20 methods of the data scientist and the mathematics behind them
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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)
Use in data science:
Applicable mathematics:
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
20 methods of the data scientist and the mathematics behind them
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9. Clustering (k-means)
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9. Clustering (k-means)
¶
Use in data science:
¶
Unsupervised learning
Population segmentation
Applicable mathematics:
¶
Vectors and norms
Iterative algorithms and approximations
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