Data Analyst vs. Data Scientist - What's the Difference?

There are many – often quite different – opinions about the roles and skillsets that drive this thriving field, which creates much confusion. So, what distinguishes a data scientist from a data analyst?

Many seem to carry the perception that a data scientist is just an exaggerated term for a data analyst. Upon searching for “what does data scientist do,” I came across a few funny comments on Twitter while writing this post:

The fact that different companies have different ways of defining roles is a significant reason for this confusion. In practice, titles don’t always reflect one’s actual job activities and responsibilities accurately. For instance, some startups use the title “data scientist” to attract talent for their analyst roles.

Besides, 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 look at the analytics business and each role in that context.

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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 the means to make more impactful business decisions.

[Read: Data Analyst: 6 Highest Paying Industries Around the World]

A Day in the Life of a Data Analyst

On a day to day basis, a data analyst will gather data, organize it and use it to reach insightful conclusions. Companies in almost all industries can benefit from the work of data analysts, from healthcare providers to retail stores. Data analysts spend their time developing new processes and systems for collecting data and compiling their conclusions to improve business.

Data Analyst Job Description

1. Delivering reports
2. Examining patterns
3. Collaborating with Stakeholders: On of the data analyst roles and responsibilities includes collaborating with several departments in your organization including marketers, and salespeople. You will also work with peers involved in data science like data architects and database developers.
4. Consolidating data and setting up infrastructure: This is the most technical aspect of an analyst’s job is collecting the data itself. Consolidating data is the key to data analysts. They work to develop routines that can be automated and easily modified for reuse in other areas.

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A Day in the Life of a Data Scientist

For businesses and organizations that can learn and benefit from that data, the explosive growth seems like a dream come true. A data scientist is an expert in statistics, data science, Big Data, R programming, Python, and SAS, and a career as a data scientist promises plenty of opportunity and high-paying salaries. 

Harvard Business Review has declared data science the sexiest job of the 21st century, and IBM predicts demand for data scientists will soar 28% by 2020.  

Data Scientist Job Description

Data scientists are primarily problem solvers. Data scientists seek to determine the questions that need answers, and then come up with different approaches to try and solve the problem. Some of the data-related tasks that a data scientist might tackle on a day-to-day basis include:

• Pulling, merging and analyzing data
• Looking for patterns or trends
• Using a wide variety of tools like Tableau, Python, Hive, Impala, PySpark, Excel, Hadoop, etc to develop and test new algorithms
• Trying to simplify data problems and developing predictive models 
• Building data visualizations
• Writing up results and pulling together proofs of concepts

The 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 the massive amounts of data, the need arose for professionals with a new skill set – 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 data scientist jobs – people who combine 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.

Data Scientist vs. Data Analyst Skills Comparison

So, what does a data analyst do that’s different from what a data scientist does? 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:

1. Usually, 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 to pursue a solution with that guidance.
   
   
2. Both roles are expected to write queries, work with engineering teams to source the right data, perform data munging (getting data into the correct 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.
   
   
3. The data scientist role also calls for strong data visualization skills and the ability to convert data into a business story. Data analyst's jobs typically don’t require professionals to transform data and analysis into a business scenario and roadmap.

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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. I 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.