Data Analyst vs Data Scientist: Career Differences

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

Data analysts are not expected to be coders, but they should know how to use visualization tools to sort through piles of data sets in order to notice certain trends or business events. Data scientists, on the other hand, should be good coders who can create algorithms, run advanced modeling techniques, and make business predictions and assessments of what the business should do now. and in the future.

What are the differences between data analysts and data scientists?

Data analysts work with datasets and visualization tools to find answers about where their business is, while data scientists need to know how to write algorithms and use advanced modeling techniques to predict where their business is. is heading where should go.

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What do data analysts do?

Data analysts work with data sets defined 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 business is facing, Josh Drew, regional manager at Robert Half Technology, told Built In.

With this data, they research trends, create charts and presentations, and help their business make important strategic decisions, according to a blog post by Northeastern University.

“Overall, most companies have and understand the need in the data space, whether it’s an analyst or a scientist, to essentially maximize productivity and maximize information in the form of data,” Drew said. “They say use the information we have to make the smartest business decisions today and tomorrow.”

What do data scientists do?

Data Scientists working with huge amounts of data and using advanced modeling techniques, statistics and coding to create algorithms to answer business questions or use the data to describe something, said Ann Blasick, Director of Career Services at Georgia Tech’s School of Industrial and Systems Engineering at Built In.

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

Steven Heuls, senior director of engineering at Red Hat, 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 for deriving a reproducible 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 don’t have to know much coding, Blasick said.

By reviewing datasets, data analysts try to answer questions such as why sales declined in a particular quarter, or why certain geographies performed better in marketing campaign results, according to the Northeastern University blog.

According to Northeastern University, skills in database management, data reporting, data mining, and data warehousing, along with SAS and SQL skills, are considered the best skills for data analysts. Additionally, a bachelor’s degree for a data analyst role will usually suffice, Drew said.

Data Scientist Skills

Data scientists should have good mathematical and statistical skills, but it is also imperative that they know how to code. These coding skills are used to build algorithms, predictive models, data modeling and also use machine learningBlasick said.

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

“If you’re not detail-oriented and you like identifying patterns, that’s probably not a big role for you and you’re going to be very, very frustrated,” Huels said. He added that the failure rate in problem solving in data science is also high, so being adept at dealing with rejection is also a key skill.

“If you’re not detail-oriented and you like identifying patterns, that’s probably not a good role for you and you’re going to be very, very frustrated.”

Presentation and communication skills are also important for data scientists.

Many people can solve math problems, type things into Python, and get answers, but articulating what the data is telling you in a context that a business person can base their decision on is extremely important.

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

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

“You’re rarely going to see someone at the PhD level as a data analyst, but you might see someone pursuing a higher level of education in statistics, math, computer science for a data scientist role. “Drew said.

Professional responsibilities

Data Analyst Job Duties

Data analysts will create dashboards or visualizations with the data that inform their business or clients about what is currently happening within the business or about trends.

“Companies have a huge amount of data and data analysts put it into an digestible format,” Blasick said. “It gives business leaders insight into what is currently happening in their business based on the massive amounts of data.”

Data Scientist Job Duties

Data scientists are considered capable of discern and identify opportunities where AI and machine learning can benefit businesses, Huels said.

Once opportunities are identified, data scientists work with subject matter experts to locate the data needed, determine what data is available to exploit and realize its value, then proceed with data integration and experimentation by executing several models. Some data scientists may also be responsible for data cleaning, Blasick said. In other cases, some companies may have data engineers to do this.

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

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Key Differences Between Data Analysts and Data Scientists

The key question to ask when debating a career between data analyst and 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 toughest data problems and thriving in a technical environment, then data science is the path for you, Blasick said.

But if you’re someone who just wants to get your hands dirty with data and doesn’t want to get so technical and would rather work with an advanced excel spreadsheet than create a full algorithm, then data analytics would be a best choice for you.

Demand for data analysts generally tends to be higher in terms of job posting volume, compared to more specialized data scientists, Drew said.

Despite increased demand for data analyst positions, the salary gap between data analysts and data scientists has widened over the past five years in favor of data scientists, according to data provided to Built In. by Robert Half Technology.

Data Analyst vs. Data Scientist Salaries

  • Year Data Analyst Data Scientist Pay Gap
  • 2022 $106,500 $135,000 $28.5,000
  • 2021 $103,250 $129,000 $25.8,000
  • 2020 $100,250 $125,250 $25,000
  • 2019 $97,500 $121,500 $24,000
  • 2018 $96,000 $119,000 $23,000
  • Source: Robert Half US Salary Guides

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

When high-skilled positions are in high demand, the pay gap tends to widen. And for positions such as data scientists, there is virtually negative unemployment, he added.

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

The career path of data analysts versus data scientists may also differ, experts said.

Data analysts can move into careers 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 can follow a career path which brings them to a role of chief data officer.

But despite these differences, one similarity stands out.

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

Sean N. Ayres