Course 2: Advanced Data Science

The course "Advanced Data Science" provides an in-depth introduction to machine learning/artificial intelligence models and techniques, as a generalization of linear factor models. In particular, this course covers the following topics:

• Machine learning models for static and dynamic, point and probabilistic prediction, including regression, classification, autoencoders, graphical models, time series filters, state space models, and more
• Estimation risk assessment and mitigation via Monte Carlo simulations and cross-validation
• Enhancements of machine learning models in terms of mean-variance trade-off: feature bases, trees, neural networks, gradient boosting, lasso/ridge regularization, random forests, and more
• Generalized inference, to infer how statements/views on some variables affect our predictions.

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 Advanced Data Science 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.