Pre-Course: Google Colab Orientation¶

Technical Preparation Module¶

Duration: Self-paced, approximately 2 hours
When: Complete in the week before Week 1

Purpose¶

This self-paced module ensures all students can navigate Google Colab before the course begins. The goal is not to make you a programmer, but to give you sufficient technical exposure to:

Understand what happens "under the hood" when AI systems are trained and run
Critically evaluate vendor claims about AI capabilities
Communicate effectively with technical teams
Recognise the difference between AI hype and operational reality

Learning Objectives¶

By completing this module, you will be able to:

Access and navigate the Google Colab environment
Understand the concept of a computational notebook
Execute pre-written code cells and observe outputs
Save work and manage files in Google Drive
Troubleshoot common issues

Technical Requirements¶

Modern web browser (Chrome recommended)
Google account (personal or institutional)
Stable internet connection
No software installation required

Part 1: Getting Started (30 mins)¶

What is Google Colab?¶

Google Colab (Colaboratory) is a free, cloud-based platform that lets you run Python code in your browser. Think of it as a "living document" that combines:

Text explanations (like a Word document)
Executable code (that actually runs and produces results)
Outputs (graphs, tables, results)

For this course, you'll use Colab to see how AI/ML systems actually work—without needing to become a programmer.

Why Colab for This Course?¶

No installation required — runs entirely in your browser
Free GPU access — can run real ML models without expensive hardware
Pre-installed libraries — common AI/ML tools ready to use
Shareable — instructors can distribute notebooks; students can share work
Persistent — notebooks save to Google Drive

Accessing Colab¶

Go to colab.research.google.com
Sign in with a Google account (personal or institutional)
You'll see the Colab welcome screen with options to create or open notebooks

Exercise 1.1: Open Your First Notebook¶

Click "New Notebook" to create a blank notebook
Notice the two types of cells:
Text cells — for explanations (like this document)
Code cells — for running Python code
The notebook auto-saves to your Google Drive under "Colab Notebooks"
Give your notebook a name by clicking "Untitled0.ipynb" at the top

Part 2: Running Code (30 mins)¶

Understanding Code Cells¶

Code cells contain Python instructions. For this course, you don't need to write code from scratch—just run what's provided and modify parameters to explore behaviour.

Exercise 1.2: Your First Code Execution¶

In a code cell, type (or copy) the following and press Shift+Enter to run:

print("Hello, Healthcare AI!")

The output appears below the cell. Congratulations—you've run Python code.

Exercise 1.3: Running a Simple Calculation¶

Try this clinical example:

# Calculate a NEWS2 score component
respiratory_rate = 22

if respiratory_rate >= 21 and respiratory_rate <= 24:
    rr_score = 2
    print(f"Respiratory rate {respiratory_rate}: NEWS2 score = {rr_score}")

Notice how the code makes a decision based on clinical parameters—this is the foundation of rule-based clinical decision support.

Exercise 1.4: Modifying Parameters¶

Change the respiratory_rate value to different numbers and re-run the cell. Observe how the output changes.

# Try different values: 12, 18, 25, 30
respiratory_rate = 12  # <-- Change this number

if respiratory_rate <= 8:
    rr_score = 3
elif respiratory_rate <= 11:
    rr_score = 1
elif respiratory_rate <= 20:
    rr_score = 0
elif respiratory_rate <= 24:
    rr_score = 2
else:
    rr_score = 3

print(f"Respiratory rate {respiratory_rate}: NEWS2 score = {rr_score}")

This is the core skill you'll use throughout the course: running provided code and modifying inputs to explore AI behaviour.

Part 3: Working with Pre-built Notebooks (30 mins)¶

Most course exercises use pre-built notebooks. You'll open them, run the cells, and modify parameters to explore behaviour.

Exercise 1.5: Opening a Shared Notebook¶

📓 Open Pre-Course Orientation Notebook

After opening the notebook: 1. Save a copy to your Drive (File → Save a copy in Drive) 2. Work through the guided exercises in the notebook

Key Operations to Practice¶

Action	How
Run a single cell	Shift+Enter or click the Play button
Run all cells	Runtime → Run all
Restart if stuck	Runtime → Restart runtime
Add a comment	In a code cell, use `#` before your text
Download notebook	File → Download → Download .ipynb

Exercise 1.6: Cell Execution Order¶

Important concept: Cells must often be run in order. Try this:

Create a new notebook
In the first cell, type: patient_name = "Jane Smith"
In the second cell, type: print(f"Patient: {patient_name}")
Run only the second cell — you'll get an error
Now run the first cell, then the second — it works

This demonstrates that code cells build on each other. If you get unexpected errors, try running cells from the top.

Part 4: Troubleshooting Common Issues (15 mins)¶

Problem	Solution
"Runtime disconnected"	Click "Reconnect" or Runtime → Reconnect
Code won't run	Check you're connected; try Runtime → Restart runtime
Output looks wrong	Run cells in order from the top
Lost your work	Check Google Drive → Colab Notebooks
Need more computing power	Runtime → Change runtime type → GPU
Variables seem wrong	Runtime → Restart runtime → Run all cells

Understanding Error Messages¶

When code fails, Colab shows an error message. Don't panic—these are often informative:

NameError — You're using a variable that hasn't been defined yet. Run earlier cells.
SyntaxError — There's a typo in the code. Check for missing quotes or brackets.
ModuleNotFoundError — A required library isn't loaded. Run the import cells at the top.

For course exercises, if you encounter an error you can't resolve, post in the discussion forum with: 1. The error message 2. Which notebook and cell you were running 3. What you were trying to do

Part 5: Self-Assessment Checklist¶

Before Week 1, confirm you can:

[ ] Access Google Colab and sign in
[ ] Create a new notebook
[ ] Run a code cell using Shift+Enter
[ ] Modify a variable value and re-run
[ ] Open a shared notebook and copy to your Drive
[ ] Run all cells in sequence
[ ] Restart the runtime if needed
[ ] Find your saved notebooks in Google Drive
[ ] Download a notebook to your computer

Support Resources¶

Troubleshooting guide: Available on unit LMS
Discussion forum: Post technical questions
Optional drop-in session: Week 1 for additional support (time TBC)
Video walkthroughs: Available on unit LMS

What's Next?¶

In Week 1, you'll use your Colab skills to run pre-trained AI models and observe their behaviour on clinical scenarios. The focus will be on understanding what AI does, not on writing code.

Appendix: Accessibility Notes¶

All exercises can be completed with screen reader software
Video walkthroughs include captions
Alternative formats available on request
Keyboard navigation fully supported
Works on tablets (desktop/laptop recommended for best experience)