Data Science for Business and Decision Making
By: Luiz Favero, Patrícia Belfiore
About This Book
Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®.
AI Overview
Title: "Data Science for Business and Decision Making" by Luiz Paulo Fávero and Patrícia Belfiore
Overview: The book "Data Science for Business and Decision Making" by Luiz Paulo Fávero and Patrícia Belfiore is a comprehensive guide that covers both statistics and operations research, setting it apart from many competing textbooks that focus on one or the other. The book aims to equip readers with the necessary tools and knowledge to apply data science effectively in business and decision-making contexts.
Key Themes:
Foundations of Business Data Analysis:
- The book begins with an introduction to data analysis and decision-making, emphasizing the hierarchy between data, information, and knowledge.
- It covers various types of variables, including nonmetric (qualitative) and metric (quantitative) variables, and their respective scales of measurement (nominal, ordinal, interval, and ratio scales).
Descriptive Statistics:
- Part II of the book delves into descriptive statistics, including tests for quantitative data, such as the Friedman test.
Integration of Statistics and Operations Research:
- The book integrates statistical methods with operations research techniques, providing a holistic approach to data science in business decision-making.
Applications in Business:
- The authors illustrate the practical applications of data science in various business contexts, helping readers understand how to translate theoretical concepts into actionable insights.
Plot Summary: The book is structured into several parts, each focusing on a different aspect of data science for business and decision-making. The narrative flows from foundational concepts to more advanced statistical and operational techniques, culminating in practical applications.
Part I: Foundations of Business Data Analysis
- This part introduces readers to the basics of data analysis and decision-making, laying the groundwork for the rest of the book.
Part II: Descriptive Statistics
- This section covers various statistical tests and methods for analyzing quantitative data.
Part III: Advanced Topics and Applications
- This part delves into more advanced topics, integrating statistical methods with operations research techniques to provide a comprehensive approach to data science in business.
Critical Reception: While specific reviews are not provided in the sources, the book's comprehensive coverage of both statistics and operations research suggests it would be well-received by both academics and practitioners in the field. The inclusion of practical applications and exercises further supports its utility in educational settings and professional development programs.
Edition and Publication Details: The book was published in 2019, with the first edition available. It includes bibliographical references and an index, making it a valuable resource for further study and research.