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Author: Elearn
Publisher: Taylor & Francis
ISBN: 1136386661
Category : Business & Economics
Languages : en
Pages : 115
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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: Elearn
Publisher: Taylor & Francis
ISBN: 1136386661
Category : Business & Economics
Languages : en
Pages : 115
Get Book Here
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: 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: Andrew Bell
Publisher: SAGE
ISBN: 1526493004
Category : Social Science
Languages : en
Pages : 232
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Book Description
The amount of data produced, captured and transmitted through the media has never been greater. But for this data to be useful, it needs to be properly understood and claims made about or with data need to be properly scrutinized. Through a series of examples of statistics in the media, this book shows you how to critically assess the presentation of data in the media, to identify what is significant and to sort verifiable conclusions from misleading claims. How accurate are polls, and how should we know? How should league tables be read? Are numbers presented as ‘large’ really as big as they may seem at first glance? By answering these questions and more, readers will learn a number of statistical concepts central to many undergraduate social science statistics courses. By tying them in to real life examples, the importance and relevance of these concepts comes to life. As such, this book does more than teaches techniques needed for a statistics course; it teaches you life skills that we need to use every single day.
Author: Glenn J. Myatt
Publisher: John Wiley & Sons
ISBN: 1118422104
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: 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: Hunter Whitney
Publisher: Newnes
ISBN: 0123877946
Category : Computers
Languages : en
Pages : 321
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Book Description
Data Insights: New Ways to Visualize and Make Sense of Data offers thought-provoking insights into how visualization can foster a clearer and more comprehensive understanding of data. The book offers perspectives from people with different backgrounds, including data scientists, statisticians, painters, and writers. It argues that all data is useless, or misleading, if we do not know what it means.Organized into seven chapters, the book explores some of the ways that data visualization and other emerging approaches can make data meaningful and therefore useful. It also discusses some fundamental ideas and basic questions in the data lifecycle; the process of interactions between people, data, and displays that lead to better questions and more useful answers; and the fundamentals, origins, and purposes of the basic building blocks that are used in data visualization. The reader is introduced to tried and true approaches to understanding users in the context of user interface design, how communications can get distorted, and how data visualization is related to thinking machines. Finally, the book looks at the future of data visualization by assessing its strengths and weaknesses. Case studies from business analytics, healthcare, network monitoring, security, and games, among others, as well as illustrations, thought-provoking quotes, and real-world examples are included.This book will prove useful to computer professionals, technical marketing professionals, content strategists, Web and product designers, and researchers. Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data Answers the question, "How can data visualization help me?" with discussions of how it fits into a wide array of purposes and situations Makes the case that data visualization is not just about technology; it also involves a deeply human process
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: Katy Borner
Publisher: MIT Press
ISBN: 0262526190
Category : Language Arts & Disciplines
Languages : en
Pages : 310
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Book Description
A guide to the basics of information visualization that teaches nonprogrammers how to use advanced data mining and visualization techniques to design insightful visualizations. In the age of Big Data, the tools of information visualization offer us a macroscope to help us make sense of the avalanche of data available on every subject. This book offers a gentle introduction to the design of insightful information visualizations. It is the only book on the subject that teaches nonprogrammers how to use open code and open data to design insightful visualizations. Readers will learn to apply advanced data mining and visualization techniques to make sense of temporal, geospatial, topical, and network data. The book, developed for use in an information visualization MOOC, covers data analysis algorithms that enable extraction of patterns and trends in data, with chapters devoted to “when” (temporal data), “where” (geospatial data), “what” (topical data), and “with whom” (networks and trees); and to systems that drive research and development. Examples of projects undertaken for clients include an interactive visualization of the success of game player activity in World of Warcraft; a visualization of 311 number adoption that shows the diffusion of non-emergency calls in the United States; a return on investment study for two decades of HIV/AIDS research funding by NIAID; and a map showing the impact of the HiveNYC Learning Network. Visual Insights will be an essential resource on basic information visualization techniques for scholars in many fields, students, designers, or anyone who works with data.
Author: Michael Bowen
Publisher:
ISBN: 9781938946035
Category : Graphic methods
Languages : en
Pages : 171
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Book Description
Here's the ideal statistics book for teachers with no statistical background. Written in an informal style with easy-to-grasp examples, The Basics of Data Literacy teaches you how to help your students understand data. Then, in turn, they learn how to collect, summarize, and analyze statistics inside and outside the classroom. The books 10 succinct chapters provide an introduction to types of variables and data, ways to structure and interpret data tables, simple statistics, and survey basics from a student perspective. The appendices include hands-on activities tailored to middle and high school investigations. Because data are so central to many of the ideas in the Next Generation Science Standards, the ability to work with such information is an important science skill for both you and your students. This accessible book will help you get over feeling intimidated as your students learn to evaluate messy data on the Internet, in the news, and in future negotiations with car dealers and insurance agents.
Author: Michael R. Berthold
Publisher: Springer Science & Business Media
ISBN: 184882260X
Category : Computers
Languages : en
Pages : 399
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Book Description
Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
