Agile Data Science – Part 1
Massive amounts of data being generated and collected across virtually all parts of business ecosystems – manufacturing, supply chains, marketing, […]
Massive amounts of data being generated and collected across virtually all parts of business ecosystems – manufacturing, supply chains, marketing, […]
In Agile Data Science – Part 1 we reviewed some of the fundamental challenges of doing data science with agility,
Consider parts 1-2 as Agile for Data Scientists. This addendum could be considered Data Science for Agilists, and seeks to illustrate
“Scrum exposes every inadequacy or dysfunction within an organization’s product and system development practices. The intention of scrum is to
Try this … Get proposed feature list Assign “business value points” using Fibonacci – just relative ranking – use Planning
“We place the highest value on actual implementation and taking action. There are many things one doesn’t understand and therefore,
“Mistakes are the portals of discovery.” – James Joyce, Ulysses “Science, my lad, is made up of mistakes, but
“Without data you’re just another person with an opinion.” W. Edwards Deming. The project is underway and I need a
Ideal Iteration Length – A survey Recently I put the question of the rationale for a max sprint length of
Agile software development has its roots in the lean manufacturing paradigm developed at Toyota – the Toyota Production System (TPS).
Our highest priority is to satisfy the customer through early and continuous delivery of valuable software. So says the first