Course 1: Data Science Foundations

The course “Data Science Foundations” covers multivariate statistics and (mean-covariance) linear factor models, which are the basis for more complex machine learning/artificial intelligence models.

In particular, this course covers the following topics:

• Multivariate distributions and notable classes: elliptical, exponential, discrete, mixtures
• The “mean-covariance/linear” ecosystem: mean vector, covariance matrix, ellipsoid, affine equivariance, correlation, linear prediction, whitening
• Estimation of the “mean-covariance/linear” ecosystem: historical, maximum likelihood, Bayesian, random matrix theory and shrinkage
• Linear factor models: regression, principal component analysis, factor analysis, cross-sectional models

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.