Massive amounts of data being generated and collected across virtually all parts of business ecosystems – manufacturing, supply chains, marketing, online advertising, customer relationship management, social media – making business operations open to data mining for business problem solving and competitive advantage. The work of a data science team (or if you prefer, data engineering…
In Agile Data Science – Part 1 we reviewed some of the fundamental challenges of doing data science with agility, specifically, how to produce and demonstrate ‘stakeholder value’ within a typical agile time-box of 2-4 weeks. In part 2, we will consider a different approach based on the Data Value Pyramid. The difference in this…
Consider parts 1-2 as Agile for Data Scientists. This addendum could be considered Data Science for Agilists, and seeks to illustrate some of the fundamental concepts of data science and machine learning referred to in earlier posts. Definitions: Data Science vs. Machine Learning Data Science is a field that studies how to extract meaning from data,…