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
Use in data science:
Applicable mathematics:
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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11. Gradient descent
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11. Gradient descent
¶
Use in data science:
¶
Part of many learning algorithms
General optimization
Applicable mathematics:
¶
Multi variable calculus
Optimization theory
Linear algebra
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