There's been a lot of buzz about "Lean Startups," "Customer Development," "Business Model Generation" and related topics lately. And there is a real transformation in the way we design and build products at work behind all that buzz.
There’s been a lot of buzz about “Lean Startups,” “Customer Development,” “Business Model Generation” and related topics lately. And there is a real transformation in the way we design and build products at work behind all that buzz.
But the fundamental principle behind all of them is applying scientific and experimental methodologies to product design decision making. The tools of Lean Startups are:
- Data collection and analysis
- Writing hypothesis and models
- Creating experiments to test those models
- Learning from those experiments, and refining/redesigning the model
Fortunately Python is a great tool for those looking to apply data science to product design.
Python can help with everything from statistical analysis, to rapid development of viable products, to the creation of complex models that can be used to tweak the levers of growth, and it’s easy to combine these with off the shelf tools that help you analyze traffic patterns and figure out what product changes actually make a difference.