Data Science, Analytics and Machine Learning with R

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