{
  "title": "Big Data for Macroeconomic Statistics",
  "sourceUrl": "https://www.imf.org/en/capacity-development/training/icdtc/schedule/ct/2026/bdmsct26-01",
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  "summary": "Big Data for Macroeconomic Statistics Training Program Target Audience This course is targeted at technical staff of national statistics offices and central banks (economists or statisticians, and data scientists) working on macroeconomic statistics, nowcasting, and data science projects.",
  "publishDate": "2025-10-24",
  "sections": [
    {
      "heading": "Target audience and qualifications",
      "content": "- Target audience:\n  - Technical staff of national statistics offices and central banks (economists or statisticians, and data scientists) working on macroeconomic statistics, nowcasting, and data science projects.\n- Qualifications:\n  - Participants are expected to have a degree in economics, statistics, data science or equivalent experience."
    },
    {
      "heading": "Course logistics",
      "content": "- Session No.: CT 26.01\n- Location: Shenzhen, China\n- Date: April 13-17, 2026 (1 week) New dates\n- Delivery Method: In-person Training\n- Primary Language: English\n- Registration status: Deadline passed"
    },
    {
      "heading": "Course description",
      "content": "- Presented by the Statistics Department.\n- 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.\n- 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.\n- Participants will present and discuss their use cases, engage in discussions, and benefit from shared insights and experiences.\n- The workshop is conducted jointly by staff of the AfDB Data Innovation Lab and the IMF Big Data Center."
    },
    {
      "heading": "Core instructional themes and tools",
      "content": "- Big Data architecture:\n  - Data extraction, preprocessing, and visualization.\n- Machine learning for macroeconomic monitoring:\n  - Modeling, variable selection, performance analysis, and model selection for GDP nowcasting and trade monitoring (based on the IMF's PortWatch platform).\n- Geospatial and satellite data analysis:\n  - 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- Natural language processing (NLP):\n  - Carry out sentiment analysis with NLP technologies to support macroeconomic analysis.\n- Application to official statistics:\n  - Application of Big Data technologies and resources to improve timelines and granularity of official statistics.\n- Peer learning and collaboration:\n  - Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest."
    },
    {
      "heading": "Course objectives (upon completion)",
      "content": "- Provide support in setting up Big Data architecture, encompassing data extraction, preprocessing, and visualization in their various organizations.\n- 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).\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 sentiment 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 their official statistics.\n- Facilitate peer-learning on Big Data applications and explore collaborations between agencies working on projects of mutual interest."
    }
  ],
  "bullets": [
    "Target audience:",
    "Qualifications:",
    "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",
    "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.",
    "Big Data architecture:",
    "Machine learning for macroeconomic monitoring:",
    "Geospatial and satellite data analysis:",
    "Natural language processing (NLP):",
    "Application to official statistics:",
    "Peer learning and collaboration:",
    "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."
  ],
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  "generatedAtUtc": "2026-09-29T17:06:04.158Z"
}