20 methods of the data scientist and the mathematics behind them

Contents

• 1. Relational databases and their algorithms
• 2. Static and dynamic summary statistics
• 3. Analysis of univariate distributions
• 4. Analysis of multivariate distributions
• 5. Analysis of covariance and correlation
• 6. Finding the best line through a cloud of points
• 7. General least squares
• 8. Logistic regression and GLM
• 9. Clustering (k-means)
• 10. PCA and data-reduction
• 11. Gradient Descent
• 12. Kalman Filtering
• 13. Textual and categorical classification
• 14. Convolutions filtering
• 15. Fourier analysis of signals
• 16. Deep neural networks
• 17. Data compression
• 18. Dynamic programming and reinforcement learning
• 19. Linear programming
• 20. Scheduling, Knapsack and Integer programming