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)
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
Use in data science:
Applicable mathematics:
20. Scheduling, Knapsack and Integer programming
20 methods of the data scientist and the mathematics behind them
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19. Linear programming
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19. Linear programming
¶
Use in data science:
¶
Variable selection
Large scale operational optimization
Applicable mathematics:
¶
Linear algebra
Discrete mathematics and combinatorics
Analysis
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