Please enable JavaScript.
Coggle requires JavaScript to display documents.
Chapter 4 - Data-Driven versus Data-Informed (A. Brief (Human should works…
Chapter 4 - Data-Driven versus Data-Informed
B. How to Think Like a Data Scientist
10 common pitfalls
Assuming the data is clean:
Simply act of data cleaning often reveal important patterns.
Not normalizing: 要包含母體
Excluding outliers: working data within extreme users
Including outliers: do qualitative research with extreme users
Ignoring seasonality
Ignoring size when reporting growth: context is critical
Metrics that cry wolf: don't set your threshold sensitive, otherwise, you will ignore data even it's critical.
The "Not collected Here" syndrome: Mashing up your data with data from other resources can lead to valuable insights.
Focusing on noise
C. Lean Startup and Big Vision
Lean Start up helps expand your vision
Start from a good place and small steps
With a bigger vision like solving problems in the world
Lean Start up advocates face:
How do you have a minimum viable product and a hugely compelling vision at the same time?
A. Brief
Human should works with data machine
Math is good at optimizing a known system ; humans are good at finding a new one.
Optimising is all about finding the lowest and highest values of a particular function.
Humans do inspirations ; machine do validations