Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

Before coming to this course please make sure you have:

  • Basic computer skills and fundamental coding knowledge (e.g., knowledge of different data types, loading data, writing simple functions)
  • Familiarity with a code editor (e.g., VSCode)
  • Interest in AI-assisted coding tools

After following this lesson, learners will be able to:

  • Generate code and optimize its performance using an AI coding assistant
  • Refactor and improve the structure and quality of existing code using an AI coding assistant
  • Automate repetitive coding tasks using AI coding assistant
  • Generate and improve documentation using AI coding assistant
  • Recognize, evaluate, and mitigate ethical and security considerations when using AI coding assistant

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

For this lesson, the setup includes a coding environment, an AI coding assistant, and datasets for practical exercises.

Code Editor

The recommended code editor for this lesson is Visual Studio Code, a popular code editor frequently used by researchers. You can download it from the official website. Follow the instructions on the website to install it on your operating system.

Setting up Codeium

  1. Make sure you have an Integrated Development Environment (IDE) installed on your computer. Go to the Codeium download page to see a list of supported IDEs and installation instructions. Visual Studio Code is on the list, so if you followed the previous step, you are all set.

  2. Open Extension Marketplace: Open the Extension Marketplace, which is the icon with boxes on the primary sidebar in your VS Code.

  3. Install Codeium: Type Codeium in the marketplace search bar, open up the page and click the blue Install button.

  1. Authorize: After installation is complete, you should be prompted by Visual Studio Code with a pop-up on the bottom right to authorize Codeium. If not, you should see an option to sign in to your Codeium account in the bottom left Accounts tab of your Visual Studio Code window. Click the Sign in with Auth to use Codeium option. Either method (popup or Accounts tab) should redirect you to the Codeium website.

  2. Create Account: If you do not have a Codeium account yet, you will be redirected to create an account.

  3. Sign In: If you are not signed in, please sign in with your account details. Once you sign in, you will be redirected back to Visual Studio Code via pop-up. Click Open Visual Studio Code, which should redirect you back to Visual Studio Code. If you are using a browser-based IDE, instead of being redirected back with pop-up, you will be routed to instructions on how to complete authentication by providing an access token. Please follow these instructions instead of Step 6, and then continue on with “Using Codeium.”

  4. All Done! You will be asked to confirm the authentication in Visual Studio Code (click Open in the resulting pop-up).

  5. Make sure that the extension is enabled by checking the bottom left corner of your Visual Studio Code window. You should see a Codeium icon there. Otherwise, go to the marketplace, look for Codeium, and click the Enable button. It could be necessary to reload the window after enabling the extension.

You can alternatively follow the Codeium installation instructions in VS Code on the official website, which will guide you through the same steps.

Additional Notes

  • If you have issues with enabling the Codeium chat, it could be due to the disabled telemetry on your VS Code. Make sure to enable telemetry by following the instructions on the VS Code telemetry page.
  • Codeium can be also used with other IDEs, such as IntelliJ IDEA, PyCharm, Vim, and WebStorm. You can find the installation instructions for these IDEs on the Codeium download page.

Data Sets

For this lesson, we will work with datasets tracking atmospheric CO2 levels, specifically the Trends in Atmospheric Carbon Dioxide dataset, available here. This dataset, sourced from the U.S. Government’s Earth System Research Laboratory, Global Monitoring Division, includes two primary series:

  1. Mauna Loa Series – The longest continuous record of atmospheric CO₂ concentrations, dating back to 1958, from measurements at Mauna Loa Observatory in Hawaii.

  2. Global Average Series – A calculated global average of atmospheric CO₂ levels from marine surface sites worldwide.

During the exercises, we will download and analyze these datasets together, so there’s no need to prepare or download anything in advance.

Setting Up Your Python Environment

Installing Python

For the practical exercises in this lesson, we will use Python. If Python isn’t already installed on your system, download it from the official Python website and follow the provided instructions for installation.

Setting Up a Virtual Environment

To ensure a consistent and isolated Python environment, we recommend using a virtual environment. Virtual environments allow you to manage dependencies specific to each project without affecting other Python projects or your global Python setup.

Install virtualenv if it’s not already available by running:

BASH 

pip install virtualenv

Alternatively, you can use pipx to manage virtualenv installation:

BASH 

pipx install virtualenv

Then, in your project directory, you can create a virtual environment:

BASH 

python3 -m venv .venv

This command will create a .venv folder in your current directory to store all environment-specific files.

To activate the virtual environment, you will use the following command:

  • On Windows, run .venv\Scripts\activate.bat
  • On macOS/Linux, run source .venv/bin/activate

After activation, you should see the name of your environment (e.g., .venv) appear in your terminal prompt, indicating that the environment is active.

Once the environment is activated, you can install the packages needed for this lesson. Run the following command to install the necessary dependencies:

BASH 

pip install -r https://raw.githubusercontent.com/olgaminaeva/gen-ai-coding/refs/heads/main/learners/files/requirements.txt

When you’re finished with your work, after the end of this lesson, deactivate the virtual environment by typing deactivate. This will return you to your global Python environment.