{
  "title": "Big Data for Macroeconomic Statistics (BDMS) — Training Program",
  "sourceUrl": "https://www.imf.org/en/capacity-development/training/icdtc/schedule/pt/2026/bdmspt26-03",
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  "summary": "Big Data for Macroeconomic Statistics Training Program Big Data for Macroeconomic Statistics (BDMS) Invitation Session No.",
  "publishDate": "2025-10-28",
  "sections": [
    {
      "heading": "Overview",
      "content": "- Event: Big Data for Macroeconomic Statistics (BDMS), Session No.: PT 26.03.\n- Location: Asuncion, Paraguay.\n- Date: May 4-8, 2026 (1 week).\n- Delivery Method: In-person Training.\n- Primary Language: English.\n- Interpretation Language: Spanish."
    },
    {
      "heading": "Target Audience & Qualifications",
      "content": "- Intended participants: technical staff and managers from national statistics offices (NSOs) and central banks involved in macroeconomic statistics, nowcasting, and data science projects.\n- Suitable roles: economists, statisticians, data scientists, and decision-makers overseeing related projects or teams."
    },
    {
      "heading": "Course Description & Delivery",
      "content": "- Organizer/instructor: data scientists of the IMF Big Data Center.\n- Format: lectures and hands-on workshops.\n- Emphasis: applications of Big Data and advanced technologies to generate high-frequency and detailed statistics to enhance macroeconomic monitoring and research in the region.\n- Participant engagement: opportunities to present and discuss use cases, engage in discussions, and benefit from shared insights and experiences."
    },
    {
      "heading": "Course Objectives",
      "content": "- Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in participants' organizations.\n- Demonstrate practical knowledge of machine learning modeling, variable selection, performance analysis, and model selection for GDP nowcasting and trade monitoring (based on the IMF's \"PortWatch\" platform).\n- 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.\n- Carry out textual analysis with natural language processing (NLP) technologies to support macroeconomic analysis.\n- Demonstrate the application of these Big Data technologies and resources to improve timelines and granularity of official statistics.\n- Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest.\n- Gain insights into setting up effective data science teams and developing institutional Big Data strategies to support innovation for macroeconomic statistics."
    },
    {
      "heading": "Skills, Tools, and Thematic Coverage",
      "content": "- Machine learning for GDP nowcasting and trade monitoring, including variable selection, performance analysis, and model selection.\n- Geospatial analysis using Google Earth Engine (GEE), Dynamic World, and related packages.\n- Jupyter Notebook-based hands-on workflows for data preprocessing, analysis, and visualization.\n- Natural language processing (NLP) techniques for textual analysis to inform macroeconomic indicators.\n- Big Data architecture components: data extraction, preprocessing, visualization, and institutional strategy for data science teams."
    },
    {
      "heading": "Logistics & Funding",
      "content": "- Travel, accommodation, and per diem costs: to be arranged and funded by participants' sponsoring agencies."
    }
  ],
  "bullets": [
    "Event: Big Data for Macroeconomic Statistics (BDMS), Session No.: PT 26.03.",
    "Location: Asuncion, Paraguay.",
    "Date: May 4-8, 2026 (1 week).",
    "Delivery Method: In-person Training.",
    "Primary Language: English.",
    "Interpretation Language: Spanish.",
    "Intended participants: technical staff and managers from national statistics offices (NSOs) and central banks involved in macroeconomic statistics, nowcasting, and data science projects.",
    "Suitable roles: economists, statisticians, data scientists, and decision-makers overseeing related projects or teams.",
    "Organizer/instructor: data scientists of the IMF Big Data Center.",
    "Format: lectures and hands-on workshops.",
    "Emphasis: applications of Big Data and advanced technologies to generate high-frequency and detailed statistics to enhance macroeconomic monitoring and research in the region.",
    "Participant engagement: opportunities to present and discuss use cases, engage in discussions, and benefit from shared insights and experiences.",
    "Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in participants' organizations.",
    "Demonstrate practical knowledge of machine learning modeling, variable selection, performance analysis, and 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 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.",
    "Machine learning for GDP nowcasting and trade monitoring, including variable selection, performance analysis, and model selection.",
    "Geospatial analysis using Google Earth Engine (GEE), Dynamic World, and related packages.",
    "Jupyter Notebook-based hands-on workflows for data preprocessing, analysis, and visualization.",
    "Natural language processing (NLP) techniques for textual analysis to inform macroeconomic indicators.",
    "Big Data architecture components: data extraction, preprocessing, visualization, and institutional strategy for data science teams.",
    "Travel, accommodation, and per diem costs: to be arranged and funded by participants' sponsoring agencies."
  ],
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  "generatedAtUtc": "2026-09-29T19:29:59.538Z"
}