Guerrilla Analytics: A Practical Approach to Working with Data
Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off...more
The book covers common data analytics project practices, but more on a side of traditional collect-analyze-report projects. So, if you seek for an advice on how to organize work to successfully build a software product with is centered on data science applications or ...more
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