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Introduction to Business Analytics

Introduction to Business Analytics

Troy Adair

ISBN: 978-1-119-13191-5

Oct 2019

896 pages

Select type: E-Book

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Description

This book features a pedagogical approach that aims to teach the discovery and communication of meaningful patterns in data.  The author stresses the importance of how to best identify, collect, organize and import data; how to present the results of the analysis in a logical and compelling manner; and how to perform the statistical analysis.  Introduction to Business Analytics combines a pragmatic expositional style that focuses on understanding the level of analysis readers should be capable of as well as understanding what types of situations and data issues may require them to engage specialized assistance.  Utilizing a conversational style and Microsoft Excel 2013, each chapter begins with a business analytics scoping exercise arrayed around an end-user needs assessment of an actual business case drawn from industry, which helps to not only develop the necessary analytics and visualization tools, but also culminates in a summary of the case findings and their recommended visualization format.  All analysis examples are supported by illustrative screen captures, all of which are linked to associated videos available via the book’s related website.  “Engage an Expert” guidance boxes are interspersed throughout each chapter to identify potential data issues that might affect the analysis; how to test for them; and what types of specialized analyses/resources are available for further study.  The book is divided into five sections: Introduction; Descriptive Analytics; Predictive Analytics; Prescriptive Analytics; and Extended Visualization.  Topical coverage includes: Introduction to Business Analytics; Basic Excel and Access Skills; Data Structure and Considerations; Building a Data Warehouse in Access; Accessing the Data Warehouse from Excel; Univariate Analysis; Bivariate Analysis; Checking for Differences: ANOVA; Uncertainty Analysis; Linear Regression Model; Logistic Regression and other Discrete Choice Models; Time Series Analysis; Survival/Duration Analysis; Discriminant Analysis; Linear Optimization and Solver; Machine Learning; Simulation; Risk Analysis; Optimization; Advanced Visualization; Dashboarding; and Contextual Data Modeling.

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