Big Data for Macroeconomic Statistics
Source details
- Canonical URL
- Big Data for Macroeconomic Statistics
Other formats
Bibliographic details
- Session: CT 26.01
- Location: Shenzhen, China
- Dates: April 13-17, 2026 (1 week)
- Delivery method: In-person Training
- Primary language: English
- Status: Deadline passed
Target audience and qualifications
- Target audience:
- Technical staff of national statistics offices and central banks (economists or statisticians, and data scientists) working on macroeconomic statistics, nowcasting, and data science projects.
- Qualifications:
- Participants are expected to have a degree in economics, statistics, data science or equivalent experience.
Course logistics
- Session No.: CT 26.01
- Location: Shenzhen, China
- Date: April 13-17, 2026 (1 week) New dates
- Delivery Method: In-person Training
- Primary Language: English
- Registration status: Deadline passed
Course description
- Presented by the Statistics Department.
- Aimed at country officials from national statistics offices (NSOs) and central banks seeking knowledge and skills to leverage Big Data and advanced technologies for generating high-frequency and detailed statistics to enhance macroeconomic monitoring and research in the region.
- AfDB and IMF experts will deliver lectures and hands-on workshops covering various Big Data applications pertinent to macroeconomic statistics, offering insights from both producer and user standpoints.
- Participants will present and discuss their use cases, engage in discussions, and benefit from shared insights and experiences.
- The workshop is conducted jointly by staff of the AfDB Data Innovation Lab and the IMF Big Data Center.
Core instructional themes and tools
- Big Data architecture:
- Data extraction, preprocessing, and visualization.
- Machine learning for macroeconomic monitoring:
- Modeling, variable selection, performance analysis, and model selection for GDP nowcasting and trade monitoring (based on the IMF's PortWatch platform).
- Geospatial and satellite data analysis:
- Practical skills in the use of Google Earth Engine (GEE), Dynamic World, Jupyter Notebook and other geospatial and data science packages to analyze satellite data and generate high-frequency macroeconomic statistics.
- Natural language processing (NLP):
- Carry out sentiment analysis with NLP technologies to support macroeconomic analysis.
- Application to official statistics:
- Application of Big Data technologies and resources to improve timelines and granularity of official statistics.
- Peer learning and collaboration:
- Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest.
Course objectives (upon completion)
- Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in their various organizations.
- Demonstrate a practical knowledge of machine learning modeling, variable selection, performance analysis as well as model selection, for GDP nowcasting and trade monitoring (based on the IMF's PortWatch platform).
- Demonstrate practical skills in the use of Google Earth Engine (GEE), Dynamic World, Jupyter Notebook and other geospatial and data science packages to analyze satellite data and generate high-frequency macroeconomic statistics.
- Carry out sentiment analysis with natural language processing (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.
- Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest.
References