AI and Big Data in Business Economics

Theme: Exploring Data-Driven Economic Intelligence

• Understanding the shift from classical economic modeling to AI-based forecasting
• Identifying and sourcing real-time economic and business datasets (World Bank, IMF, UNCTAD, Statista APIs)
• Fundamentals of data pipelines, feature engineering, and data transformation
• Overview of essential tools: Python, R, Jupyter, Pandas, Power BI

 Hands-On Activities:

• Live coding session: Cleaning and transforming real-world economic data
• Practical: Economic indicator mapping using Python & Excel
• Visual analytics using Power BI/Tableau for trade, inflation, and fiscal trends

Theme: Building Predictive and Prescriptive Economic Models

• Developing forecasting models (ARIMA, LSTM, Prophet) for GDP, inflation, FDI flows
• AI-based classification for business risk and consumer behavior segmentation
• Introduction to Natural Language Processing (NLP) for policy document and sentiment analysis
• Applications of Transformer models (BERT) in analyzing economic surveys and budgets

Hands-On Activities:

• Model training lab: Build & validate economic forecasting models in Python
• Predicting inflation rates using LSTM and Prophet with IMF datasets
• Sentiment analysis: Budget speech classification using NLP pipelines
  

Theme: From Insight to Implementation in Business & Policy

• Building scalable AI pipelines for business economics using cloud tools (Azure, Colab, MLFlow)
• Integrating AI into business intelligence dashboards for CFOs and policy units
• Principles of explainable AI, fairness, accountability in economic applications
• Overview of global regulations: OECD AI Principles, RBI AI Guidelines, EU AI Act

Hands-On Activities:

• End-to-end build: Real-time economic dashboard using Streamlit/Power BI
• SHAP & LIME lab: Interpreting AI models in economic decision-making