Data Science Productivity Tools with Git, GitHub, and RStudio

Managing data science projects requires more than just writing analysis code; it demands organization, version control, and reproducible reporting to collaborate effectively. In this text-based course, you will learn how to structure your projects professionally, track changes systematically, and automate repetitive tasks using industry-standard tools. What you'll learn: * Understand fundamental Unix/Linux command-line tools to navigate directories and automate tasks. * Manage project history with Git to track code changes and collaborate without conflicts. * Publish and share your data science repositories on GitHub for seamless team collaboration. * Organize files and code efficiently within the RStudio development environment. * Create reproducible reports and dynamic documents using modern Markdown and Quarto standards. * Apply clean project structures and modern configuration workflows to keep your analysis organized. You will start with key terminology and foundational command-line concepts before moving step-by-step through local version control, remote collaboration, and automated report generation. This course is designed for beginning data analysts and scientists looking to establish a professional workflow, with no prior command-line or version control experience required. Start building a structured, reproducible, and collaborative data science workflow today.