Transform your coding approach from quick fixes to lasting solutions. 

This hands-on workshop teaches you how to evolve from writing disposable scripts to building robust, professional systems that serve as long-term research assets. You'll learn to apply computational "compassion" and legibility principles—designing code that minimizes burden on collaborators and your future self while maximizing readability and navigability of your research process.

The workshop covers essential practices for professional-grade coding, including project organization using the TIER Protocol, implementing effective naming conventions, and creating control files that orchestrate complex workflows. You'll discover how to write code that communicates clearly through comprehensive documentation, logical flow, and meaningful variable names, while incorporating proactive debugging strategies and automated error handling. Beyond technical skills, you'll learn to present results professionally through publication-ready visualizations and tables that emphasize statistical substance. Whether you're analyzing survey data, running statistical models, or processing large datasets, you'll leave with concrete strategies for writing code that grows with your research career and serves as a valuable professional asset. Basic familiarity with at least one statistical programming language (R, Python, Stata, etc.) is recommended.