Big Data: New Opportunities in Economic and Statistical Analysis


01 - 01 Jan, 1970


To Be Determined;

Why Attend?

Big data and data science are transforming central banking. Vast quantities of data available in near-real time offer decision-makers unparalleled opportunities for analysis.

Big data empowers researchers to explore and understand complex statistical as well as economic challenges. For supervisors advanced data science can deliver an unprecedented view into the financial system.

Yet, transformation is not without cost and risk. New approaches to data are resource intensive and new techniques require care and calibration. The challenge for central bankers is to integrate innovation with existing practices in a way that adds value for policy-makers and stakeholders.

course-obj_img Course Objectives

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    By the end of the course, participants will be able to:

    • Understand the opportunities and limitations of big data
    • Gain insight into the application of cloud technology in policy-making
    • Identify areas where big data and data science can improve operations
    • Understand the requirements for a framework for data governance
    • Use new tools and techniques for visualising new data sets and networks

course-obj_img 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

1970 - Course Type & Date

DATE: 01 - 01 Jan, 1970

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