Economic analysis has changed. Central banks, universities, and policy institutions are working with larger, more diverse datasets, tighter timelines, and increasing demands for transparency and reproducibility. At the same time, reliance on expensive, closed software systems is becoming harder to justify.

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Open-source tools such as Python, Julia, and R are now standard across many research and policy institutions. They support modern economic modelling, data analysis, and reporting workflows—while enabling collaboration, version control, and long-term sustainability.

This course is designed for economists who want to work efficiently with these tools in real policy and research settings.

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About The Better Policy Project Courses

The Better Policy Project has delivered applied economics and data-driven training to policymakers, researchers, and students across the world. Our programmes combine economic theory with hands-on implementation, always grounded in real institutional needs.

Participants come from central banks, ministries, international organisations, and universities—creating a learning environment shaped by diverse perspectives and practical experience.

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What Makes This Course Different

• Designed for Economists: Programming is taught as a means to an end: better analysis, clearer communication, and more robust results.

• Policy-Relevant Applications: Examples and exercises draw on real economic and institutional data, not abstract toy problems.

• Modern Workflows: Emphasis on reproducible analysis, clean data pipelines, and transparent reporting.

• Experienced Instructors: Taught by economists who actively use these tools in research and policy work.

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Why Python Today?

• A Common Language Across Institutions: Python is widely used in central banks, research departments, and academia, making skills directly transferable.

• Integrated Workflows: From data collection and cleaning to analysis and visualisation, Python supports the full research cycle.

• Open and Sustainable: Open-source tools reduce vendor lock-in and make long-term projects easier to maintain and share.

• Clear and Efficient: Readable code supports collaboration within teams and across institutions.

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Course Overview

This 5-day online course focuses on using Python for text-based economic analysis, with applications relevant to policy institutions and research departments.

Participants will learn how to work with large collections of documents—such as reports, statements, and publications—and extract structured information that can support economic analysis and policy communication.

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By the end of the course, participants will be able to:

• manage and preprocess text data,

• apply descriptive and analytical techniques,

• produce clear summaries and visual outputs suitable for reports and presentations.

 

Course Structure

Day 1 – Python for Text-Based Economic Data

• Types of text data used in economics

• Python environment and core libraries

• Working with text as data

Day 2 – Cleaning and Preparing Text Data

• Preprocessing economic and institutional texts

• Structuring unstructured data

• Practical data-cleaning workflows

Day 3 – Descriptive Text Analysis

• Sentiment and tone analysis

• Topic discovery in document collections

• Visualising patterns and trends

Day 4 – Advanced Text Modelling

• Representing meaning in text

• Sequence-based approaches

• Model evaluation and interpretation

Day 5 – Applications and Integration

• Summarising large document sets

• Case studies from policy and research contexts

• Integrating text analysis into economic workflows

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Practical Details

📅 Course Dates: March 9–13, 2026
⏰ Time: To be announced
📌 Application Deadline: March 2, 2026