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The M.B.A. with a Concentration in Business Intelligence/Analytics provides a foundation in advanced business administration while focusing on the science of business modeling, database systems, data warehousing, data mining, and benchmarking.
Data Mining for Business Intelligence by Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: * Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government * Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students * More than a dozen case studies demonstrating applications for the data mining techniques described * End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented * A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Publication Date: 2017-08-29
Data Mining and Market Intelligence by This book is written to address the issues relating to data gathering, data warehousing, and data analysis, all of which are useful when working with large amounts of data. Using practical examples of market intelligence, this book is designed to inspire and inform readers on how to conduct market intelligence by leveraging data and technology, supporting smart decision making. The book explains some suitable methodologies for data analysis that are based on robust statistical methods. For illustrative purposes, the author uses real-life data for all the examples in this book. In addition, the book discusses the concepts, techniques, and applications of digital media and mobile data mining. Hence, this book is a guide tool for policy makers, academics, and practitioners whose areas of interest are statistical inference, applied statistics, applied mathematics, business mathematics, quantitative techniques, and economic and social statistics.
Publication Date: 2018-04-30
Deep Learning: Convergence to Big Data Analytics by This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX.
Publication Date: 2019
Free and Open Source Software in Modern Data Science and Business Intelligence by Computer software and technologies are advancing at an amazing rate. The accessibility of these software sources allows for a wider power among common users as well as rapid advancement in program development and operating information. Free and Open Source Software in Modern Data Science and Business Intelligence: Emerging Research and Opportunities is a critical scholarly resource that examines the differences between the two types of software, integral in the FOSS movement, and their effect on the distribution and use of software. Featuring coverage on a wide range of topics, such as FOSS Ecology, graph mining, and project tasks, this book is geared towards academicians, researchers, and students interested in current research on the growing importance of FOSS and its expanding reach in IT infrastructure.
Publication Date: 2017-12-15
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