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Setup

To get started, we setup the programming environment. Follow these couple of steps to get ready, no prerequisites needed.

Visual Studio Code

First, install a code editor. We urge you to instal Visual Studio Code (VS Code) a free and open-source editor developed by Microsoft .

If you don't have Visual Studio Code already installed, download it from their website: https://code.visualstudio.com/.

Profile

To quickstart your VS Code setup, download our profile that includes essential plugins and convenient settings tailored for data science work.

VS Code Profile

Included Extensions

The profile comes with the following essential extensions:

  • Python - Core Python language support
  • Python Debugger - Debug your Python code
  • Jupyter - Work with Jupyter Notebooks directly in VS Code

Additionally, stylistic plugins are included for a more pleasant coding experience and auto-save is enabled by default so you never lose your work. 🚀

Installation

To activate the profile, you can follow the video below.

uv

From the Python course you should already be familiar with the package manager pip. That background will help you quickly understand uv, a modern tool that not only replaces pip for package management but also handles Python installations.

Why the switch? While pip remains widely used and important to understand, this course aims to prepare you for modern real-world projects. uv has become a popular, state-of-the-art tool in modern Python development and learning it now will give you a competitive advantage.

No prior Python install necessary

A key benefit of uv is that you don’t need to install Python manually.

Install uv

Open Windows Powershell. Visit the uv documentation under under "Standalone installer" link. Make sure the Windows tab is selected.

Return to PowerShell and paste the installer command shown in the docs.

uv standalone installation

On macOS or Linux, open Terminal. Visit the uv documentation under "Standalone installer", link. Make sure the macOS or Linux tab is selected.

Return to your terminal and paste the installer command.

Press Enter to execute the command

Regardless of your operating system, upon completion you should see something like:

Downloading uv
    uv
    uvx
    uvw
everything's installed!

You can now close the Terminal ( macOS / Linux) or PowerShell ( Windows).

Info

The following steps are OS-agnostic; they are the same for Windows, macOS and Linux.

1. Create a project

Now, we will cover a typical workflow to set up and initialize a new project.

Info

A project is a folder that contains all scripts, configuration and data files that belong together. Everything for the project lives in that folder.

Create a new folder named data-science in an easy-to-find location you’ll use throughout this course.

Open VS Code. Go to File → Open Folder…, select the data-science folder. VS Code will open a new window.

Tip

For more on navigating VS Code, see the Python course chapter: link

2. Initialize the project

In VS Code, open the integrated terminal (via Terminal → New Terminal).

uv init --vcs none  # (1)!
  1. With the --vcs flag a version control system can be specified. By default --vcs git is set, which initializes a git repository. Since git is not within the scope of this project, we set --vcs to none.
Restart VS Code if command fails

If the command returns an error saying uv was not found, close and reopen VS Code. This allows your system to recognize the newly installed uv executable. Then run the command again.

Navigate command history

There's no need to re-type old commands. Press Up / Down to cycle through previously executed commands in the terminal.

This initializes the project. uv creates a few files in your folder. Your workspace should look like this:

With the project structure:

📁 data-science/
├── 📄 .python-version
├── 🐍 main.py
├── 📄 pyproject.toml
├── 📄 README.md

Explore the new files

Click through these new files:

  • .python-version Contains the Python version used by your virtual environment.
  • main.py An entry script to verify the setup (we’ll revisit this later).
  • pyproject.toml Project metadata such as name and version.
  • README.md An empty README for a project description; you can ignore it for now.

3. Virtual Environment

With an initialized project we can easily set up a virtual environment. To do so simply run:

uv sync
Virtual Environments?

If you need a refresh on virtual environments, what they do and their purpose, read through the corresponding section in the Python course: link

What happens during uv sync?

When you run uv sync, three things happen automatically:

  1. Python installation: uv checks the .python-version file and installs the specified Python version if it's not already available on your machine.

  2. Virtual environment: A .venv folder is created at the root of your project, containing an isolated Python environment for your project.

  3. Dependency locking: A uv.lock file is generated. This file pins all package versions used in your project, ensuring anyone else can faithfully recreate the exact same environment.

No manual edits

Since the uv.lock is auto-generated, never ever manually edit this file!

Test your setup

Let's verify everything works by running the main.py script that was created during initialization:

uv run main.py

If you have a similar output, you've successfully created your first project. 🎉

>>> Output
Hello from data-science!
No activation needed

Notice that the run command automatically invokes the project's virtual environment, meaning you do not have to activate the environment beforehand. In practice that means you create your scripts and simply execute them without an activated environment.

4. Packages

Since, we will be working with a couple of different packages, we have to discuss commands for installing and removing packages.

Again, no activation needed

Once again, you don't have to activate your environment to install and remove packages. With uv, you can manage dependencies directly from any terminal in your project folder, the virtual environment is "handled" automatically in the background.

To install packages use the add command:

uv add <package-name>

replace <package-name> for example with pandas:

uv add pandas

After a successful installation, take some time to open the pyproject.toml file. Under dependencies you should find the pandas package.

pyproject.toml
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[project]
name = "data-science"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.14"
dependencies = [
    "pandas>=3.0.0",
]

The content of uv.lock was changed as well, the file contains more info on the installed packages such as pandas and its dependencies as well (i.e., numpy, python-dateutil, six and tzdata).

Share a project

If you share your project, be sure to include the files .python-version, pyproject.toml and uv.lock. These allow for a recreation of your virtual environment.

Let's remove the package with the remove command:

uv remove pandas

Again, you can check both pyproject.toml and uv.lock which are automatically updated accordingly.

Get a script running

  1. Create a new script called plot.py

  2. Paste following example (taken from matplotlib docs) within your script:

    plot.py
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    import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import PathPatch
from matplotlib.path import Path

N = 400
t = np.linspace(0, 2 * np.pi, N)
r = 0.5 + np.cos(t)
x, y = r * np.cos(t), r * np.sin(t)

fig, ax = plt.subplots()
ax.plot(x, y, "k")
ax.set(aspect=1)
plt.show()

  3. Determine necessary packages to get this script running and install them with uv.

  4. Lastly, run the script with uv.

Python Scripts or Jupyter Notebooks?

For this course, you can work with Python scripts (.py files) and/or Jupyter Notebooks (.ipynb files). Both are supported in VS Code and each has its strengths.

  • Python Scripts

    Advantages

    • Better for production code and reusability
    • Easier version control and collaboration
    • Runs faster without cell-by-cell overhead
    • Cleaner debugging with standard tools

    Disadvantages

    • Less interactive during exploration
    • Need to rerun entire script for changes
    • Harder to visualize intermediate results

  • Jupyter Notebooks

    Advantages

    • Great for exploration and prototyping
    • Inline visualizations
    • Combines documentation and code
    • Easier to share findings with non-programmers

    Disadvantages

    • Can become messy with non-linear execution
    • Harder to maintain as projects grow
    • More challenging for version control
    • Not ideal for reusability
Our recommendation

Many data scientists use both: notebooks for exploration, scripts for production. Simply experiment with both. For quick prototyping lean towards a Jupyter Notebook. For more refined code switch to Python scripts.

Wrap-Up

You've successfully set up your development environment! Throughout this course, you'll create multiple projects using the workflow covered in sections 1-4. Don't worry about memorizing every step—just refer back to this page when needed.

For quick reference, here's a cheat sheet:

Cheat Sheet - Project Setup
  1. Create a new folder for your project
  2. Open the folder in VS Code
  3. In the terminal, run: 
    uv init --vcs none
uv sync
  4. Install packages as needed: 
    uv add <package-name>
  5. Run your code: 
    uv run <script-name>.py

Need help? Run uv --help for more commands and options.