Data Science for Business and Decision Making - Luiz Paulo Favero

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Auteur(s) : Luiz Paulo Favero - Patricia Belfiore

Editeur : Elsevier Science

Langue : Anglais

Parution : 01/04/2019

Format : Moyen, de 350g à 1kg

Nombre de pages : 1244

Expédition : 2416

Dimensions : 27.4 x 21.6 x 6.1

ISBN : 9780128112168

Résumé :

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?.

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Biographie:
Dr. F?vero 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 Get?lio 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, Get?lio 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. F?vero 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 Get?lio 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, Get?lio 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....

Sommaire:

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