Luiz Paulo Favero, Patricia Belfiore

Data Science for Business and Decision Making (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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®.

• 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 1240
• Erscheinungstermin: 11. April 2019
• Englisch
• ISBN-13: 9780128112175
• Artikelnr.: 51227882

Autorenporträt

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.

Inhaltsangabe

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