Data Science, Analytics and Machine Learning with R

ISBN-13: 9780128242711 / Angielski / Miękka / 2023

Luiz Paulo Favero; Patricia Belfiore; Rafael de Freitas Souza

Spis treści

Part 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