• Understand the principles and benefits of data analysis for policymaking
• Identify and formulate relevant policy questions that can be answered by data
• Use R and ggplot2 to create effective and attractive data visualizations
• Use Excel and Power BI to perform basic data analysis techniques, such as descriptive statistics, probability distributions, hypothesis testing, and regression analysis
• Use R to perform advanced data analysis techniques, such as microsimulation modeling, policy options analysis, and program evaluation
• Use data storytelling techniques to communicate and persuade your audience with data
• Avoid common mistakes and pitfalls in data analysis and visualization

To enhance learning and practical application of concepts, the training course will use a combination of interactive lectures, case studies, group discussions, practical exercises, and real-world examples. Participants will also get the chance to collaborate on group projects and create action plans adapted to the needs of their respective organizations.

Day One:

Introduction to Data Analysis for Policymakers

• What is data analysis and why does it matter for policymaking?
• Data analysis process and pipeline
• Data types, sources, and quality
• Data analysis tools and platforms
• Data analysis applications and use cases in various policy domains

Day Two:

Data Visualization with R and ggplot2

• What are R and ggplot2 and how to use them for data visualization?
• Grammar of graphics and aesthetics
• Geometric objects, scales, facets, coordinates, themes, etc.
• Plotting univariate, bivariate, multivariate, categorical, numerical, temporal, spatial, etc. data
• Customizing and saving visualizations

Day Three:

Data Analysis with Excel and Power BI

• What are Excel and Power BI and how to use them for data analysis?
• Data structures and manipulation
• Descriptive statistics and probability distributions
• Hypothesis testing and statistical inference
• Linear regression and correlation analysis

Day Four:

Data Analysis with R

• What is R and how to use it for data analysis?
• Data structures and manipulation
• Microsimulation modeling: creating synthetic populations and scenarios
• Policy options analysis: comparing costs and benefits of alternative policies
• Program evaluation: measuring the impact of policies using experimental or quasi-experimental designs

Day Five:

Data Storytelling with Data

• What is data storytelling and why is it important for communicating policy analysis and recommendations?
• Data storytelling principles and practices
• Data storytelling tools and platforms
• Data storytelling techniques: narrative, context, visuals, interactivity, etc.
• Data storytelling examples and best practices

To enhance learning and practical application of concepts, the training course will use a combination of interactive lectures, case studies, group discussions, practical exercises, and real-world examples. Participants will also get the chance to collaborate on group projects and create action plans adapted to the needs of their respective organizations.