Data Scientist or Data Analyst - Who's Really Driving?

A few of the most popular jobs on icrunchdata recently have been Data Scientist, Data Analyst, Data Engineer and Data Analytics Engineer. So do these job titles all really just mean the same thing or are there differences?

Venu Anuganti wrote an article giving a number of detailed differences that clearly define Data Science and Data Analytics. Venu defines Data Science as “One who understands the data and business logic and provides predictions by sampling the current business data.”  He also mentions that without a PhD in math, physics, statistics, machine learning or computer science, it’s unlikely to be hired as a Data Scientist.  This person makes business decisions by interpreting the results and predicting the outcome and impact to the business.

The definition that Venu gives for a Data Analytics Engineer “is a logical extension to Data Warehousing and Business Intelligence which provides complete insights into business data in the most usable form. The major difference in warehousing to analytics is analytics can be real-time and dynamic in most cases where as warehouse is ETL driven in off-line fashion.”  The Data Analysts and Data Analytics Engineers are performing the analysis on the data and collaborating with the Data Scientists once the analysis is completed.

He finishes by stating that “data science is a data consumer within the business unit and solely depends on data provided by the data analytics team.”

So the takeaway is that one side can’t be successful without the other. Data Analytics is the engine of the Ferrari but without an experienced Data Scientist at the wheel it’s just a cool car running in circles or worse yet, just sitting looking pretty but not going anywhere.

Article written by Todd Nevins
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