Big Data Analytics for Informal Economies, Labour Market Intelligence and Consumer Price Analysis Workshop/Seminar

Course overview

This course equips economists, statisticians, policymakers and data professionals with advanced skills in big data analytics, artificial intelligence and alternative data integration to analyse informal economic activity, labour market dynamics and consumer price movements. Participants explore how administrative records, mobile money transactions, digital payments, online job postings, web-based retail prices, satellite imagery and household survey data can generate insights into employment trends, informal business activity, cost-of-living pressures and economic conditions.

This course equips central bank professionals with practical skills in big data analytics, artificial intelligence and machine learning to strengthen monetary policy analysis, inflation forecasting, labour market monitoring and economic surveillance. Participants learn to analyse alternative economic data, monitor consumer prices and cost-of-living trends, assess informal economic activity, forecast wage and employment movements, and develop economic intelligence dashboards using Python, SQL, Power BI and geospatial analytics. The course supports central banks in enhancing inflation monitoring, improving economic nowcasting, assessing financial inclusion, and making timely, evidence-based monetary policy decisions.

Course objectives

Apply big data analytics and AI to measure informal economic activity and employment trends. Integrate alternative data sources to strengthen labour market intelligence and economic monitoring.
Apply data governance, privacy protection, bias assessment and responsible AI principles to economic data analysis.
Build interactive dashboards and predictive models for economic planning, policymaking and evidence-based decision-making.
Use machine learning and statistical modelling to identify employment patterns, price changes and emerging economic trends.
Develop consumer price intelligence systems using online prices, commodity data and web-scraping techniques.

Who should attend

Central bank economists, monetary policy analysts, national statistics office professionals, labour market analysts, government economic planners, ministries of finance and labour officials, economic research officers, data scientists, statisticians, development economists, employment policy specialists, social protection analysts, financial inclusion specialists, digital economy researchers, consumer price statisticians, market intelligence analysts, AI and machine learning professionals, county economic planning officers, international development practitioners, NGO research and monitoring specialists.

Course content

economic theory in selecting model specifications. use this format Module 1. Big Data Analytics Frameworks for Informal Economies and Labour Market Intelligence Informal economy measurement, big data analytics for economic research, digital economy indica
Module 2. Alternative Data Sources for Measuring Informal Employment and Economic Activity Household surveys, census data, administrative records, mobile network data, satellite imagery and night-time lights, business registries, tax records, social prote
Module 3. Mobile Money and Digital Payment Analytics for Informal Sector Monitoring Mobile money transaction analysis, digital payment patterns, financial inclusion indicators, informal business activity monitoring, consumption behaviour analytics, anomal
Module 4. Labour Market Data Engineering and Employment Trend Analysis Labour market data collection, Python data cleaning, SQL-based data integration, employment and unemployment indicators, underemployment measurement, labour force participation, workfo
Module 5. AI-Powered Job Market Intelligence and Skills Demand Forecasting Job vacancy data collection, web scraping, natural language processing, occupational classification, skills extraction, machine learning for labour demand forecasting, wage trend a
Module 6. Web Scraping and Consumer Price Intelligence for Retail and Commodity Markets Automated retail price collection, Python web scraping, Beautiful Soup, Scrapy, API-based price collection, product matching, price standardisation, commodity price mo
Module 7. Automated Price Collection and Consumer Price Index (CPI) Monitoring CPI methodology, consumer basket weights, food and fuel price analysis, price data cleaning, outlier detection, inflation measurement, regional price comparisons, automated pri
Module 8. Machine Learning for Informal Economic Activity Estimation and Employment Prediction Feature engineering, regression and classification, Random Forest, XGBoost, employment prediction, informal sector estimation, model evaluation, explainable AI,
Module 9. Geospatial Analytics for Informal Settlements, Markets and Economic Clusters GIS mapping of informal businesses, spatial employment analysis, market accessibility, economic cluster identification, satellite-derived economic indicators, spatial d
Module 10. Cost-of-Living Analysis and Household Consumption Patterns Household expenditure analysis, consumption patterns, food affordability, regional cost-of-living comparisons, inflation exposure, purchasing power analysis, poverty indicators, househo
Module 11. Big Data Platforms and Cloud-Based Economic Data Processing SQL databases, economic data warehouses, Apache Spark, PySpark, cloud data platforms, ETL and ELT pipelines, large-scale data processing, data quality management, metadata and reproduc
Module 12. Economic Dashboards and Real-Time Labour Market Monitoring Power BI, Tableau, Plotly, labour market dashboards, employment trend visualisation, consumer price monitoring, commodity price dashboards, automated reporting, economic intelligence sy
Module 13. Predictive Analytics for Wage Trends, Underemployment and Labour Demand Wage distribution analysis, earnings inequality, underemployment forecasting, occupational demand prediction, workforce planning, skills gap analytics, employment scenario
Module 14. Data Quality, Privacy, Bias and Responsible AI in Economic Statistics Data validation, statistical quality assurance, alternative data bias, privacy protection, responsible AI, model explainability, data governance, access controls, secure econ
Module 15. Integrated Economic Intelligence Capstone Project Informal economy analytics, labour market intelligence dataset development, automated consumer price monitoring, employment and price forecasting, interactive economic dashboards, model validati

General notes

Big Data Analytics Frameworks for Informal Economies and Labour Market Intelligence, Alternative Data Sources for Measuring Informal Employment and Economic Activity, Mobile Money and Digital Payment Analytics for Informal Sector Monitoring, Labour Market Data Engineering and Employment Trend Analysis, AI-Powered Job Market Intelligence and Skills Demand Forecasting, Web Scraping and Consumer Price Intelligence for Retail and Commodity Markets, Automated Price Collection and Consumer Price Index (CPI) Monitoring, Machine Learning for Informal Economic Activity Estimation and Employment Prediction, Geospatial Analytics for Informal Settlements, Markets and Economic Clusters, Cost-of-Living Analysis and Household Consumption Patterns, Big Data Platforms and Cloud-Based Economic Data Processing, Economic Dashboards and Real-Time Labour Market Monitoring, Predictive Analytics for Wage Trends, Underemployment and Labour Demand, Data Quality, Privacy, Bias and Responsible AI in Economic Statistics, Integrated Economic Intelligence Capstone Project.
Practical exercises — comma-separated Informal employment trend analysis using survey and administrative data, mobile money and digital payment pattern analysis, online job vacancy scraping and skills demand forecasting, automated retail price collection and commodity price monitoring, CPI trend analysis using disaggregated price data, machine learning for employment and labour demand prediction, geospatial mapping of informal markets and business clusters, household cost-of-living and consumption analysis, interactive labour market intelligence dashboards, consumer price anomaly detection, alternative data quality and bias assessment, integrated economic intelligence dashboard development.

Frequently asked questions

1. 1. Who should attend the Big Data Analytics for Informal Economies, Labour Market Intelligence and Consumer Price Analysis course?

The course is designed for central bank economists, labour market analysts, statisticians, data scientists, government economic planners, consumer price analysts, financial inclusion specialists, policy researchers and professionals involved in economic monitoring, employment analysis and data-driven policymaking.

2. 2. What will participants learn in this course?

Participants will learn to analyse informal economic activity, integrate alternative data sources, monitor labour market trends, automate consumer price collection, apply machine learning to economic forecasting and develop interactive dashboards for economic intelligence and policy planning.

3. 3. Which technologies and tools are covered?

The course covers Python, pandas, SQL, Beautiful Soup, Scrapy, scikit-learn, XGBoost, Apache Spark, Power BI, Tableau, Plotly, QGIS and GeoPandas, alongside APIs, web scraping, geospatial analytics and cloud-based data processing.

4. 4. How is big data analytics used to measure informal economic activity?

Big data analytics can combine household surveys, administrative records, digital payment aggregates, business data, satellite imagery and other alternative indicators to estimate economic activity, identify informal business clusters and monitor employment patterns. Results depend on data coverage, quality and appropriate validation against official statistics.

5. What practical outputs will participants develop?

Participants will work towards producing an informal economy analytics report, a labour market intelligence dashboard, a consumer price monitoring prototype, an employment or price forecasting model and evidence-based recommendations for economic planning and policymaking.

Schedule & fees

Dates	Location	Duration	Fee (USD)
26/04/2027 - 07/05/2027	Naivasha,Kenya	5 Days	$2,000
05/07/2027 - 16/07/2027	Nairobi,kenya or Virtual	5 Days	$2,000