Are Data Analytics and Data Science Two Separate Fields?

By John Thompson, author of the book Building Analytics Teams, explains the motive behind the existence of two different terms associated with data

Data analytics has been with us since the beginning of time. People have always been interested in analysing and understanding data of all types. For example, in support of my new book Building Analytics Teams, we have been requesting that readers post reviews to Amazon.com. We have been keeping track of the number of reviews requested, the number of people who agree to provide a review, and the number of completed reviews. This is a simple example of data analytics. Data analytics is inherent in, and part of, data science.

Data Science is an extension of data analytics. Data Science is a new field that extends data analytics and includes new elements of the process including: stakeholder management, data visualization and numerous other activities that broaden the data analytics portion of the work to include a myriad of analytical approaches and brings in the business people, technology support staff, external data providers and other interested parties to ensure that the data science process is providing a comprehensive and complete solution to the business problem being solved.

To put it simply, data analytics is what you do, data science is the process in which it is executed.
To reinforce the contextual point that I started with, this is a definition from one perspective, if you ask another person, you may receive a completely different answer from a different viewpoint that is not only completely true and valid, but informative as well.

Both data analytics and data science enthusiasts who aim to begin their journey often wonder – what should be the starting point? Well, it depends on where they are starting their journey.

If they are in the process of completing their undergraduate or graduate studies, I would suggest that they take classes in programming, data science or analytics. If they are professionals, I would suggest that they take classes on Coursera, Udemy or any other on-line educational platform to see if they have a real interest in, and affinity for, analytics. If they do have an interest, then they should start working on analytics for themselves to test out analytical techniques, apply critical thinking and try to understand what they can see or cannot see in the data. If that works out and their interest remains, they should volunteer for projects at work that will enable them to work with data and analytics in a work setting. If they have the education, the affinity and the skill, then apply for a data science position. Grab some data and make a difference!

About the author

John Thompson is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics & Artificial Intelligence team and efforts at CSL.