Course 2: Advanced Data Science

The course “Advanced Data Science” provides an in-depth discussion of machine learning/artificial intelligence models and their applications to quantitative finance.

In particular, this course covers the following topics:

• Machine learning models for point and probabilistic prediction, including supervised regression and classification, unsupervised autoencoders and graphical models
• Decision theory and estimation risk assessment via cross-validation and Monte Carlo: variance /bias tradeoffs
• Feature engineering and enhancements: feature bases, trees, neural networks, gradient boosting, lasso/ridge regularization, random forests etc
• Financial applications of machine learning models for hedging, credit default and clustering

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

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.