Search results for ""Author Patricia Belfiore""
Elsevier Science Publishing Co Inc Data Science for Business and Decision Making
Book SynopsisTrade Review"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 PerugiaTable of ContentsPart 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
£141.30
Elsevier Science Data Science Analytics and Machine Learning with
Book SynopsisTable of ContentsPart I: Introduction 1. Overview of Data Science, Analytics, and Machine Learning 2. Introduction to the R Language Part II: Applied Statistics and Data Visualization 3. Variables and Measurement Scales 4. Descriptive and Probabilistic Statistics 5. Hypotheses Tests 6. Data Visualization and Multivariate Graphs Part III: Data Mining and Preparation 7. Building Handcrafted Robots 8. Using APIs to Collect Data 9. Managing Data Part IV: Unsupervised Machine Learning Techniques 10. Cluster Analysis 11. Factorial and Principal Component Analysis (PCA) 12. Association Rules and Correspondence Analysis Part V: Supervised Machine Learning Techniques 13. Simple and Multiple Regression Analysis 14. Binary, Ordinal and Multinomial Regression Analysis 15. Count-Data and Zero-Inflated Regression Analysis 16. Generalized Linear Mixed Models Part VI: Improving Performance and Introduction to Deep Learning 17. Support Vector Machine 18. CART (Classification and Regression Trees) 19. Bagging, Boosting and Uplift (Persuasion) Modeling 20. Random Forest 21. Artificial Neural Network 22. Introduction to Deep Learning Part VII: Spatial Analysis 23. Working on Shapefiles 24. Dealing with Simple Features Objects 25. Raster Objects 26. Exploratory Spatial Analysis Part VII: Adding Value to your Work 27. Enhanced and Interactive Graphs 28. Dashboards with R
£103.50
