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Data Science for Business and Decision Making - 1st Edition - ISBN: 9780128112168, 9780128112175

Data Science for Business and Decision Making

1st Edition

5.0 star rating 11 Reviews
Authors: Luiz Paulo Fávero Patrícia Belfiore
Paperback ISBN: 9780128112168
eBook ISBN: 9780128112175
Imprint: Academic Press
Published Date: 22nd April 2019
Page Count: 1240
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Description

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®.

Key Features

  • Combines statistics and operations research modeling to teach the principles of business analytics
  • Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business
  • Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Readership

Upper-division undergraduates and graduate students worldwide working on business decision-making. This book will help them with statistics, particularly optimization and multivariate modeling, and their manipulation through the use of Excel, SPSS, and Stata

Table of Contents

Part 1: Foundations of Business Data Analysis
1. Introduction to Data Analysis and Decision Making
2. Type of Variables and Mensuration Scales

Part 2: Descriptive Statistics
3. Univariate Descriptive Statistics
4. Bivariate Descriptive Statistics

Part 3: Probabilistic Statistics
5. Introduction of Probability
6. Random Variables and Probability Distributions

Part 4: Statistical Inference
7. Sampling
8. Estimation
9. Hypothesis Tests
10. Non-parametric Tests

Part 5: Multivariate Exploratory Data Analysis
11. Cluster Analysis
12. Principal Components Analysis and Factorial Analysis

Part 6: Generalized Linear Models
13. Simple and Multiple Regression Models
14. Binary and Multinomial Logistics Regression Models
15. Regression Models for Count Data: Poisson and Negative Binomial

Part 7: Optimization Models and Simulation
16. Introduction to Optimization Models: Business Problems Formulations and Modeling
17. Solution of Linear Programming Problems
18. Network Programming
19. Integer Programming
20. Simulation and Risk Analysis

Part 8: Other Topics
21. Design and Experimental Analysis
22. Statistical Process Control
23. Data Mining and Multilevel Modeling

Details

No. of pages:
1240
Language:
English
Copyright:
© Academic Press 2019
Published:
22nd April 2019
Imprint:
Academic Press
Paperback ISBN:
9780128112168
eBook ISBN:
9780128112175

About the Authors

Luiz Paulo Fávero

Dr. Fávero is a Full Professor at the Economics, Business Administration and Accounting College and at the Polytechnic School of the University of Sao Paulo (FEAUSP and EPUSP), where he teaches Data Science, Data Analysis, Multivariate Modeling, Machine and Deep Learning and Operational Research to undergraduate, Master’s and Doctorate students. He has a Post-Doctorate degree in Data Analysis and Econometrics from Columbia University in New York. He is a tenured Professor by FEA/USP (with greater focus on Quantitative Modeling). He has a degree in Engineering from USP Polytechnic School, a post-graduate degree in Business Administration from Getúlio Vargas Foundation (FGV/SP), and he has received the titles of Master and PhD in Data Science and Quantitative Methods applied to Organizational Economics from FEA/USP. He is a Visiting Professor at the Federal University of Sao Paulo (UNIFESP), Dom Cabral Foundation, Getúlio Vargas Foundation, FIA, FIPE and MONTVERO. He has authored or co-authored 9 books and he is the founder and former editor-in-chief of the International Journal of Multivariate Data Analysis. He is member and founder of the Latin American Academy of Data Science. He is a consultant to companies operating in sectors such as retail, industry, mining, banks, insurance and healthcare, with the use of Data Analysis, Machine and Deep Learning, Big Data and AI platforms, such as R, Python, SAS, Stata and IBM SPSS. Dr. Fávero is a Full Professor at the Economics, Business Administration and Accounting College and at the Polytechnic School of the University of Sao Paulo (FEAUSP and EPUSP), where he teaches Data Science, Data Analysis, Multivariate Modeling, Machine and Deep Learning and Operational Research to undergraduate, Master’s and Doctorate students. He has a Post-Doctorate degree in Data Analysis and Econometrics from Columbia University in New York. He is a tenured Professor by FEA/USP (with greater focus on Quantitative Modeling). He has a degree in Engineering from USP Polytechnic School, a post-graduate degree in Business Administration from Getúlio Vargas Foundation (FGV/SP), and he has received the titles of Master and PhD in Data Science and Quantitative Methods applied to Organizational Economics from FEA/USP. He is a Visiting Professor at the Federal University of Sao Paulo (UNIFESP), Dom Cabral Foundation, Getúlio Vargas Foundation, FIA, FIPE and MONTVERO. He has authored or co-authored 9 books and he is the founder and former editor-in-chief of the International Journal of Multivariate Data Analysis. He is member and founder of the Latin American Academy of Data Science. He is a consultant to companies operating in sectors such as retail, industry, mining, banks, insurance and healthcare, with the use of Data Analysis, Machine and Deep Learning, Big Data and AI platforms, such as R, Python, SAS, Stata and IBM SPSS.

Affiliations and Expertise

Economics, Business Administration and Accounting College of the University of Sao Paulo, Brazil/ Faculdade de Economia, Administracao e Contabilidade, Universidade de Sao Paulo, Brazil

Patrícia Belfiore

Dr. Belfiore is Associate Professor at the Federal University of ABC (UFABC), where she teaches Data Science, Statistics, Operational Research, Production Planning and Control, and Programming and Algorithms Development to Engineering students. She has a master’s in electrical engineering and a PhD in production engineering from the Polytechnic School of the University of Sao Paulo (EPUSP). She has a post-doctorate degree in Operational Research and Computer Programming from Columbia University in New York. She takes part in several research and consultancy projects in the fields of modeling, optimization and programming. She has taught Operational Research, Multivariate Data Analysis and Operations Research and Logistics to undergraduate and master’s students at FEI University Center and at the Arts, Sciences and Humanities College of the University of Sao Paulo (EACH/USP). Her main research interests are in the fields of modeling, simulation, combinatorial optimization, heuristics and computer programming. She is the author/co-author of 9 books. She is a consultant to companies operating in sectors such as retail, industry, banks, insurance and healthcare, with the use of Process Simulation and Optimization, Data Analysis, and Machine and Deep Learning platforms, such as R, Python, Stata, IBM SPSS and ProModel.

Affiliations and Expertise

Federal University of ABC (UFABC)/ Federal University of ABC, Brazil

Reviews

"Data Science for Business and Decision Making brings together the key topics required as the foundation for understanding and applying analytics for decision making. The authors have carefully selected the topics, and each one is clearly explained, described, and reinforced with a diverse set of exercises." --Rahul Saxena, Cobot Systems

"Data Science for Business and Decision Making provides a thorough essay about statistical methods which are commonly used in business without requiring a strong mathematical background. The presentation is rigorous and accessible thanks to a large number of examples that are developed step-by-step. The illustrations feature various software and the proposed exercises are particularly helpful for students and practitioners." --Francesco Bartolucci, University of Perugia

Ratings and Reviews