The data science swiss-army knife

The data science swiss-army knife

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Full-stack analytics… is that made up?

Maybe?

Though, I am not the originator! The first time I heard of the term I was at an Adobe conference, where I came across Frederik Werner’s Full Stack Analyst Blog.

It is a generous title that I have adopted in recent times to help communicate all of the things that I can do with data.

Some days, it means that I am a Digital Analyst pitching the value of analyzing customer fallthrough and retention in Adobe Analytics to my Product Manager.

Other days, it means that I am a Data Analyst writing SQL to query POS transaction data from the on-premises data warehouse, and running an affinity analysis in R.

Still, on other days, I am a Data Engineer & BI Developer writing API GET requests in R or Python and creating data pipelines to be ingested and transformed into an insight-rich and user-friendly PowerBI Dashboard.

And finally on whatever days that are left, I become a Data Scientist that attempts to classify unstructured text data using a mixed membership approach to topic modeling in R.

These are all the things that I have become over the years to be able to answer the problems that I find most interesting.

Full Stack Analyst / Full Stack Analyst

All jokes aside, I believe that a full-stack analyst is someone who is curious by nature and loves to apply their diverse assortment of analytical skills and techniques to solve valuable problems.

That, or a generally informed and indecisive person as it relates to careers within data & analytics.