Doing Data Science is difficult. Projects are very dynamic with requirements that evolve as data understanding grows. A wide variety of data arrives piecemeal, is added to, replaced, and contains flaws. Teams have mixed skill sets, use a variety of tooling, and are constrained by technology and time.Despite these disruptions, a Data Science team must quickly demonstrate value with traceable, tested work products.
This is when your team’s operating model is put to the test. This is when you need Guerrilla Analytics.
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 operating model 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 Data Science while producing results ready for demanding and high profile stakeholders including international bodies, state regulators and governments.