The course is aimed at economists and statisticians from central banks who are interested in seasonal adjustment and, in particular, in the application of JDemetra+. Prior knowledge and/or experience of time series analysis in general and seasonal adjustment in particular are welcome but are not a prerequisite for participation.

The expert panel is aimed at central bankers as well as regulators and supervisors with an interest in helping develop best practices in big data mining and management. Participants will be expected to make an active contribution to the discussions and will be invited to present and discuss current issues in machine learning, artificial intelligence and big data in their own jurisdiction.

The course is designed for staff working in information technology, statistics or research departments in central banks and regulatory and supervisory authorities with an interest in applying machine learning methods with Python. It is not aimed at those who already have extensive and advanced knowledge of Python.

Junior to mid-level central bank officials working in the communication department or economics/monetary policy department, especially in central banks under or moving towards an inflation targeting regime, and officials from central banks whose responsibilities require, or who have a strong interest in acquiring, a thorough understanding of central bank communication.

The course is aimed at data-savvy central bankers, regulators and supervisors in areas such as information technology and statistics, or research departments interested in implementing machine learning methods.

Mid-level to senior banking supervisors from central banks and supervisory authorities. Basic knowledge of the Basel framework is essential. Participants should also be familiar with the supervisory policies at their institution and be prepared to actively contribute to discussions.