Data Science for Business and Decision Making

ISBN-13: 9780128112168 / Angielski / Miękka / 2019 / 1240 str.

Luiz Favero; Patricia Favero

Recenzje

"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

Spis treści

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