Your First Mission
Your first week as a Data Scientist
You just joined the data science team at a retail company. The product team ran an experiment: half of users saw a new checkout (group A), half saw the old one (group B). On day two, the product lead asks:
"Group A's orders look bigger. Did the new checkout actually lift order value, or is that just luck? Tell me before we roll it out to everyone."
The tool you reach for is Python, and by the end of this 20-minute intro you will have answered that question yourself, in your browser, with zero setup.
Your mission: decide whether group A's higher orders are a real effect or just noise. You'll write real Python and reason like a data scientist, one small step at a time.
The data: two samples
You have the order value (in €) for ten users in each group:
group_a = [52, 63, 48, 72, 59, 61, 55, 68, 52, 64] # new checkout
group_b = [49, 52, 47, 55, 44, 58, 46, 51, 43, 56] # old checkout
These two lists are already loaded for you in every code box below, along with Python's built-in statistics module. You just have to ask the right questions of them.
Why Python is the data scientist's tool
Spreadsheets can average a column. Python lets you go further: measure spread, run a model, quantify how sure you are. That last part, turning "looks bigger" into "here's how confident I am," is what separates a data scientist from a dashboard. That is what this intro builds toward.
Next: let's look at the data.