Be prepared
As prerequisites for the course, we recommend becoming familiar with the following:
- Browse the SciLifeLab Data-Driven Life Science (DDLS) initiative to understand national priorities and the concept of the data life cycle, which is central in this course.
- Refresh core Python basics (variables, data types, control flow, functions, modules, simple plotting, reading/writing files). See the resources below.
Technical setup for labs (all online):
- A computer with reliable internet access
- A modern browser (e.g. Chrome)
- A Google account (for Google Colab and Drive storage)
- (Optional but encouraged) Accounts for AI coding/assistant tools (e.g. ChatGPT); free tiers are sufficient
- A GitHub account for versioning and sharing notebooks/code
Build a foundation in Python
As a warm‑up, ensure you are comfortable coding in Python. If you are new or rusty, use any of the options below. There is no submission requirement—this is for your own preparation.
Option A: Guided practice with AI
Use curated prompts to accelerate review: Learn Python with ChatGPT. Treat AI output critically—run code, fix errors, and keep notes of what you clarified.
Recommended minimal competency checklist:
- Running cells in Colab / Jupyter
- Using
print, f-strings, and basic input/output - Lists, tuples, dictionaries, sets (creation, indexing, iteration)
- Control flow (
if,for,while, list comprehensions) - Defining functions; understanding scope and return values
- Importing standard libraries (
math,random,json,pathlib) - Basic plotting with
matplotliborseaborn - Reading CSV/TSV data with
pandas - Simple error handling (
try/except)
Option B: Traditional refresher
Use the interactive notebook below or other beginner tutorials (e.g. Python docs, Software Carpentry). Progress until the checklist above feels easy.
Interactive Notebook
Quick Python basics: 
Or load locally in Jupyter Lite:
If you are new to programming, you may also watch this introductory video:
Quick Quiz (self-check)
What is the difference between lists and tuples?
Lists
- Lists are mutable - they can be changed
- Slower than tuples
- Syntax:
a_list = [1, 2.0, 'Hello world']
Tuples
- Tuples are immutable - they can’t be changed
- Tuples are faster than lists
- Syntax:
a_tuple = (1, 2.0, 'Hello world')
Is Python case-sensitive?
Yes
How do you execute a single cell in Colab/Jupyter and restart the kernel?
Run the cell with Shift+Enter (or the play icon). Restart the kernel via the Runtime / Restart runtime (Colab) or Kernel / Restart Kernel (Jupyter) menu.
Give an f-string that prints variable x=5 as ‘Value: 5’
x = 5
print(f"Value: {x}")
Show one operation that differs between a list and a tuple
Lists are mutable: a = [1,2]; a.append(3) works. Tuples are immutable: t = (1,2); t.append(3) raises AttributeError.
List comprehension: create a list of squares for numbers 0-4
squares = [i*i for i in range(5)] # [0,1,4,9,16]
Write a function ‘mean_or_none(values)’ returning the mean or None if empty
def mean_or_none(values):
if not values:
return None
return sum(values)/len(values)
What does ‘from pathlib import Path’ enable?
It imports the Path class for object-oriented filesystem paths (joining, reading, iterating) in a cross‑platform way.
Minimal matplotlib example plotting y = x^2 for x=0..4
import matplotlib.pyplot as plt
x = list(range(5))
y = [i**2 for i in x]
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('x^2')
plt.show()
Read a CSV ‘data.csv’ into a pandas DataFrame and show first 3 rows
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head(3))
Wrap code to catch a ValueError when converting input to int
try:
n = int(user_input)
except ValueError:
n = None
Explain scope: why does this fail? ‘def f(): x+=1’ before x=0
Inside the function, x += 1 tries to assign to local x before it exists; Python treats x as local due to assignment. Use global x or pass/return a value instead.