Data Analyst vs Data Scientist - What's the Difference?
With the recent boom in the data industry, topics to do with the field of data science have become interesting talking points. Of late, I’ve come across many people asking an intriguing question about the field: what distinguishes a data scientist from a data analyst?
Many seem to carry the perception that data scientist is just a glammed up term for the data analyst role. Here are a few comments I came across on twitter while writing this post -
I think a lot of the confusion is due to 2 reasons. One, different companies have different ways of defining the roles. I’ve seen a few startups actually use data scientist as a fancy designation to attract talent for their analyst roles. Second, Data science is a nascent field and not everyone is familiar with the inner workings of the industry.
Before we attempt to understand the difference between a Data Analyst and a Data Scientist, let’s understand the context.
Business Analytics to Data Science
As a discipline, Business Analytics began has been around for 30 years, beginning with the launch of MS Excel in 1985. Before this, data analytics for business was rudimentary, performed manually using calculators and trial and error. It was the launch of computer software like MS Excel and many others that kick-started the business analytics wave.
Likewise, two major trends contributed to the start of the Data Science phenomenon. First, the use of technology in various walks of life and the internet in particular led to an unprecedented data boom. The kind of information now available for many businesses to use in decision-making is nearly infinite in comparison to the reality of, say, 10 years ago. Second, new technologies have made analyzing and interpreting such vast amounts of data possible, and companies can now use all this data in decision-making.
[Related reading: How to build a career in Data Science]
Advent of the Data Scientist
Businesses saw the availability of such large volumes of data as a source of competitive advantage. It was clear that companies which could utilize this data effectively would take better decisions and would be ahead of the curve.
To make sense out of such data, the need arose for a new skillset – a profile that included business acumen, customer/user insights, analytics skills, statistical skills, programming skills, machine learning skills, data visualization, and more.
This led to the emergence of the data scientist role – a person who combines sound business understanding, data handling, programming and data visualization skills.
A data scientist is expected to directly deliver business impact through information derived from the data available. And in most cases a data scientist needs to create these insights from chaos, which involves structuring the data in the right manner, mining it, making relevant assumptions, building correlation models, proving causality, and searching the data for signs of anything that can deliver business impact throughout.
In just a few years since its conception, data science has become one of the most celebrated and glamourized professions in the world.
Differences Between the Two Roles
Now that we’ve seen what the data analyst and data scientist roles are all about, let’s take a closer look at the key differences between the two:
- Normally a data scientist is expected to formulate the questions that will help business and then goes on to solve for them, while a data analyst is given questions by the business team and solves for them.
- Both roles are expected to write queries, work with engineering teams to source the right data, data munging (getting data into the right format, convenient for analysis/interpretation) and derive information from data. However, in most cases a data analyst is not expected to build statistical models or be hands-on in machine learning and advanced programming. Instead, a Data Analyst typically works on simpler structured SQL or similar databases or with other BI tools/packages.
- The data scientist role also calls for strong data visualization skills and the ability to convert data into a business story. A data analyst is normally not expected to be adept at business and in advanced data visualization.
In a Lighter Vein
I came across this amazing Venn diagram recently from Stephen Kolassa’s post on a data science forum. Its both factual and funny at the same time. Hope you guys enjoy it as much as I did.
Above: Data Scientist Venn Diagram sourced from Stephen Kolassa’s comment in Data Science Stack Exchange.
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