Economic data analysis is essential for policymakers, business leaders, researchers, and analysts who seek to make evidence-based decisions. This program equips participants with the skills to collect, process, interpret, and visualize economic data to uncover trends, evaluate policies, and forecast outcomes. Participants will explore statistical tools, econometric techniques, and modern data visualization methods, applying them to real-world economic challenges.
Economists and financial analysts
Policy advisors and researchers
Business strategy and planning professionals
Academic and postgraduate students in economics or finance
Professionals working in government, NGOs, and international organizations
By the end of the program, participants will be able to:
Understand different types and sources of economic data.
Apply statistical and econometric techniques for data analysis.
Interpret economic indicators to support decision-making.
Utilize modern tools for economic data visualization.
Develop evidence-based economic reports and forecasts.
Day 1:
Fundamentals of Economic Data
Types of economic data: micro, macro, time series, and panel data
Key economic indicators (GDP, CPI, unemployment rate, trade balance, etc.)
Data sources: national statistical agencies, international organizations, and private databases
Data quality assessment and cleaning techniques
Case study: Interpreting recent national economic statistics
Day 2:
Statistical Tools for Economic Analysis
Descriptive statistics: measures of central tendency and dispersion
Correlation and causation in economics
Probability distributions relevant to economic data
Hypothesis testing in economic research
Practical exercises using Excel or statistical software
Day 3:
Introduction to Econometrics
Basics of regression analysis and model building
Simple and multiple linear regression in economic contexts
Identifying multicollinearity, autocorrelation, and heteroskedasticity
Application of econometric models to real economic data
Interpreting regression outputs for policy and business decisions
Day 4:
Economic Forecasting and Modeling
Time series analysis and trend detection
Moving averages, exponential smoothing, and ARIMA models
Using leading and lagging indicators for forecasting
Scenario analysis and sensitivity testing
Hands-on exercise: Building a simple GDP growth forecast
Day 5:
Data Visualization and Reporting for Economics
Principles of effective economic data visualization
Tools: Excel, Tableau, Power BI for economic dashboards
Combining charts, maps, and tables for clarity
Writing economic reports for policymakers and stakeholders
Final group project: Presenting an economic analysis report