Data Science for Business and Decision Making

Business decisions often call for more than one analytical tool. Data Science for Business and Decision Making brings statistics and operations research into the same learning path, moving from foundational data concepts toward models used to investigate and support practical decisions.

From data fundamentals to analytical models

Fávero and Belfiore begin with variable types, measurement, descriptive statistics, and probability. The progression continues through sampling, estimation, and hypothesis testing before turning to multivariate methods and generalized linear models. This sequence helps readers see how core statistical ideas connect to more specialized approaches.

Methods for asking sharper questions

Topics include cluster analysis, principal component and factor analysis, linear and logistic regression, and models for count data. The book also covers optimization and simulation, alongside experimental design, statistical process control, data mining, and multilevel modeling. Together, these subjects give students a wide-ranging introduction to quantitative business analytics.

Examples linked to familiar tools

Practical examples use Excel, Stata, and IBM SPSS, and the book is structured with exercises and answers. That combination makes it useful for readers who want to study the reasoning behind a method and see how analytical work can be carried out with commonly used software.

For students and working analysts

Upper-level undergraduates, graduate students, and professionals developing their business analytics knowledge can use this text to build familiarity across statistical and operations research methods. Its breadth is especially relevant to readers who want to understand how descriptive analysis, inference, modeling, optimization, and simulation contribute to decision-making.

For a course or a personal reference shelf, this is a substantial introduction to the methods that help turn business data into structured analysis.