Background

There is a need for reliable data to support planning, budgeting, and evaluation of activities essential for prudent economic management. To be useful, such data must be accurate, timely, disaggregated, and widely accessible. Robust statistical systems that support policy development and enhance accountability enable stakeholders to freely exchange high-quality data, ensuring that funding and development efforts are effective.

Nowhere is the need for better data more urgent than in the MEFMI region. There have been gains in the frequency and quality in the past because all MEFMI member countries, apart from Namibia (in Special Data Dissemination Standard (SDDS)), participate in Enhanced General Data Dissemination System (e-GDDS). However, the building blocks for statistical systems in the MEFMI region remain weak.

The region still faces significant data-related challenges, including data quality issues, limited data access and infrastructure, lack of skilled data professionals, and cybersecurity concerns. These challenges hinder the nations’ ability to leverage digitalisation and innovation for informed decision-making, economic growth, and improved service delivery.

It is against this background that MEFMI will conduct a virtual regional workshop on introduction to Data Science for Macroeconomic Statistics to assist member countries to attain technological advancements and innovation in statistics collection, processing and dissemination.

Objectives

The primary objective of the course is to assist participants to explore how big data and machine learning techniques may be used in the compilation of high frequency indicators.

Course Content

The course will cover the following:

• Scalable Big Data Systems: Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in their various institutions.

• Machine Learning: Developing skills in machine learning data modelling.

• Implement natural language processing (NLP) for Sentiment Analysis: Carry out sentiment analysis with NLP technologies to support macroeconomic analysis.

• Demonstrate the application of these Big Data technologies and resources to improve timelines and granularity of their official statistics.

• Collaboration and Knowledge Sharing: Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest; and

• Artificial Intelligence technologies in macroeconomic statistics.

Target Group

Junior-to-mid-level officials in ministries of finance/ economic planning responsible for economic modelling and policy analysis. The course is also relevant for central banks and statistics officials dealing with issues related to policy analysis.