitative, graphic, or mixed-methods modes of analysis. Chapters on each topic are organized so that researchers can read them sequentially or can easily "flip and find" answers to specific questions. Nontechnical discussions of cutting-edge approaches--illustrated with real-world examples--emphasize how to choose (rather than how to implement) the various analyses. The book shows how using the right analysis methods leads to more justifiable conclusions and more persuasive presentations of research results. Useful features for teaching or self-study: *Chapter-opening preview boxes that highlight useful topics addressed. *End-of-chapter summary tables recapping the 'dos and don'ts' and advantages and disadvantages of each analytic technique. *Annotated suggestions for further reading and technical resources on each topic. Subject Areas/Keywords: analyses, coding, combined methods, data analysis, data collection, dissertation, graphical, interpretation, mixed methods, qualitative, quantitative, research analysis, research designs, research methods, social sciences, thesis, visual Audience: Researchers, instructors, and graduate students in a range of disciplines, including psychology, education, social work, sociology, health, and management; administrators and managers who need to make data-driven decisions"--
“To design future networks that are worthy of society’s trust, we must put the ‘discipline’ of computer networking on a much stronger foundation. This book rises above the considerable minutiae of today’s networking technologies to emphasize the long-standing mathematical underpinnings of the field.” –Professor Jennifer Rexford, Department of Computer Science, Princeton University “This book is exactly the one I have been waiting for the last couple of years. Recently, I decided most students were already very familiar with the way the net works but were not being taught the fundamentals–the math. This book contains the knowledge for people who will create and understand future communications systems." –Professor Jon Crowcroft, The Computer Laboratory, University of Cambridge The Essential Mathematical Principles Required to Design, Implement, or Evaluate Advanced Computer Networks Students, researchers, and professionals in computer networking require a firm conceptual understanding of its foundations. Mathematical Foundations of Computer Networking provides an intuitive yet rigorous introduction to these essential mathematical principles and techniques. Assuming a basic grasp of calculus, this book offers sufficient detail to serve as the only reference many readers will need. Each concept is described in four ways: intuitively; using appropriate mathematical notation; with a numerical example carefully chosen for its relevance to networking; and with a numerical exercise for the reader. The first part of the text presents basic concepts, and the second part introduces four theories in a progression that has been designed to gradually deepen readers’ understanding. Within each part, chapters are as self-contained as possible. The first part covers probability; statistics; linear algebra; optimization; and signals, systems, and transforms. Topics range from Bayesian networks to hypothesis testing, and eigenvalue computation to Fourier transforms. These preliminary chapters establish a basis for the four theories covered in the second part of the book: queueing theory, game theory, control theory, and information theory. The second part also demonstrates how mathematical concepts can be applied to issues such as contention for limited resources, and the optimization of network responsiveness, stability, and throughput.