This course aims to compensate a missing dimensionin Data Science/Machine Learning studies addressing the analysis of data whichchanges over time, that is time-series. It will provide students with the toolsfor analyzing time-series data. The course start by building a background onrandom/stochastic processes and frequency transforms. We will then discussparametric process models for time-series such as AR, ARMA, etc and provideclassical estimation methods. We will then extend the discussion to predictionand forecasting. Unlike classical courses on Time-Series Analysis we will coveralso non-stationary time series, introducing methods and transforms. We willalso extend multivariate analysis to graph time series. We aim to present alsoimplementations using R or Python.