Five theory courses and two labs. Tap any card to drill into its modules and topics.
Mathematical and statistical building blocks used across data science: discrete math, graphs, linear algebra, vector geometry, and probability.
The data-science life cycle: collection, pre-processing, exploration, modelling and evaluation — plus core algorithm-design techniques.
Foundations of machine learning across supervised, unsupervised and reinforcement learning, ending with an introduction to neural networks.
Predictive modelling: process, regression and classification performance, discriminant methods, ROC analysis, and tree/rule ensembles.
Computational tools applied to data: statistics, Fourier and wavelet analysis, image processing, SVD/PCA/ICA, image recognition and compressed sensing.
Hands-on lab to master the R interactive environment for data manipulation, statistics and visualization.
Python programming for data analysis and machine learning — from basic data structures to classifiers and visualization.