Data Science Productivity Tools: Git, GitHub, and Command Line Basics

Organizing code, managing data versions, and ensuring your analysis is reproducible are critical skills for any modern data professional. This text-based course guides you from manual, disorganized file-tracking to professional, reproducible workflows using standard industry tools. What you will learn: - Understand core data science productivity concepts and why reproducible workflows matter. - Master Git version control to track project history and prevent data loss. - Navigate directories and automate basic tasks using the Unix/Linux command line. - Configure GitHub repositories to host, share, and document your data science code. - Create structured project environments to manage dependencies and ensure reproducibility. - Use modern markdown-based reporting tools to generate clean, automated documentation. You will start with foundational command-line concepts and Git basics before moving into collaborative workflows and structured project organization. This course is designed for absolute beginners with no prior terminal or version control experience. Start building cleaner, more professional data projects today.