Description
Book SynopsisThe textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.
Table of Contents1. Introduction2. Introduction to learning from data3. Part 1: General topics4. Prediction models5. Error measures6. Resampling7. Data types8. Part 2: Core methods9. Maximum Likelihood & Bayesian analysis10. Clustering11. Dimension Reduction12. Classification13. Hypothesis testing14. Linear Regression15. Model Selection16. Part 3: Advanced topics17. Regularization18. Deep neural networks19. Multiple hypothesis testing20. Survival analysis21. Generalization error22. Theoretical foundations23. Conclusion.
