Software Guides
Software tools and applications commonly used for research workflows at IPA, including statistical software, version control, programming languages, and development environments.
Research and data work at IPA relies on a variety of software tools for data collection, processing, analysis, version control, and collaboration. This section provides guides for the core software applications used across IPA projects, helping you get started, learn best practices, and troubleshoot common issues.
The Software Guides cover several categories. Click on each section below to learn more.
The shell—also called the terminal or command line—is a text-based interface for interacting with your computer’s operating system. Learning basic shell commands enables efficient file management, automation, and integration with other development tools.
Version control systems help track changes to files over time, enabling collaboration, backup, and the ability to revert to previous versions. Git is the most widely used version control system, and GitHub provides cloud-based hosting for Git repositories along with collaboration features.
Code editors and integrated development environments provide tools for writing, debugging, and managing code. Virtual environments allow you to isolate project dependencies and avoid conflicts between different projects.
These tools form the core of data analysis and research workflows at IPA. Stata is widely used for econometric analysis and data cleaning. Python provides powerful libraries for data science, machine learning, and automation. Quarto enables creation of reproducible research documents combining code, analysis, and narrative.