Data Science Foundations

The course "Data Science Foundations" covers fundamental notions of multivariate statistics that are necessary to absorb the Topics of the ARPM Certification. In particular, this course covers the following topics:

• An introduction to univariate and multivariate distributions
• Notable classes of multivariate distributions: elliptical, categorical, exponential, and more
• The theory of copulas and tips for practical implementations, which enhance the flexibility of our models for multivariate randomness
• The definition and properties of locations and dispersion features, most notably expectation and covariance, stressing their connections with all branches of mathematics
• Linear factor models, which are omnipresent in finance, and lay at the foundation of all machine learning/artificial intelligence models.

For a detailed list of topics, refer to the Syllabus here below (expand the dropdown).

This training will help you to achieve a twofold goal:

• Goal 1: strengthen your existing knowledge of the quantitative models and techniques covered by the Data Science Foundations course
• Goal 2: lay out the foundations to interact with a group of colleagues across geographies and departments, using a common language and notation.

You can choose to complete an optional project at the end of this course that will count as the Practical Project towards the attainment of the ARPM Certification.