🟢 macOS: Install Tools¶

Installation instructions for required tools on macOS.

Always follow the vendor-recommended installers.

These tools are essential for professional data analytics.

WHY?

Professional analytics projects depend on local tools for running code, managing packages, checking quality, and working with GitHub repositories. Installing the required tools creates a consistent project environment across machines and operating systems. Correct tool installation reduces setup errors and makes later project commands more predictable.

Required Tools¶

Git¶

Git is often already installed on macOS.

To check, open Terminal and run:

git --version

If Git is missing or outdated, install it from the official site: https://git-scm.com/

WHY?

Git **tracks changes** in project files over time. It records what changed, when it changed, and who made the change. Git is used with GitHub, but they are different. - **Git is version control software** that runs on a machine. - **GitHub is a cloud platform** that stores Git projects online.

Visual Studio Code¶

Download and install Visual Studio Code from: https://code.visualstudio.com/

After installation:

Move Visual Studio Code.app to the Applications folder
(Recommended) Enable the code command in Terminal by following: https://code.visualstudio.com/docs/setup/mac

WHY?

Visual Studio Code is a **code editor** used to open, inspect, edit, and run project files. It provides an integrated **terminal**, which allows project commands to be run from inside the same workspace. The `code` command makes it possible to open a project folder in VS Code directly from the terminal.

uv (Python environment and dependency manager)¶

Follow the official installation instructions from Astral: https://docs.astral.sh/uv/getting-started/installation/

Note: Python is not installed at this step. Python will be installed per-project using uv, which also manages versions.

WHY?

`uv` manages the Python version and project packages used by a project. Using `uv` helps each project install the correct Python version and packages from the project configuration in `pyproject.toml` and keeps project Python **separate** from any other Python being used on the machine. If you worked with older Python projects, you might have used `pip` to manage required packages and `venv` to manage the virtual environment kept in the local `.venv` folder. We moved to `uv` because: - it is fast - it uses the same commands on Windows, macOS, and Linux - it creates and updates the local `.venv` project environment - it can install and manage the Python version for the project - it reduces the number of separate Python setup tools needed

Verify¶

After installation, open a new Terminal window and run:

git --version
code --version
uv --version

Each command should display a version number. If any command fails, revisit the corresponding installer.