Making Sense of Data III

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 Na?ve 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.

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 Traceis™ 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.

The book sets out deliberately to challenge the current construction improvement debate and the way in which it is conducted. It confronts the supposedly neutral nature of construction 'best practice' and demonstrates that that the advocated recipes seldom stand up to critical scrutiny. It further argues that commonly accepted components of best practice such as lean construction, partnering and collaborative working rarely live up to the claims made on their behalf. Such recipes invariably suffer from definitional vagueness, and are constantly reinterpreted to suit the needs the different audiences. Making Sense of Construction Improvement argues that construction sector improvement techniques cannot be understood in terms of their substantive content, and are best understood in terms of the rhetoric within which they are presented. The author also contends that the persuasiveness of such recipes depends upon the extent to which practitioners can adopt them for the purposes of making sense of the changes they observe happening around them. To be accepted as 'best practice' construction improvement techniques must also resonate with broader agendas of socio-technological change. The author charts how the best practice debate has developed from the aftermath of the Second World War through to the election of David Cameron's coalition government in 2010. Attention is given to the way in which the improvement debate throughout the 1960s and 70s was shaped by the broader aspirations of the post-war social consensus and the associated desire for a centrally planned economy. Attention thereafter is given to the way the construction sector was radically re-shaped by the advent of the enterprise culture. The privatisation of the sector's client base, coupled with the withdrawal of the state as a provider of mass housing, caused a significant and long-lasting shift in the construction landscape. Private sector clients similarly experienced extensive downsizing while outsourcing their procurement capabilities. Such strategies were frequently justified by mobilising the rhetoric of business process re-engineering (BPR). Contracting firms simultaneously faced unpredictable workloads and increasing market competition. In response, the sector at large chose to base their competitive advantage on leanness and agility. Hence the emergence of the hollowed-out firm as the dominant form of organising. These structural trends combined to provide the backcloth to the industry improvement agenda throughout the 1980s and 1990s. Making Sense of Construction Improvement argues that the popularity of improvement recipes such as partnering, collaborative working and integrated teams can be understood as strategies for overcoming the loss of control associated with downsizing and outsourcing. In contrast to other textbooks, Making Sense of Construction Improvement does not offer advice on how to manage construction projects more effectively; the aim is rather to understand the forces which have shaped the construction sector improvement agenda over time.

The Children Act is a major piece of legislation that affects all professionals working with children. Since the third edition of this book was published in 1996, there have been some important developments. This Fourth Edition takes these developments into consideration. Topics discussed include case law in the superior courts; the establishment of CAFCASS; and the Fostering Services Regulations of 2002; and much more.

Making Sense of the Organization elaborates on the influential idea that organizations are interpretation systems that scan, interpret, and learn. These selected essays represent a new approach to the way managers learn and act in response to their environment and the way organizational change evolves. Readers of this volume will find a wealth of examples and insights which go well beyond thinking and cognition to explain action. The author's ideas are at the forefront of our thinking on leadership, teams, and the management of change. “This book engages the puzzle of impermanence in organizing. Through rich examples, evocative language, artful literature citing, and imaginative connecting, Weick re-introduces core ideas and themes around attending, interpreting, acting and learning to unlock new insights about impermanent organizing. The wisdom in this book is timeless and timely. It prods scholars and managers of organizations to complicate their views of organizing in ways that enrich thought and action.” – Jane E. Dutton, Robert L. Kahn Distinguished University Professor, University of Michigan

Streamline data analysis with an intuitive, visual Six Sigma strategy Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual knowledge to pose relevant questions and make more sound decisions. You'll learn dynamic visualization and exploratory data analysis techniques that help you identify occurrences and sources of variation, and the strategies and processes that make Six Sigma work for your organization. The Six Sigma strategy helps you identify and remove causes of defects and errors in manufacturing and business processes; the more pragmatic Visual approach opens the strategy beyond the realms of statisticians to provide value to all business leaders amid the growing need for more accessible quality management tools. See where, why, and how your data varies Find clues to underlying behavior in your data Identify key models and drivers Build your own Six-Sigma experience Whether your work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the skills and understanding to get more from your data. With intuitive, easy-to-use tools and clear explanations, Visual Six Sigma is a roadmap to putting this strategy to work for your company.

The book is intended to provide a much deeper understanding of agile principles, methodologies, and practices to enable project managers to develop a more agile approach and understand how to blend and tailor agile and traditional principles, methodologies, and practices to create an appropriate balance of control and agility to fit a business environment as well as the risks and complexities of any individual project. The book will also provide business managers and leaders an understanding of how to fit agile methodologies into an overall business strategy that provides the right balance of control and agility for their business.

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

This book is an ideal resource on the subject of systems practice for busy managers whose time is scarce. It provides a rapid introduction to straightforward, yet powerful ideas that enable users to address real world problems. Systems theory and practice is predominantly a framework for thinking about the World, in which holistic views are maintained. In this respect it contrasts with some familiar techniques of management science, in which problem situations are broken down into their constituent parts with resultant loss of coherence.

Wiley's new Handbook of Decision Making is a vital reference text for all students and professionals of management, organization and decision making. The handbook offers a wide range of theoretical and empirical approaches to the understanding of organizational and strategic decisions. Contributors are internationally known experts drawn from North America, Canada and Europe who have spent many years in the study of decision making, and decision making relevant topics. We believe the handbook will become a tour de force in the understanding decision making, offering a wide variety of perspectives, topics, and summative understanding of the field. Chapters in the Handbook were prepared by the leading experts in their field and include cutting edge empirical, theoretical, and review chapters. The chapters bring together for the first time a critical mass of writing on decision making as an organizational and research activity. The Editors are two of the leading international experts in decision making and contribute to the Handbook with five original Chapters that offer an appraisal of the field and suggestions for research, as well as the current status of decision making practice and suggestion for improvement.