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, ...
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.
This self-instructional manual on the interpretation and use of epidemiologic data deals with the basic concepts and skills needed for the appraisal of published reports or one's own findings.
Author: J. H. Abramson
Publisher: Oxford University Press
This self-instructional manual on the interpretation and use of epidemiologic data deals with the basic concepts and skills needed for the appraisal of published reports or one's own findings. Applications in clinical medicine, public health, community medicine, and research are all taken into consideration. Making Sense of Data is designed as a workbook of short exercises and instructional self-tests that introduce fundamental approaches and procedures in data interpretation and develop competency in working with epidemiologic tools. Basic concepts are presented in the first section, which also demonstrates the step-by-step assessment of data. The next section discusses rates and other simple measures, and the third shows how to judge their accuracy. Section IV and V deal with more complex issues of associations between variables and the appraisal of cause-effect relationships. Section VI deals with meta-analysis (the critical review and integration of the findings from separate studies) and section VII with the questions to be asked before deciding to apply study results in practice. Numerous changes have been made in this edition, including the addition of a section on the practical application of epidemiological findings, discussions of new topics (Cox proportional hazards regression, qualitative studies, ROC curves), and fresh examples.
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 ...
Author: Glenn J. Myatt
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)
This book addresses the isues of Data Analysis and SPC in a service setting.
Author: Donald J. Wheeler
Publisher: Spc Press
Category: Social Science
This book addresses the isues of Data Analysis and SPC in a service setting. Emphasis is give to three basic questions of quality improvement: What do you want to accomplish? By what method? How will you know? 130 Examples and Case Histories from real businesses are used to illustrate the concepts. Readers discover where to start, what to measure, how to measure it, how to understand the measurement.
Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing ...
Author: Glenn J. Myatt
Publisher: John Wiley & Sons
Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.
Features include: - Logical, intuitive organization of key statistical concepts and tests with an emphasis on understanding which test to use and why - Innovative graphic illustrations and insightful dialogues that help you to get to grips ...
Author: Gerry Mulhern
Publisher: Macmillan International Higher Education
'I became a psychology student because I'm curious about why people behave as they do. Why am I expected to study statistics?' Statistics is one of the most useful elements of any psychology degree. This popular textbook will equip you with the tools needed not only to make sense of your own data and research, but also to think critically about the research and statistics you will encounter in everyday life. Features include: - Logical, intuitive organization of key statistical concepts and tests with an emphasis on understanding which test to use and why - Innovative graphic illustrations and insightful dialogues that help you to get to grips with statistics - Concise, easy-to-follow guidelines for making sense of SPSS - COverage of more complex tests and concepts for when you need to dig deeper Making Sense of Data and Statistics in Psychology will help you design experiments, analyse data with confidence and establish a solid grounding in statistics; it will become a valuable resource throughout your studies. Gerry Mulhern is Senior Lecturer in Psychology at Queen's University Belfast, UK, and was President of the British Psychological Society from 2010 to 2011. Brian Greer is Adjunct Professor in the Graduate School of Education at Portland State University, USA. He has taught statistics to psychology students for many years, and has published widely on mathematics education. At www.palgrave.com/psychology/mulhern2e, students and lecturers will find a wealth of resources, including additional data sets, extra guidance on tests and lecture slides.
This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field.
Author: William Lahoz
Publisher: Springer Science & Business Media
Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
Although introductory, the book encourages the reader to reflect critically on the general strengths and limitations of MDA techniques. Each chapter includes references for further reading accessible to the beginner.
Author: John Spicer
Making Sense of Multivariate Data Analysis is a short introduction to multivariate data analysis (MDA) for students and practitioners in the behavioral and social sciences. It provides a conceptual overview of the foundations of MDA and of a range of specific techniques including multiple regression, logistic regression, discriminant analysis, multivariate analysis of variance, factor analysis, and log-linear analysis. As a conceptual introduction, the book assumes no prior statistical knowledge, and contains very few symbols or equations. Its primary objective is to expose the conceptual unity of MDA techniques both in their foundations and in the common analytic strategies that lie at the heart of all of the techniques. Although introductory, the book encourages the reader to reflect critically on the general strengths and limitations of MDA techniques. Each chapter includes references for further reading accessible to the beginner.
Who is this book for? * Managers and business professionals * Marketers, product managers, and business strategists * Entrepreneurs, founders and startups team members * Consultants, advisors and educators * Almost anybody who has an ...
Author: Mert Damlapinar
Category: Business & Economics
Analytics of Life provides the reader with a broad overview of the field of data analytics and artificial intelligence. It provides the layperson an understanding of the various stages of artificial intelligence, the risks and powerful benefits. And it provides a way to look at big data and machine learning that enables us to make the most of this exciting new realm of technology in our day-to-day jobs and our small businesses. Questions you can find answers* * What is artificial intelligence (AI)? * What is the difference between AI, machine learning and data analytics? * Which jobs AI will replace, which jobs are safe from data analytics revolution? * Why data analytics is the best career move? * How can I apply data analytics in my job or small business? Who is this book for? * Managers and business professionals * Marketers, product managers, and business strategists * Entrepreneurs, founders and startups team members * Consultants, advisors and educators * Almost anybody who has an interest in the future According to an article by Cade Metz in The New York Times, "Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology." Oxford University professor Nick Bostrom argues that if machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. MIT professor Max Tegmark describes and illuminates the recent, ground-breaking advances in Artificial Intelligence and how it might overtake human intelligence. As Oxford University economist Daniel Susskind points out, technological progress could bring about unprecedented prosperity, solving one of humanity's oldest problems: how to make sure that everyone has enough to live on. Distinguished AI researcher and professor of computer science at UC Berkeley, Russell Stuart suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Industry experts claim that AI will have a negative impact on blue-collar jobs, but Mert predicts that Americans and Europeans will experience a strong impact on white-collar jobs as well. And Mert also provides research results and a clear description of which jobs will be affected and how soon, which jobs could be enhanced with AI. Analytics of Life also provides solutions and insight into some of the most profound changes to come in human history.
The social sector is still working out how to best acquire quality data and use it
consistently and productively. In one of our most read posts, Laura Quinn,
founder of Idealware, asserts, “We all need to understand that if we as a social
Author: Markets for Good
Category: Literary Collections
Markets for Good is an effort by the Bill & Melinda Gates Foundation, the William & Flora Hewlett Foundation, and the progressive financial firm Liquidnet to improve the system for generating, sharing, and acting upon data and information in the social sector. Our vision is of a social sector powered by information, where interventions are more effective and innovative, where capital flows efficiently to the organizations that are having the greatest impact, and where there is a dynamic culture of continuous learning and development. Over the past several years, Markets for Good has been a forum for discussion and collaboration among online giving platforms, nonprofit information providers, nonprofit evaluators, philanthropic advisors, and other entities working to improve the global philanthropic system and social sector. This effort has included over 50 people from more than 20 organizations. The website, MarketsforGood.org, and the work that we hope follows from it, is an outgrowth of what we have learned and observed through this collaboration. This retrospective collection of selected readings from our site includes an introduction by Jeff Raikes, CEO of the Bill & Melinda Gates Foundation, in which he highlights the "continuing wave of efforts that will push our sector to achieve even greater impact." Following Jeff's introduction, the Markets for Good Collaboration Team recaps the first 15 months of the campaign, and how they expect Markets for Good to evolve going forward. The subsequent 17 posts and authors' updates provide a range of perspectives on the most critical data-related challenges facing the social sector, and how these challenges can be addressed. Posts were chosen for their high readership, topic diversity, and thought leadership. The authors debate new and recurring hurdles in the social sector, like capacity and capital constraints; how qualitative data, including stories and beneficiary insights, can be incorporated into data-driven decision processes; and big-, medium-, and small-data management.
Making Sense of Data A Practical Guide to Exploratory Data Analysis and Data
Mining Glenn J. Myatt B I c E N T E N N I AL I. B I c E N T E N N I AL WILEY-
INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION Making Sense of Data ...
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.
Good for 'Mercedes-Benz'; bad for 'performance cars'. uk.yahoo.com An overview
of the subject area, structured so that you can narrow down a search or make it
broader. For example, from 'Motor manufacturer' you can go up to the broader ...
Category: Business & Economics
Managers need to be able to make sense of data and to use it selectively to answer key questions: Why has quality fallen in the last week? Should we subcontract or employ more people? What will consumer demand be in the future? They need to be able to assess the value of data and to detect what is and what isn’t spin. The focus is on analysing numbers. On their own, figures tell us very little. To become meaningful they need to be processed and analysed and it is the patterns that emerge from this that provide the information that is needed for decision-making. The book is arranged in four themes. It starts by considering the value of information in organisations and by assessing how effectively the information is used in a management role. It then goes on to look at different options for presenting figures so that trends become clearer and patterns simpler to spot. As well as making data easier to interpret, the techniques the book presents are valuable communication tools that will help the reader use information more effectively with others. The last two themes then provide a toolkit of techniques that you can use to investigate situations and help solve problems. These include statistical and operational techniques as well as computer tools. Like any toolkit, the key to using it properly lies in knowing not only what each tool does but when to use it. This book will help the reader to develop this ability by applying the methods that are described within a business context.
While sensemaking provides a useful framework in which to place the data
gathered from the study of Nova Scotia Power, it is important to note that the
study has been limited by (a) the inherent weaknesses and limitations of the sensemaking ...
Author: Jean Helms-Mills
Category: Business & Economics
Applying an invaluable sensemaking framework to organizational change and combining the theory and practice of implementing change, this book represents an instructive and informative view on change in business. Its strength lies in two key areas: the discussion and explanation of a strategic sensemaking approach, for helping managers, management educators and students to understand organizational change a longitudinal study of a major company which underwent several organizational changes, revealing some of the key problems and challenges that managers face when introducing, implementing and managing change. Rather than being structured as a ‘how to’ book, this outstanding text provides the reader with practical insights and skills for managing (or resisting) change. Applying Weick's famous sensemaking approach, it offers a unique way to understand the processes involved in organizational change.
Data Collection Strategies With our goal to collect systematic data on a
comprehensive range of governance issues, we had several options. We
considered using an international panel of experts or well-informed persons. This
would have ...
Author: Goran Hyden
Publisher: Lynne Rienner Publishers
Category: Political Science
The first conclusive, empirical demonstration of the utility of research on governance.
Shows them the meanings of the statistics they are computing. • This book is easy to digest because it is divided into short sections with review questions at the end of each section. • Running sidebars draw students’ attention to ...
Author: Fred Pyrczak
Publisher: Taylor & Francis
• An overview of descriptive and inferential statistics without formulas and computations. • Clear and to-the-point narrative makes this short book perfect for all courses in which statistics are discussed. • Helps statistics students who are struggling with the concepts. Shows them the meanings of the statistics they are computing. • This book is easy to digest because it is divided into short sections with review questions at the end of each section. • Running sidebars draw students’ attention to important concepts.
This chapter looks at the process of 'statistical inference': using a limited sample
of data to draw conclusions about a wider context. We start with the approach
based on a famous theorem of probability, 'Bayes' theorem', and then move on to
Author: Michael Wood
Publisher: Macmillan International Higher Education
Making Sense of Statistics provides a thorough, but accessible, introduction to statistics and probability, without the distractions of mathematics. The book does not require you to use any algebraic formulae or equations, but it does explain how and why methods work, and exactly what answers mean. Guidance is provided on how to design investigations, analyze data and interpret results. There are exercises and case studies from a variety of areas of application, and an accompanying website from which interactive spreadsheet models and data files can be downloaded.
eCaUse sTaTisTiCal DaTa ofall kinds often plays a role in a business reference
question scenario, in this chapter i cover some of the key resources you'll want to
know about. the word statistics is, by itself, a very vague and broad term, and ...
Author: Celia Ross
Publisher: American Library Association
Category: Language Arts & Disciplines
Celia Ross explains how to provide quality reference help on issues from marketing to finance - for business people, students, and even business faculty.
principles about agent-dependent reasons to make sense (in light) of the data,
they are less attractive in an account of what it is for normative principles about
agent-independent reasons to make sense of the data. Someone who disagrees
Author: B.C. Postow
Publisher: Springer Science & Business Media
2 first-person point of view, I acknowledge these possible handicaps and try to overcome them. Other people may coherently judge that I am incapable of figuring out correctly what I rationally ought to do, or they may inform me of reasons of which I had heretofore been ignorant, or they may try to help me overcome intellectual hindrances. Like me, these people would be assuming that the goal is to identify what I really rationally ought to do. Nevertheless, we are concerned with reasons for the agent to act in a certain way, rather than with reasons, say, for someone to want it to be the case that the agent act. Thus to be a reason in our sense is to be a consideration which has an appropriate guiding role to play in the. agents deliberation. (An agent is guided by reasons if she determines what to do in light of the reasons. ) Suppose then that a nor mative theory says that it is supremely desirable, or that it rationally ought to be the case, that agents act in a way that maximizes the general utility, but that (since the general utility is never in fact maximized by those who pay attention to it) considerations of the general utility should play no role in the agents' deliberation. Such a theory would not be said to ascribe to agents a reason to maximize the general utility on our usage.