## 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/schedule/jv/2025/bdmsjv25-33)

## Other formats

- [Markdown version](/en/capacity-development/training/icdtc/schedule/jv/2025/bdmsjv25-33/index.md)
- [Structured JSON version](/en/capacity-development/training/icdtc/schedule/jv/2025/bdmsjv25-33/index.json)
- [Bundle manifest](/en/capacity-development/training/icdtc/schedule/jv/2025/bdmsjv25-33/bundle-manifest.json)

## Bibliographic details
- Session: JV 25.33
- Location: Vienna, Austria
- Dates: October 27-31, 2025 (1 week)
- Delivery method: In-person Training
- Primary language: English
- Status: Deadline passed

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### Key logistics
- Session No.: JV 25.33
- Location: Vienna, Austria
- Date: October 27-31, 2025 (1 week)
- Delivery Method: In-person Training
- Primary Language: English
- Status: Deadline passed

### Target audience
- Technical staff within national statistics offices (NSOs) and central banks, including economists, statisticians, and data scientists working on macroeconomic statistics, nowcasting, and data science projects.

### Course description
- The workshop is presented by the Statistics Department of the IMF and conducted by staff of the IMF Big Data Center.
- Aimed at country officials from national statistics offices (NSOs) and central banks who seek knowledge and skills to leverage Big Data and advanced technologies for generating high-frequency and detailed statistics to enhance macroeconomic monitoring and research.
- IMF experts deliver lectures and hands-on workshops covering various Big Data applications pertinent to macroeconomic statistics, offering insights from both producer and user standpoints.
- Participants present and discuss their use cases, engage in discussions, and benefit from shared insights and experiences.

### Course objectives
- ¿ Build Scalable Big Data Systems: Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in their various organizations.
- ¿ Applying Machine Learning to Economic Forecasting: Develop skills in machine learning modeling, variable selection, performance analysis as well as model selection, for GDP nowcasting and trade monitoring using the IMF¿s PortWatch platform.
- ¿ Utilize Geospatial Analytics: 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.
- ¿ Implement NLP for Sentiment Analysis: Carry out sentiment analysis with natural language processing (NLP) technologies to support macroeconomic analysis.
- ¿ Enhance Official Statistics with Big Data: 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.

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## References

- [In-person Training](https://www.imf.org/en/capacity-development/training/icdtc/delivery-methods)
- [Core Elements of Banking Supervision (CBS)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/at/2027/cbsat27-10)
- [Advanced Government Finance Statistics (GFS-A)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/gfs-ace26-45)
- [Fiscal Sustainability (FS)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/fsce26-38)
- [Safeguards Assessments of Central Banks (SAC)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/at/2026/sacat26-30)
- [Macroeconomic Management in Resource Rich Countries (MRC)](https://www.imf.org/en/capacity-development/training/icdtc/schedule/ce/2026/mrcce26-44)

_Source: https://www.imf.org/en/capacity-development/training/icdtc/schedule/jv/2025/bdmsjv25-33_