Training course on Big Data Analytics in Central Bank Research

Module 1: Introduction to Big Data in Central Banking

What is big data? Characteristics (volume, velocity, variety, veracity, value)

Relevance of big data to central bank mandates

Case studies of big data in global central banks

Module 2: Data Sources and Collection Methods

Structured vs. unstructured data (financial, macroeconomic, digital, textual)

High-frequency indicators and alternative data (satellite images, mobile payments, online prices)

Data privacy, ethics, and governance in central banks

Module 3: Data Cleaning, Processing, and Management

Handling missing values, duplicates, and outliers

Data pipelines and ETL (Extract, Transform, Load) processes\

Database systems for large datasets (SQL, NoSQL, cloud storage)

Module 4: Big Data Tools and Programming

Introduction to Python and R for big data analysis

Hadoop and Spark for distributed computing

Visualization tools for big data insights

Module 5: Machine Learning Applications

Supervised vs. unsupervised learning

Predictive modeling for inflation, GDP, and unemployment

Clustering and classification for financial stability monitoring

Module 6: Text Mining and Natural Language Processing (NLP)

Analyzing central bank communications (speeches, reports, social media)

Sentiment analysis for market expectations

Topic modeling in economic research

Module 7: Real-Time Data Analytics for Policy

Nowcasting techniques using big data

Early warning systems for financial crises

Real-time monitoring of payment systems and financial markets

Module 8: Integrating Big Data with Econometric Models

Hybrid approaches combining big data and traditional models

Forecast evaluation and accuracy improvement

Case studies of big data-enhanced policy analysis

Module 9: Visualization and Communication of Results

Effective dashboards for policymakers

Interactive data storytelling

Best practices in communicating big data insights

Module 10: Capstone Project and Policy Simulation

Group project using real-world datasets

Building and presenting a big data model for central bank research

Policy recommendations based on analytical findings