Data Science Workflow and Next Steps

This final lesson ties together everything you have learned and provides a framework for approaching any data science project. We will cover the end-to-end data science workflow, best practices for project organisation, and the many directions you can take your learning from here.

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The Data Science Workflow

Every data science project, regardless of domain or complexity, follows a similar lifecycle:

1. Problem Definition

"What question are we trying to answer?"

This is the most important step. A poorly defined problem leads to wasted effort.

Good question	Poor question
"Can we predict which customers will churn in the next 30 days?"	"Tell me something interesting about our customers"
"What factors most influence house prices in this region?"	"Analyse the housing data"
"Can we classify support tickets by urgency automatically?"	"Do some machine learning on our tickets"

Key considerations:

• What decision will this analysis inform?
• What does success look like? How will we measure it?
• What data is available? What data would we need?
• What are the constraints (time, budget, privacy)?

2. Data Collection