## Big Data for Macroeconomic Statistics (BDMS)

## Source details

**Canonical URL:** [Big Data for Macroeconomic Statistics (BDMS)](https://www.imf.org/en/capacity-development/training/icdtc/courses/bdms)

## Other formats

- [Markdown version](/en/capacity-development/training/icdtc/courses/bdms/index.md)
- [Structured JSON version](/en/capacity-development/training/icdtc/courses/bdms/index.json)
- [Bundle manifest](/en/capacity-development/training/icdtc/courses/bdms/bundle-manifest.json)

## Bibliographic details
- Published: July 26, 2025

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## Upcoming offerings

| Start date | End date | Location | Delivery Method | Session No. | Primary & (Interpretation) language | Apply |
| --- | --- | --- | --- | --- | --- | --- |
| October 26, 2026 | October 30, 2026 | Ebene, Mauritius | [[[In-person Training](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods) | [AT 26.27](https://www.imf.org/en/capacity-development/training/icdtc/schedule/at/2026/bdmsat26-27) | English (French, Portuguese) | Deadline passed |
| November 2, 2026 | November 6, 2026 | Singapore, Singapore | [[[In-person Training](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods) | [ST 26.24](https://www.imf.org/en/capacity-development/training/icdtc/schedule/st/2026/bdmsst26-24) | English | Deadline passed |
| December 14, 2026 | December 17, 2026 | Abu Dhabi, United Arab Emirates | [[[In-person Training](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods) | [CE 26.46](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/bdmsce26-46) | English (Arabic) | [Apply online](https://www-ins.imf.org/TAS/signon.aspx?pkey=CE26.46E) by November 6, 2026 |

### Overview
- Workshop aimed at country officials from national statistics offices (NSOs) and central banks to acquire 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.
- Delivered by data scientists of the IMF Big Data Center through lectures and hands-on workshops.
- Offers insights from both producer and user standpoints.
- Participants present and discuss use cases, engage in discussions, and benefit from shared insights and experiences.
- Topic: Macroeconomic Statistics

### Target audience
- Technical staff and managers from national statistics offices and central banks involved in macroeconomic statistics, nowcasting, and data science projects.
- Suitable for economists, statisticians, data scientists, and decision-makers overseeing related projects or teams.

### Course objectives
Upon completion, participants should be able to:
- 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 textual 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.
- Gain insights into setting up effective data science teams and developing institutional Big Data strategies to support innovation for macroeconomic statistics.

### Delivery and core topics
- Combination of lectures and hands-on workshops led by IMF Big Data Center data scientists.
- Practical applications relevant to macroeconomic statistics, including GDP nowcasting and trade monitoring via the IMF's PortWatch platform.
- Geospatial analysis using Google Earth Engine (GEE), Dynamic World, and Jupyter Notebook.
- Textual analysis using natural language processing (NLP).
- Emphasis on both technical implementation (data extraction, preprocessing, visualization) and institutional practices (team setup, strategy development).

### Expected competencies and institutional outcomes
- Ability to set up and support organizational Big Data architectures for macroeconomic statistics.
- Practical machine learning and model evaluation skills applicable to nowcasting and monitoring.
- Capability to produce high-frequency, geospatially informed macroeconomic indicators from satellite data.
- Competence in NLP-based textual analysis to enrich macroeconomic monitoring and research.
- Strengthened inter-agency collaboration and peer-learning on Big Data projects.
- Foundations for institutional Big Data strategies and effective data science teams to support innovation in official statistics.

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## References

- [In-person Training](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)
- [AT 26.27](https://www.imf.org/en/capacity-development/training/icdtc/schedule/at/2026/bdmsat26-27)
- [ST 26.24](https://www.imf.org/en/capacity-development/training/icdtc/schedule/st/2026/bdmsst26-24)
- [CE 26.46](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/bdmsce26-46)
- [Apply online](https://www-ins.imf.org/TAS/signon.aspx?pkey=CE26.46E)

_Source: https://www.imf.org/en/capacity-development/training/icdtc/courses/bdms_