Data analysts and data scientists are two of the hottest jobs in information technology (IT)—with impressive salaries to match. But while data analysts and data scientists share some similarities, there are key differences between the careers that need to be considered before selecting a path to follow, as the individuals best suited for each vary.
While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
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How to Choose Between a Career as a Data Analyst and a Data Scientist
So, how do you determine which career path is right for you? To determine that, you should consider three key factors:
- Your educational and professional background
- Your personal interests
- Your desired career trajectory
1. Consider Your Background
While data analysts and data scientists are similar in many ways, their differences are rooted in their professional and educational backgrounds, says Martin Schedlbauer, associate clinical professor and director of Northeastern University’s information, data science and data analytics programs, including the Master of Science in Computer Science and Master of Science in Data Science.
As mentioned above, data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. They have usually earned an undergraduate degree in a science, technology, engineering, or math (STEM) major, and sometimes have an advanced degree, as well as experience in math, science, programming, databases, modeling, and predictive analytics.
Data scientists, on the other hand, design and construct new processes for data modeling and production. In addition to performing and interpreting data studies and product experiments, these professionals are tasked with developing prototypes, algorithms, predictive models, and custom analysis.
Data scientists also use a variety of techniques to comb through data, including data mining and machine learning—key differentiators between the two roles. Because of this, an advanced degree, such as a master’s or PhD, is essential for professional advancement, according to Schedlbauer.
“Data scientists are quite different from data analysts; they’re much more technical and mathematical,” Schedlbauer says. “They’ll have more of a background in computer science, and most businesses want an advanced degree.”
2. Consider Your Interests
Are you excited by numbers and statistics, or do your passions extend into computer science and business?
Data analysts love numbers, statistics, and programming. As the gatekeepers for their organization’s data, they work almost exclusively in databases to uncover data points from complex and often disparate sources. Data analysts should also have a comprehensive understanding of the industry they work in, Schedlbauer says.
While the data scientist’s job requires them to have a blend of math, statistics, and computer science, they also need to be interested in, and knowledgeable of, the business world. Data scientists should have strong presentation and communication skills, according to Robert Half Technology (RHT). They’re tasked with gleaning risks, trends, and opportunities from the data, then relaying these findings in nontechnical ways to executives.
3. Consider Your Career Path
Data scientists and data analysts differ in the level of experience they bring to a role and, in turn, compensation.
Data analysts have an earning potential of between $81,750 and $138,000, according to RHT’s 2019 Salary Guide. Because these professionals work mainly in databases, they’re able to increase their salaries by learning additional programming skills, such as R and Python.
According to PayScale, however, data analysts with more than 10 years of experience usually have maximized their earning potential and move on to other jobs. Two common career moves, after the acquisition of an advanced degree, include transitioning into a developer role or data scientist job, according to Blake Angove, director of technology services at IT recruiting firm LaSalle Network.
Data scientists—who have typically earned a graduate degree, boast an advanced skill set, and are often more experienced—are considered more senior than data analysts, according to Schedlbauer. As such, they’re compensated higher: Data scientists received the highest salary boost for IT professionals from 2016 to 2017 at 6.4 percent, landing them in the $116,000 to $163,500 salary range, according to RHT.
Which Is Right for You?
Data analysts and data scientists have job titles that are deceptively similar given the many differences they have in terms of job responsibility, educational requirements, and career trajectory.
But qualified individuals for both careers are highly coveted in today’s job market, Schedlbauer says, thanks to businesses’ strong need to make sense of, and capitalize on, their data.
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