Exercise - Data science basics and environment setup


Meteors can be visible from Earth every night. But the best meteor showers are the ones that originate from one of the comets that orbits around our sun. To see those meteor showers, beyond knowing the path of the comets, we must also consider the side of the Earth where the meteors will enter our atmosphere. And we must consider whether the night sky will be dark enough to optimize our view of the meteor tails.

Review the data science life cycle

Before we jump into the code, we need to review the data science life cycle. Data science is a field that supports the discovery of new ways to view and understand our world by using data.

A typical data science project iterates through four main steps:

  1. Business understanding
  2. Data acquisition and understanding
  3. Modeling
  4. Deployment

A project will iterate through these steps. So in every step, you can revisit one of the other steps to see if something has changed or if what you now know provides insights that might change one of the other steps.

Although we're not space-exploration experts, we can use what we learn from experts to help guide this module. In a scenario where what you're building and discovering affects something real, you should always consult an expert in the field.

This module focuses on the "Data acquisition and understanding" node of the data science life cycle.

Set up your development environment

This module helps you create a model to predict the best date to view a meteor shower. To create the model, you need some kind of Python notebook development environment.

If you haven't set up a Python environment before, the easiest way to do this is to use GitHub Codespaces. To start using GitHub Codespaces, follow these steps:

  1. Make sure you're signed in to GitHub.
  2. Go to Codespaces.
  3. In the Explore quick start templates section, locate the Jupyter Notebook template and select Use this template.


If the Jupyter Notebook template isn't displayed in the quick start templates, select See all to list all available templates.

The Jupyter Notebook codespace launches and should look similar to the following screenshot:

Screenshot that shows a new Jupyter Notebooks codespaces environment.

The Jupyter Notebook codespace provides a workspace with Visual Studio Code, Python, and Jupyter Notebooks in your browser.


For personal GitHub accounts, you can use 120 core hours of Codespaces a month for free. For more information on billing, see About billing for GitHub Codespaces. For more information on how to use Codespaces, see GitHub Codespaces overview.

After you've created your codespace, follow these steps to prepare your environment:

  1. Create a folder called over-the-moon.
  2. Create a folder inside the over-the-moon folder. Name it meteor-showers.
  3. Create a file called meteor-showers.ipynb.
  4. Create a folder inside the meteor-showers folder. Name it data.
  5. Open the meteor-showers.ipynb file.

Your environment should look like this screenshot:

Screenshot that shows a the set up of folders and files in the codespaces environment.