Deadline for application

09 August 2024

Objective

This course provides an introduction to the application of Python for Data Science and Machine Learning. In recent years, Data science and Machine Learning have had a significant impact on the way Central Banks perform tasks such as analysing markets, assessing risk, anti-money laundering and banking supervision. Python, as one of the leading programming languages for Data Science, plays a crucial role in this area.

In this course, participants will learn how to use Python to analyse data, create machine learning models and support business decisions. A fairly basic introduction to Python will be provided, and following an introduction to the principles, we will move on quite quickly to the application of Python for Data Science and Machine Learning. NumPy, Pandas, Scikit-learn and Matplotlib are the main libraries used in the course, with others appearing  more sporadically. 

Programming tasks and smaller projects will be carried out in practical exercises, independently or in groups, and participants will be encouraged to collaborate with others.

At the end of the course, participants should be able to use Python to analyse data, build models and autonomously extend the knowledge gained so that it can be transferred to tasks and issues in their central banks. To this end, they will also learn where to find resources on the internet that will allow them to independently discover and apply further approaches and methods.

Contents

Introduction to Python

• Basics and fundamental concepts
• Data structures in Python
• Data visualisation with Python

Data preparation and analysis

• Data retrieval and cleaning
• Descriptive statistics in Python

Machine Learning

• Regression and Classification
• Artificial Neural Networks
• Machine learning for central bank tasks

Case studies and projects

• Practical application of data science in central banks
• Project work and programming exercises

Starting on the second day, there will be a Q & A slot before every session, during which participants will have the opportunity to discuss the content of the previous day and the challenges they encountered with the practical exercises.

Target group

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.

Indeed, little prior knowledge is expected, and the necessary Python skills are covered at the beginning, making the course suitable for previously inexperienced staff. It may be interesting for those who wish to switch from the programming language R to Python. 

Please note that we will not discuss the deeper statistical backgrounds of the methods used in any great depth.

Technical requirements

Computer with microphone, camera, speakers, or headphones; an up-to-date internet browser.

Participants should be able to work on their own computers with an existing Python installation (e.g. Anaconda). If necessary, assistance with the installation will be given in advance. Two screens will make participation and coursework much easier.