Business Analytics: Handbooks/Background

Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. The second edition enables the reader to think like a veteran data scientist and approach solutions to business problems in an entirely new way, and develop a solid data strategy that is continuously effective. This book also covers ethics and legal issues surrounding data collection and algorithmic bias. This second edition includes case studies, tools for project assessment, and expanded content for hiring and managing data scientists.

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.

DEA is a popular, effective method that can be used to measure productive efficiency in operations management assessment. Emphasizing the importance of analyzing productivity by measuring inputs, goals, economic growth, and performance, this book covers a wide breadth of DEA innovative knowledge.

Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions.

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.

Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. This book demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result. It examines a variety of spatial diagnostics and models through a series of applied examples, such as spatial lag models, spatial error models, and models that incorporate spatial heterogeneity in the effects of covariates. This book also covers advanced spatial models for time-series cross-sectional data, categorical and limited dependent variables, count data, and survival data.

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.