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  • Format: PDF

Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework of data mining in business analytics. It explores the contents, capabilities, and applications of business analytics without assuming any prior knowledge or technical skills. The authors describe business analytics from a non-commercial standpoint, demystify the main concepts and terminologies, and give many examples of real-world applications. They take you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.

Produktbeschreibung
Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework of data mining in business analytics. It explores the contents, capabilities, and applications of business analytics without assuming any prior knowledge or technical skills. The authors describe business analytics from a non-commercial standpoint, demystify the main concepts and terminologies, and give many examples of real-world applications. They take you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Autorenporträt
David R. Hardoon is head of analytics at SAS Singapore, where he is responsible for the positioning of business analytics capabilities and solutions to customers across different business sectors. Dr. Hardoon is also an adjunct faculty member in the School of Information Systems at Singapore Management University and an honorary senior research associate in the Centre for Computational Statistics and Machine Learning at University College London. His research interests include developing and applying computational analytical models for business knowledge discovery and analysis in areas such as taxonomy, neuroscience, aerospace, and finance. He earned a PhD in computer science in the field of machine learning from the University of Southampton.

Galit Shmueli is a SRITNE chaired professor of data analytics and associate professor of statistics and information systems at the Indian School of Business. She is the author of 70 journal articles, references, textbooks, and book chapters in statistics, management, information systems, and marketing. Her research and teaching focus on statistical and data mining methods for contemporary data and applications in information systems and healthcare. She earned a PhD in statistics from the Israel Institute of Technology.