Using Google Colab for Data Science · Lesson 1 of 1
Using Google Colab for Data Science
Run notebooks in hosted environments and handle files, runtime state, and compute limitations carefully.
Lesson
1. Introduction
Hosted notebooks make it easy to experiment without configuring a local environment. They are convenient for learning and collaboration, but runtime storage may be temporary and available hardware or session duration can change.
2. Core concepts
A notebook runtime is a remote process. Mounting cloud storage grants access to files, so use least privilege and unmount when finished. Variables disappear when the runtime resets; notebooks should document dependency installation and execute from a clean state.
3. Topics covered in this module
- Notebook creation, cells, runtimes, variables, snippets, and terminal access
- Drive mounting, file paths, runtime reset, and saving outputs
- GPU availability, resource limits, dependency installation, and reproducibility
- Audio transcription with Whisper as a bounded processing example
4. Syntax and how to read it
from pathlib import Path
project_dir = Path('/content/project')
project_dir.mkdir(parents=True, exist_ok=True)
print(project_dir.exists())The path is specific to a hosted runtime and may not persist after a reset. Do not assume runtime-local storage is durable.
5. Practical workflow
Keep source data in an approved location, mount only when needed, install pinned dependencies, and run the notebook from top to bottom. Verify that outputs are saved intentionally and do not expose private data in a shared notebook.
6. Common mistakes
- Assuming a temporary runtime file survives disconnection or reset.
- Mounting broad cloud storage permissions into an untrusted notebook.
7. Best practices
- Restart and run all cells before sharing a notebook.
- Store source code and instructions in version control; handle data and secrets through approved storage.
8. Practice task
Your notebook works only after running cells in a special order. What should you do before sharing it?
9. One possible solution
Restart the runtime and run all cells sequentially, fix hidden dependencies, and document required setup so the result is reproducible.10. Summary
Run notebooks in hosted environments and handle files, runtime state, and compute limitations carefully. Restart and run all cells before sharing a notebook. Store source code and instructions in version control; handle data and secrets through approved storage.
11. What comes next
The next module, Project 3 - RAG based AI Teaching Assistant, builds on this foundation and applies it to a new part of the analytics workflow.
Visual guide
A reliable Using Google Colab for Data Science workflow
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Step 01
Frame
Define the question, input scope, and intended output.
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Step 02
Work
Apply the method and validate assumptions as you go.
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Step 03
Check
Compare results with known cases and communicate limitations.
Code examples
import torch
print('CUDA available:', torch.cuda.is_available())Reports whether a compatible CUDA device is available to this runtime.
from pathlib import Path
project_dir = Path('/content/project')
project_dir.mkdir(parents=True, exist_ok=True)
result_path = project_dir / 'summary.txt'
result_path.write_text('Analysis complete', encoding='utf-8')
print(result_path.stat().st_size)Prints the saved file size in bytes.
Key points
- Run notebooks in hosted environments and handle files, runtime state, and compute limitations carefully.
- Restart and run all cells before sharing a notebook.
- Store source code and instructions in version control; handle data and secrets through approved storage.
Check your understanding
Practice
Your learning progress