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Data Science for Business and Decision Making

  • Book
  • June 2019
  • Elsevier Science and Technology
  • ID: 4465281

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®.

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

Authors

Luiz Paulo Favero Economics, Business Administration and Accounting College of the University of Sao Paulo, Brazil/ Faculdade de Economia, Administracao e Contabilidade, Universidade de Sao Paulo, Brazil. 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. Patricia Belfiore Associate Professor, Federal University of ABC (UFABC)/ Federal University of ABC, Brazil. 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.