The Analytic Process

Here is our generic approach to an end-to-end data science project. This outline is based on many years of project management and product development experience in small and large enterprises.¬† For maximum generality we’ve avoided getting too specific, but we believe that all of these elements are important to a successful project.

 

Welcome to the Eureka Data Science Blog

We have tons of content planned (just not too sure when we’ll get it all published!)

There will be a category called Expertise and Assignments featuring our specialties and completed projects. Subcategories include Customer Behaviour, eHealth, Risk Modelling, and Supply Chain.

I’m planning a technical section called “3 T’s” (the name being a nod to the “3 V’s” of data science), which stands for Tips, Techniques, and Treatises.

I’m also planning a mixed section (opinion pieces and related technical information) called “3 R’s”. The first subcategory will be Rants, in which we point the finger at perceived transgressions in the world of data science. The second will be Raves, in which we dish out accolades to products, organisations, and individuals. The third will address shortcomings, or holes, in the R analytic programming language, and will be called “R’s Holes”. (To prove that I’m not just being one myself, it will also provide proposed fixes for said holes, including code and workarounds).

Thanks for visiting, and do let me know if you’d like to contribute!

— Allen Nugent