The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of data-driven decision making are plentiful and increasing on a daily basis. This book will motivate the need for data and data-driven solutions, using real data from real business scenarios. It will allow managers to better interact with personnel specializing in analytics by exposing managers and decision makers to the key ideas and concepts of data-driven decision making. Business Analytics for Managers conveys ideas and concepts from both statistics and data mining with the goal of extracting knowledge from real business data and actionable insight for managers. Throughout, emphasis placed on conveying data-driven thinking. While the ideas discussed in this book can be implemented using many different software solutions from many different vendors, it also provides a quick-start to one of the most powerful software solutions available. The main goals of this book are as follows: to excite managers and decision makers about the potential that resides in data and the value that data analytics can add to business processes and provide managers with a basic understanding of the main concepts of data analytics and a common language to convey data-driven decision problems so they can better communicate with personnel specializing in data mining or statistics.
This groundbreaking book introduces the application of statistical methodologies to e-Commerce data With the expanding presence of technology in today's economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity. This collection gathers over thirty researchers and practitioners from the fields of statistics, computer science, information systems, and marketing to discuss the growing use of statistical methods in e-Commerce research. From privacy protection to economic impact, the book first identifies the many obstacles that are encountered while collecting, cleaning, exploring, and analyzing e-Commerce data. Solutions to these problems are then suggested using established and newly developed statistical and data mining methods. Finally, a look into the future of this evolving area of study is provided through an in-depth discussion of the emerging methods for conducting e-Commerce research. Statistical Methods in e-Commerce Research successfully bridges the gap between statistics and e-Commerce, introducing a statistical approach to solving challenges that arise in the context of online transactions, while also introducing a wide range of e-Commerce applications and problems where novel statistical methodology is warranted. It is an ideal text for courses on e-Commerce at the upper-undergraduate and graduate levels and also serves as a valuable reference for researchers and analysts across a wide array of subject areas, including economics, marketing, and information systems who would like to gain a deeper understanding of the use of statistics in their work.
Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data. The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include: Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research. Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes. Visit this book's companion website by clicking here
The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of data-driven decision making are plentiful and increasing on a daily basis. This book will motivate the need for data and data-driven solutions, using real data from real business scenarios. It will allow managers to better interact with personnel specializing in analytics by exposing managers and decision makers to the key ideas and concepts of data-driven decision making. Business Analytics for Managers conveys ideas and concepts from both statistics and data mining with the goal of extracting knowledge from real business data and actionable insight for managers. Throughout, emphasis placed on conveying data-driven thinking. While the ideas discussed in this book can be implemented using many different software solutions from many different vendors, it also provides a quick-start to one of the most powerful software solutions available. The main goals of this book are as follows: to excite managers and decision makers about the potential that resides in data and the value that data analytics can add to business processes and provide managers with a basic understanding of the main concepts of data analytics and a common language to convey data-driven decision problems so they can better communicate with personnel specializing in data mining or statistics.
Combining rigorous academic research with the latest practical tools and techniques, this book explores the growing area of complextity management in business. Through a range of high quality international cases leading authors help students to understand how to manage organizations in unpredictable and complex business environments.
We are drowning in data but are starved for knowledge. Data Analytics is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools. Hence, we gain knowledge by understanding the data. A roadmap to achieve this is encapsulated in the knowledge discovery in databases (KDD) process. Databases help us store data in a structured way. The structure query language (SQL) allows us to gain first insights about business opportunities. Visualising the data using business intelligence tools and data science languages deepens our understanding of the key performance indicators and business characteristics. This can be used to create relevant classification and prediction models; for instance, to provide customers with the appropriate products or predict the eruption time of geysers. Machine learning algorithms help us in this endeavour. Moreover, we can create new classes using unsupervised learning methods, which can be used to define new market segments or group customers with similar characteristics. Finally, artificial intelligence allows us to reason under uncertainty and find optimal solutions for business challenges. All these topics are covered in this book with a hands-on process, which means we use numerous examples to introduce the concepts and several software tools to assist us. Several interactive exercises support us in deepening the understanding and keep us engaged with the material. This book is appropriate for master students but can be used for undergraduate students. Practitioners will also benefit from the readily available tools. The material was especially designed for Business Analytics degrees with a focus on Data Science and can also be used for machine learning or artificial intelligence classes. This entry-level book is ideally suited for a wide range of disciplines wishing to gain actionable data insights in a practical manner.
Complex analysis nowadays has higher-dimensional analoga: the algebra of complex numbers is replaced then by the non-commutative algebra of real quaternions or by Clifford algebras. During the last 30 years the so-called quaternionic and Clifford or hypercomplex analysis successfully developed to a powerful theory with many applications in analysis, engineering and mathematical physics. This textbook introduces both to classical and higher-dimensional results based on a uniform notion of holomorphy. Historical remarks, lots of examples, figures and exercises accompany each chapter.
This book delivers new IMD insights on an emerging challenge - how to deal with overwhelming complexity. Global organizations face a complex decision-making environment. On one side, diversity of cultures, customers, competitors and regulations creates complexity; on the other, competitive pressures cause expanding countries to extract more synergies across products and regions. In such a climate, a new way of thinking, acting and organizing is needed beyond the familiar ‘control’ mindset. Drawing together insights from across the expert faculty, Managing Complexity in the Global Organization presents IMD’s framework on how to understand complexity and its four key drivers (diversity; interdependence; ambiguity and flux), along with solutions on specific issues in a variety of functions, industries and markets. The focus is on providing practical solutions based on real-life examples.
Over the last decade the world has experienced a growing interest in problems associated with the nonmedical use of drugs. This interest has corresponded to a real growth in the extent, diversity, and social impact of the use of alcohol and drugs in many societies. As a result, the amount of research and writing on the subject of drug problems has greatly increased, and it has become very difficult for one individual to keep up with all the relevant literature. There is thus an acute need in the field for critical reviews that assess current developments, and the present series is intended to fill this need. The series is not to be an "annual review" in the usual sense. The aim is not to cover all the work reported during the preceding year in relation to a fixed selection of topics. Rather, it is to present each year evaluative papers on topics in which enough recent progress has been made to alter the general scope in a particular area. Owing to the multidisciplinary nature of problems of drug use and dependence, the papers published in each volume will be drawn from several disciplines. However, some volumes may be devoted to one partic ular problem, with individual reviews and papers examining various aspects of it. The composition of the editorial board and the international advisory board reflects these objectives. The editors are members of the senior scientific staff of the Addiction Research Foundation of Ontario.
Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, or science. Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them. This book starts with an introduction to visualization and historical examples of visual representations. At its core, the book presents and discusses a systematic view of the visualization of time-oriented data along three key questions: what is being visualized (data), why something is visualized (user tasks), and how it is presented (visual representation). To support visual exploration, interaction techniques and analytical methods are required that are discussed in separate chapters. A large part of this book is devoted to a structured survey of 101 different visualization techniques as a reference for scientists conducting related research as well as for practitioners seeking information on how their time-oriented data can best be visualized.
This is the new edition of a two-volume directory that documents the entire European music industry. Entries include contact information, as well as descriptions of the organizations and the types of music involved, when available and/or applicable. The first volume discusses orchestras (from symphonies to chamber orchestras and brass bands), choirs, European music theaters, competitions and prizes, concert management and promotion agencies, radio and television, information on associations and foundations, teaching and instruction, and music libraries and archives, museums, and research and university institutes. The second volume covers all areas of the music industry and trade, i.e. instrument making, music and computers, music trade and sales, trade fairs for music, antiquarians and auction houses, sound studios and record companies, music publishers, and sound, lighting and scenery. It also contains the indexes of institutions and firms, persons, and instruments. Distributed by Gale. Annotation copyrighted by Book News, Inc., Portland, OR
This groundbreaking book introduces the application of statistical methodologies to e-Commerce data With the expanding presence of technology in today's economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity. This collection gathers over thirty researchers and practitioners from the fields of statistics, computer science, information systems, and marketing to discuss the growing use of statistical methods in e-Commerce research. From privacy protection to economic impact, the book first identifies the many obstacles that are encountered while collecting, cleaning, exploring, and analyzing e-Commerce data. Solutions to these problems are then suggested using established and newly developed statistical and data mining methods. Finally, a look into the future of this evolving area of study is provided through an in-depth discussion of the emerging methods for conducting e-Commerce research. Statistical Methods in e-Commerce Research successfully bridges the gap between statistics and e-Commerce, introducing a statistical approach to solving challenges that arise in the context of online transactions, while also introducing a wide range of e-Commerce applications and problems where novel statistical methodology is warranted. It is an ideal text for courses on e-Commerce at the upper-undergraduate and graduate levels and also serves as a valuable reference for researchers and analysts across a wide array of subject areas, including economics, marketing, and information systems who would like to gain a deeper understanding of the use of statistics in their work.
Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data. The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include: Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research. Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes. Visit this book's companion website by clicking here
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