Reproducible Data Science with Unix, Git, and RStudio

Managing complex data projects requires more than just writing code; it requires a structured environment and reliable version control to ensure results are reproducible and professional. This course guides you through the essential tools and workflows used by data scientists to keep their work organized, collaborative, and easy to audit. You will transform your workflow from scattered scripts into well-documented, versioned projects that others can easily understand and replicate. What you'll learn: - Understand the foundations of version control using Git to track changes and collaborate. - Navigate and manage files efficiently using the Unix command line interface. - Configure RStudio projects to maintain a clean and reproducible workspace. - Apply GitHub workflows to share code and manage project repositories. - Practice modern environment management to ensure consistent results across different systems. - Implement basic automation patterns to streamline data processing tasks. The course begins with fundamental concepts of project organization and key terminology before progressing through written exercises in the command line, version control systems, and integrated development environments. You will read through practical scenarios that mirror real-world data science challenges. This course is designed for beginners entering the data science field who want to build a professional technical foundation. No prior experience with Git, Unix, or advanced R is required. Start building a more organized and professional data science workflow today.