1. Visualization of univariate data (density plots, histograms, q-q plots, empirical CDF, box and whisker plots, barplots)
2. Visualization of bivariate data (scatter plots and simple regression)
3. Visualization of multi-dimensional data, heatmaps, contourplots, PCA, LDA.
4. Visualization of time series data, interpolation, smoothing, periodograms, spectrograms, autocorrelation.
5. Linear regression.
6. Modelling of time-series data (ARMA and ARIMA)
7. Generalized linear models.
8. Support vector machines.
9. Convolutional neural networks.
10. Unsupervised models - K-means.
11. Unsupervised models - GMM
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge