Course 3: Data Science for Finance

The course "Data Science for Finance" covers the pillars of quantitative statistics for finance (the "P" in ARPM), namely the tools needed to model and estimate the joint dynamics of the markets. In particular, this course covers the following topics:

• Univariate and multivariate econometrics, that allows us to analyze time series and model the dynamics of the markets by means of suitable discrete time stochastic processes
• Estimation techniques: non-parametric, maximum likelihood (with missing data), robust and Bayesian estimation.

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 techniques covered by the Data science for finance 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.