Course Objectives

• This Course Objective Hasn't Been Provided Yet

Target Audience

This course is designed to equip central bankers to meet these challenges.

Course introduction

• Introductions and welcome from the chair
• Overview of the training course and key themes
• Discussion of delegate expectations and particular areas of interest

Overview of new data sources in economics and finance

• Big data and central banking – purpose and use
• Fintech, data never sleeps
• Quality and transparency of new data sources

How to assess trustworthy AI in practice

• The ethical and societal implications of artificial intelligence systems
• Introduction to a novel process based on applied ethics - Z-Inspection®
• Assessing if an AI system is trustworthy in practice

Machine learning and statistics: variations on a theme

• Machine learning and statistics
• Classifying data-analysis methodologies
• What are the limits of our predictive capacity?
• Pitfalls and hidden strengths of machine learning methods

Making sense of big data

• Working with big data
• Opportunities for central banks
• Organising big data work
• Challenges & policy issues with handling and using big data

Text mining: applications in economic analysis (and text analysis)

• Taxonomy of data derived from textual databases
• Overview of tools and methods for their systematic analysis
• Examples of applications in predictive models
• Case study: textual analysis for monitoring macroeconomic developments

Applying data science in economics and finance

• Data science models for large datasets
• Using predictive models in macroeconomics
• Case study: working with big data, models, software and examples from Bank of Italy

Visualisation: new tools and techniques for visualising new data sets

• Why is good data visualisation and storytelling important?
• Overview of best practice techniques
• Examples of improved communication of financial data using these techniques and technology
• Discussion: where are the opportunities for central bank communication?

AI and ML implications for data management and analytics

• Current capabilities of AI and machine learning
• Data management, processing and analysis
• Examples of machine learning based software solutions for the regulators and the regulated
• Discussion: what are the best opportunities in AI and machine learning

The principles for successful data governance

• Opportunities: identify the benefits of data governance for your organisation.
• Capability: set yourself up for success by ensuring that you have the right resources and knowledge.
• Custom-build: design a Data Governance Framework which is tailormade to your organisation.
• Simplicity: avoid complexity and make it easy to embed Data Governance.
• Launch: implement on an iterative basis and start to see the benefits of your work.
• Evolve: develop your framework as your organisation evolves to make further gains

Advanced statistical analysis of large-scale web-based data

• Data science methods for big data
• Case study using social media data
• Challenges and learnings

Cloud and data management: innovation and opportunities for central banks

• Examples of cloud applications in today's central banking environment, advantages and disadvantages
• Importance of cloud computing for data science and big data analytics
• Discussion: what does it take to effectively manage limitations and potential legal and security risks?

Closing remarks and delegate action plans

Concluding session led by the chair

• Summary of the course
• Discussion of the observed trends and case studies
• Application of learning points in the delegates’ home organisations
• Preparation of action points

This course hasn't been scheduled yet, however you can request for In-House training