Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data scientist and data analyst job titles are often used interchangeably. However, the two roles are quite different — as are the skills needed for each career. 

Data analysts aren’t expected to be coders but they do need to know how to use visualization tools to sort through heaps of data sets to notice certain business trends or occurrences. Data scientists, on the other hand, are expected to be strong coders with the ability to create algorithms, run advanced modeling techniques and make company forecasts and assessments of what the business should be doing now and in the future.

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What Do Data Analysts Do? 

Data analysts work with defined data sets using visualization tools, such as Tableau Software and Microsoft Power BI.They try to interpret and make assumptions based on that data for the current situation their company is facing, Josh Drew, regional director at Robert Half Technology, told Built In. 

With this data, they look for trends, create charts and presentations and assist their company with making important strategic decisions, according to a blog post by Northeastern University.

“Across the board, most companies have and understand the need within the data space, whether it’s an analyst or scientist, to essentially maximize productivity and maximize information in the form of data,” Drew said. “They’re saying let’s use the information in front of us to make the smartest business decisions today and tomorrow.”

What Do Data Scientists Do? 

Data scientists work with huge amounts of data and use advanced modeling techniques, statistics and coding to create algorithms to answer business questions or use the data to describe something, Ann Blasick, director of career services at Georgia Tech’s Industrial and Systems Engineering school, told Built In.

Data scientists also use their mountain of information to answer questions about the future via predictive analytics that seek potential outcomes and prescriptive analytics that examine outcomes and search for more options, she added. 

Red Hat’s Steven Huels, senior director of engineering, said companies like to employ data scientists to deliver value to the business, customers, or both, through the use of analytics, machine learning, or deep learning techniques to derive repeatable value from the data. 

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Data Analyst Skills vs. Data Scientist Skills

Data Analyst Skills 

Data analysts need to be good at math and statistics but they aren’t required to know a lot of coding, said Blasick. 

In examining data sets, data analysts try to answer questions such as the reasons for a drop in sales during a particular quarter, or why certain geographic regions yielded better marketing campaign results, according to the Northeastern University blog post.

Database management, data reporting, data mining and data warehousing skills, as well as SAS and SQL skills, are considered top skills for data analysts, according to Northeastern University. Additionally, a bachelor’s degree for a data analyst role will usually suffice, Drew said. 

Data Scientist Skills

Data scientists need to possess good math and statistics skills, but it’s also imperative they know how to code. These coding skills are used to construct algorithms, predictive models, data modeling and also use machine learning, Blasick said. 

Data scientists also need to be detail-oriented and possess the ability to recognize patterns, as well as enjoy the process of learning, Huels told Built In. 

“If you’re not detail-oriented and like identifying patterns, then this is probably not a great role for you and you’re going to get very, very frustrated,” said Huels. He added the failure rate in solving problems in data science is also high, so being adept in handling rejection is also a key skill.

“If you’re not detail-oriented and like identifying patterns, then this is probably not a great role for you and you’re going to get very, very frustrated.”

Presentation and communication skills are also important for data scientists. 

A lot of people can do math problems, punch some things into Python and get answers back, but articulating what the data is telling you in a context that a business person can base their decision on is extremely important. 

“I’d be willing to bet that in the industry there are a lot of really, really great ideas that get missed because no one really understood it,” Huels added. “You’re not going to base your business, customer satisfaction, or legal liability on something you don’t understand. So being able to bridge that gap between the technical and business aspect is huge.”

Data scientists also often earn master’s degrees and PhDs. 

“Rarely are you going to see someone at the PhD level as a data analyst, but you might see somebody pursue a higher level of education within statistics, mathematics, computer science for a data scientist role,” Drew said. 

Job Responsibilities

Data Analyst Job Duties

Data analysts will create dashboards or visualizations with the data that informs their company or customers about things that are currently going on within the business or trends. 

“Businesses have a huge amount of data and data analysts put it into a digestible format,” Blasick said. “This gives business leaders insights into what is currently going on in their business based on the huge amounts of data.”

Data Scientist Job Duties

Data scientists are looked upon to be able to discern and identify opportunities where AI and machine learning can be advantageous to businesses, Huels said.

Once opportunities have been identified, data scientists work with subject matter experts to locate the needed data, determine which data is available to mine and realize its value, then do the data integration and experimentation running multiple models. Some data scientists may also be responsible for cleaning up the data too, Blasick said. In other cases, some companies may have data engineers to do that.

“Computers are really good at giving you answers after you ask a question. What your job becomes is how do you know if that’s a valid answer,” Huels explained. “How do you know the prediction it’s giving you is actually correct?”

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Key Differences Between Data Analysts vs. Data Scientists

The key question to ask yourself when debating a career between a data analyst vs data scientist comes down to one thing, experts said.

“I think the million dollar question is how technical do you want to be?,” Blasick said. 

If you enjoy coding like working with SQL, solving the hardest data problems out there, and thrive in a technical environment, then data science is the path for you, said Blasick.

But if you’re someone who just wants to get your hands dirty with data and doesn’t want to get as technical and prefers working with an advanced Excel spreadsheet rather than building out a full algorithm, then data analytics would be a better choice for you.

Demand for data analysts generally tends to be higher in terms of the volume of job openings, compared with data scientists who are more specialized, Drew said. 

Despite the higher demand for data analyst positions, the salary gap between data analysts vs data scientists has been widening over the past five years in favor of data scientists, according to data supplied to Built In from Robert Half Technology.

“You see this happening with highly specialized skills, like an enterprise architect as opposed to a software engineer,” Drew said.

When highly skilled positions are in high demand, the compensation gap tends to widen. And for positions such as data scientists, there’s virtually a negative unemployment rate, he added.

“First and foremost ... They are always passionate about numbers.”

The career path for data analysts vs data scientists can also differ, experts said. 

Data analysts may veer towards a career on the business side of the house, such as focusing on business strategy or project management, Blasick said. But for data scientists, especially those with good communication and presentation skills, they may follow a career path that takes them to a chief data officer role. 

But despite these differences, one similarity stands out.

“First and foremost,” said Drew, “They are always passionate about numbers.”