So you’ve just unboxed your sleek brand-new MacBook M 5, and now you intend to transform it right into an effective yet marginal information scientific research maker– no Docker, no heavy data source web servers, and no GUI mess.
Just pure, clean command-line performance. ⚡
In this overview, I’ll show you just how to establish your Mac for Information Scientific research– the right way.
We’ll set up only what’s necessary: Homebrew, pyenv, Python, and core data-science collections like pandas, NumPy, and JupyterLab.
Why a Very little Configuration?
Most configuration guides for Information Scientific research overload you with points you might not need on the first day– Docker, PostgreSQL, VS Code, Conda, etc.
This guide concentrates on a quickly, light-weight arrangement that:
- Keeps your Mac clean and effective
- * Utilizes indigenous Apple Silicon efficiency
- * Gives you a reproducible setting with separated Python versions
- Perfect for:
- ➡ information evaluation,
- ➡ notebook prototyping,
- ➡ light-weight device discovering jobs.
What You’ll Have by the End
ToolPurpose
HomebrewmacOS bundle manager
pyenvManages Python versions