Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 1118407415
Category : Mathematics
Languages : en
Pages : 262

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Book Description
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.

Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 0470101016
Category : Mathematics
Languages : en
Pages : 294

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Book Description
A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: * Problem definitions * Data preparation * Data visualization * Data mining * Statistics * Grouping methods * Predictive modeling * Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.

Author: Donald J. Wheeler
Publisher: SPC Press, Incorporated
ISBN: 9780945320616
Category : Graphic methods
Languages : en
Pages : 0

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Book Description
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.

Author: Glenn J. Myatt
Publisher: Wiley
ISBN: 9781118395141
Category : Mathematics
Languages : en
Pages : 0

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Book Description
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)

Author: Elearn
Publisher: Routledge
ISBN: 113638667X
Category : Business & Economics
Languages : en
Pages : 120

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Book Description
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.

Author: Gerry Mulhern
Publisher: Bloomsbury Publishing
ISBN: 0230357997
Category : Psychology
Languages : en
Pages : 320

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Book Description
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. Companion Site: www.palgrave.com/psychology/mulhern2e An innovative and easy-to-read introduction to understanding statistical concepts and data in Psychology, written with even the most maths-averse Psychology student in mind. Authored by the current president of the BPS (British Psychological Society), this second edition includes guidance for SPSS and extended statistical coverage to bridge the gap between conceptual understanding of data and how to run statistical tests. Confronts the challenge of teaching statistics The material is structured so that the reader revisits ideas at increasing levels of sophistication, building on their existing knowledge in order to develop their understanding of statistics. This book, grounded in the authors' research into the way students learn maths and statistics, provides a 'way in' to statistics for all Psychology undergraduates, from those who have studied Maths to A Level to those who find their statistics courses to be the most daunting of their university years. The authors emphasise the importance of developing a 'feel' for data, particularly through visual representation, before statistical tests are discussed in detail. Making extensive use of exploratory data analysis, the text emphasises conceptual understanding. Concepts are introduced and clearly explained, enabling the student to understand the foundations of data analysis in interpreting psychological research. There is an abundant use of examples from psychological research throughout, helping students to get to grips with different forms of data. Flexible approach Can easily be integrated into 'standard courses', but also used to support more mathematicallyorientated courses. Reinforces understanding Avoids the jargon that makes statistics so inaccessible to many Psychology students. Pedagogical features include Socratic dialogues between statisticsaverse students and their lecturers; 'Making Links' boxes to help students see the connections between basic and more complex tests; and innovative comprehension check boxes which encourage students to stop and think before reading on. A new feature, 'Making sense of SPSS', links this conceptual comprehension to the way students mostly carry out their statistical tests. Making Sense of Data and Statistics in Psychology ensures that students have a firm basis in the use of statistics that will serve them for life, not just for the duration of their statistics course.

Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 0470222808
Category : Mathematics
Languages : en
Pages : 325

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Book Description
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.

Author: Markets for Good
Publisher: eBookIt.com
ISBN: 1456621149
Category : Literary Collections
Languages : en
Pages : 82

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Book Description
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.

Author: Pengfei Zhao
Publisher: SAGE Publications, Incorporated
ISBN: 1506378692
Category : Social Science
Languages : en
Pages : 497

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Book Description
Making Sense of Social Research Methodology: A Student and Practitioner Centered Approach introduces students to research methods by illuminating the underlying assumptions of social science inquiry. Authors Pengfei Zhao, Karen Ross, Peiwei Li, and Barbara Dennis show how research concepts are often an integral part of everyday life through illustrative common scenarios, like looking for a recipe or going on a job interview. The authors extrapolate from these personal but ubiquitous experiences to further explain concepts, like gathering data or social context, so students develop a deeper understanding of research and its applications outside of the classroom. Students from across the social sciences can take this new understanding into their own research, their professional lives, and their personal lives with a new sense of relevancy and urgency. This text is organized into clusters that center on major topics in social science research. The first cluster introduces concepts that are fundamental to all aspects and steps of the research process. These concepts include relationality, identity, ethics, epistemology, validity, and the sociopolitical context within which research occurs. The second and third clusters focus on data and inference. These clusters engage concretely with steps of the research process, including decisions about designing research, generating data, making inferences. Throughout the chapters, Pause and Reflect open-ended questions provide readers with the space for further inquiry into research concepts and how they apply to life. Research Scenario features in each chapter offer new perspectives on major research topics from leading and emerging voices in methods. Moving from this dialogic perspective to more actionable advice, You and Research features offer students concrete steps for engaging with research. Take your research into the world with Making Sense of Social Research Methodology: A Student and Practitioner Centered Approach.

Author: ROB. COLLIE
Publisher: Holy Macro! Books
ISBN: 9781615470594
Category :
Languages : en
Pages : 0

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Book Description
From Data Science to Analytics to Business Intelligence to Big Data, the authors clear the smokescreens of an industry whose capacity for buzzword innovation obscures simple truths that remain stable even in the face of remarkable technological advances. Drawing on their unique perspectives as longtime analytics software industry insiders, and incorporating dozens of illustrations and case studies, Farmer and Collie address crucially-important questions such as: What are the things we should be doing with data, and what are the things we should STOP doing? Which new technologies and methodologies should we embrace or avoid? When is it appropriate to pull the plug on a project? How can we transition from simply "feeling smarter" to actually impacting the bottom line?