AI Coding in Colab with Gemini — Build a Twitter Network in Minutes

Xuebin Wei
Oct 29
3 min read

Overview

This tutorial demonstrates how to use Google Colab with Gemini, Google’s generative AI, to perform real data science — from connecting GitHub and MongoDB to building a Twitter social network analysis — all without local setup.

Gemini works directly inside Colab as an AI coding agent, writing, explaining, and improving Python code within the notebook. This workflow shows how AI turns Colab into an intelligent, cloud-based data science environment that’s ideal for teaching, experimentation, and applied analytics.

What You’ll Learn

Connect Google Colab to GitHub and Google Drive
Store and access API keys securely using Colab secrets
Use Gemini to generate, explain, and refine Python code
Import and analyze Twitter data from MongoDB
Build and visualize a social network using NetworkX
Publish your Colab notebook to GitHub for collaboration

Step 1: Set Up Google Colab and Gemini

Open Google Colab at https://colab.research.google.com with your Gmail account. The free tier is sufficient for most analytics tasks and includes GPU and TPU resources.

You can open notebooks from Google Drive, GitHub, or upload them from your computer. Gemini is built into Colab — simply click the Gemini icon on the right sidebar to start coding with AI.

GitHub interface with "demo-student-lbsocial/test-colab" repository open. Options to select branch, with button for "New notebook" visible. — Colab home screen showing “New Notebook” and “Connect to GitHub

Step 2: Connect to GitHub and Google Drive

Authorize GitHub to pull and push code directly from Colab. You can also mount Google Drive to access datasets and save notebooks automatically.

Google Colab interface showing code snippet mounted at '/content/drive'. File tree includes 'drive' and 'sample_data' folders. — Notebook cell showing Drive mounted in /content/drive/MyDrive/

Use the terminal to clone repositories and manage version control:

!git clone <repository-url>

Step 3: Store Credentials Securely

Use Colab’s key icon (Secrets Manager) to store credentials such as API keys or database connection strings. This keeps them hidden and secure when sharing notebooks.

Colab Secret Manager showing entries "mongodb_connect," "openai_api," "twitter_api" with obscured values. Python code snippet below. — Colab sidebar showing a MongoDB connection string added as a secret

Example:

import user_data
connection_string = user_data.get('mongodb')

This approach prevents sensitive information from being exposed on GitHub or shared notebooks.

Step 4: Use Gemini as a Coding Agent

Gemini acts as your AI coding partner in Colab. Prompt it naturally — for example:

“Connect to MongoDB using my stored secret and count the total number of tweets.”

Gemini will generate code, explain each step, and even debug when errors occur. You can accept, edit, or rerun its suggestions directly in the notebook.

Code snippet in a notebook shows Python code using pymongo to connect to MongoDB, counting tweets. Side panel offers Gemini’s help. — Gemini panel generating and inserting Python code cells

Step 5: Build and Visualize the Twitter Network

After loading tweets from MongoDB, Gemini can help build a NetworkX graph of user mentions. It identifies connections between authors and mentioned users, calculates centrality measures, and generates visualization code to render an interactive social network.

Twitter Mention Network graph showing connections with thin gray lines on a light blue background. Legend indicates betweenness centrality. — Interactive network graph showing connected Twitter accounts in Colab output

This end-to-end workflow illustrates how AI can handle real-world data pipelines: from connection to visualization.

Step 6: Publish to GitHub and Share

Once your notebook is complete, save it directly to GitHub.Colab automatically adds an “Open in Colab” button to the repository, allowing others with a Google account to rerun your analysis in one click.

GitHub interface displaying a Colab notebook. The notebook shows Python code for mounting Google Drive and installing PyMongo. — GitHub repository page showing the “Open in Colab” badge above the notebook file.

Conclusion

AI is no longer just a tool for automation — it is a collaborator in coding and data science. With Gemini in Colab, you can write, debug, and visualize data interactively, focusing more on ideas and insights rather than setup and syntax.

Try this workflow yourself: connect Colab to GitHub, store credentials securely, and let Gemini assist you in building an intelligent, cloud-powered data science pipeline.