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Doing 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 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 Data Science.
What is Guerrilla Analytics?
Guerrilla Analytics is a proven methodology for doing fast-paced Data Science. The book “Guerrilla Analytics: A Practical Approach to Working with Data” will teach you:
- The Guerrilla Analytics Principles: 7 straightforward guidelines for maintaining data provenance across the entire Data Science life cycle.
- Almost 100 best practice tips: how to deliver Data Science that is reproducible, testable and stands up to scrutiny, despite the disruptions and dynamics of a typical Data Science project.
- Building capability: how to set up a Data Science team’s people, process and technology to embrace the reality of data science projects.
Where is Guerrilla Analytics used?
Guerrilla Analytics methods have been successfully used to manage agile teams delivering complex data science projects at pace. Guerrilla Analytics is used in:
- several global banking audit functions
- Big 4 consultancies
- US government
- large UK retailers
The methods have stood the test of fast paced and dynamic projects while producing results ready for demanding and high profile stakeholders including international bodies, state regulators and governments.
