Data Analyst vs. Data Scientist - What's the Difference?
With the recent boom in the data industry, there seems to be no shortage of interesting opinions about the roles and skillsets that drive this thriving field. Of late, I’ve come across many people asking one particularly intriguing question: “What distinguishes a data scientist from a data analyst?”
Many seem to carry the perception that a 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 stems from two reasons. One, different companies have different ways of defining the roles. Job titles are not always accurate portrayals of one’s actual job activities and responsibilities, and there is certainly a lot of grey area with most job titles. I’ve even seen a few startups 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. So before we attempt to understand the difference between a data analyst and a data scientist, let’s first take a historical at the analytics business and each role in that context.
Business Analytics to Data Science
As a discipline, business analytics has been around for more than 30 years, beginning with the launch of MS Excel in 1985. Before this, data analytics for business was a manual exercise, performed using calculators and trial and error. It was the launch of computer software like MS Excel and many other applications 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 exponentially more massive than it was even ten years ago. Second, new technologies have made analyzing and interpreting such vast amounts of data possible, and companies now have a means to make more impactful business decisions.
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 that could utilize this data effectively could make better business inferences and act accordingly, putting them ahead of competitors that didn’t have these insights.
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 to drive better business results.
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 glamorized professions in the world.
How the Scientist Differs from the Analyst
A data analyst deals with many of the same activities but the leadership component is a bit different. Let’s take a look at a few examples:
Normally a data scientist is expected to formulate the questions that will help a business and then proceed in solving them, while a data analyst is given questions by the business team and pursues a solution with that guidance.
Both roles are expected to write queries, work with engineering teams to source the right data, perform 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 transform data and analysis into a business scenario and roadmap.
On the Lighter Side
I came across this amazing Venn diagram recently from Stephen Kolassa’s post on a data science forum. It’s both factual and funny at the same time and puts a lot of data science responsibilities into a humorous (and yet pretty accurate) context. Hope you all 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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