Practical Data Science is difficult. Changing data. Evolving requirements. Iterations and dead ends.
Are your projects dynamic because a wide variety of data arrives piecemeal? Are your projects dynamic because your team have mixed skill sets and must use a variety of tooling? Are your projects dynamic because Data Science is an iterative and exploratory process?
High performing practical Data Science teams are robust to dynamic projects rather than at their mercy. These teams can embrace change with confidence rather than resist change out of fear. They demonstrate quantified value quickly and confidently rather than reactively second-guessing their own analyses.
When you’re building Data Science, you need solid scaffolding you can rely on. This is the Guerrilla Analytics operating model for practical Data Science.