Given this era of the fourth industrial revolution (hereafter, referred to as 4IR) the technological developments we see around us in most industries (such as mining, agriculture, engineering and geospatial science) rely on big data acquisition, processing and analysis in-order to produce end-user products and automated systems. In particular the processing and analysis of big data often require the use or adoption of certain advanced algorithms such as artificial intelligence and machine learning. This remote sensing data analysis course cover the physical principles of satellite remote sensing and remote sensor data processing using machine learning methods. In particular, this includes: (i) examining the characteristics of spectral signatures across regions of the electromagnetic spectrum (EM) spectrum for various surface targets, (ii) criteria for interpretation of remote sensing images, (iii) image pro-processing in R-studio, (vi) supervised classification of images using machine learning methods in R-studio such as the Artificial neural network (ANN), Random forest (RF), Support vector machine (SVM) and Classification and regression trees (CART); followed by verification of classification results by constructing an error assessment matrix. Furthermore course will also cover the comparison between non-parametric linear (Stepwise multiple linear regression) and non-linear (K-NN) regression methods for the estimation or modelling of terrestrial biophysical and/ or biochemical properties.