Business Analytics: Handbooks/Background

By adapting and exploiting digital technology in new ways, businesses can gain better and more detailed customer experiences and build stronger relationships with their clients. In addition, digital transformation can help organizations to be more agile. This handbook contains chapters and cases written by experts in the field, that covers topics like implementing new systems, integrating existing systems, and using data analytics and artificial intelligence (AI) or machine learning to drive better outcomes.

This comprehensive handbook covers GeoAI, which is the integration of geospatial studies, AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, technologies and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses.

An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences.

This encyclopedia is a comprehensive reference book featuring some 500 entries, from "Access" to "Zillow," written by international experts in thef ield. The Encyclopedia will serve as a fundamental resource for researchers and students to understand the scope and depth to all topics that fall under the ‘big data’ umbrella.

This perennial favorite beginner’s guide to reading and understanding financial reports has been updated. It’s intended for investors, lenders, business leaders, analysts, and managers who want to read, analyze, and interpret financial accounting reports. Designed specifically for non-specialists, this reader-friendly resource covers the fundamentals of financial reporting in jargon-free English.

Data Science students and practitioners want to find a forecast that "works" and don't want to be constrained to a single forecasting strategy. This book covers techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject. This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.

This book is a comprehensive resource about the core concepts of big data analytics, including tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches.