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