Data analyst vs. data scientist are two popular data-related careers in the technology industry. While the jobs and many of the associated work tasks are related, they aren’t identical.
So, what’s the difference between a data analyst and a data scientist? Do the responsibilities and skill sets differ? What are salaries and job outlooks like for the two career paths? In this guide, we’ll cover the following:
- Data Analyst vs. Data Scientist: Definitions
- Data Analyst vs. Data Scientist Careers
- Data Analyst vs. Data Scientist: The Bottom Line
Data Analyst vs. Data Scientist: Definitions
How do data analyst and data scientist roles compare? Data scientists usually have the skill set of a data analyst plus additional skills, explains Jenna Bellassai, lead data reporter at Forage and former senior data scientist.
“A lot of people who become data scientists start out as data analysts — but not all,” Bellassai says. “A data analyst specializes in manipulating data to create reports or dashboards, while a data scientist does a combination of data analysis, software engineering, and machine learning to create statistical models.”
Another key difference is that the data analyst role has become ubiquitous. As a result, analysts’ skills are in high demand across industries, domains, and companies of all sizes, says Aswini Thota, a lead data scientist at Bose Corporation with over 12 years of experience in data analytics.
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On the other hand, data scientists possess a deeper understanding of mathematics and computer science. Data scientists are most valued for their ability to build accurate models to inform strategic decision-making.
When considering data analyst and data scientist careers, it’s important to keep in mind some factors that distinguish each of these data-focused jobs. “Data analysts use their extensive domain knowledge and analytical skills to explain the factors that have caused an outcome,” Thota says. “In contrast, data scientists develop predictive models that can ‘learn’ the latent relationships and rules within historical data to predict future results.”
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Data Analyst vs. Data Scientist Careers
Although some elements these data professionals work on overlap, significant differences emerge when comparing responsibilities, skills, and salaries.
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Data Analyst vs. Data Scientist Responsibilities
Data analysts’ responsibilities focus on understanding business problems, identifying relevant datasets, cleaning and blending data, and analyzing it to help businesses make informed decisions. They begin by collecting data from various sources, such as databases, spreadsheets, and other enterprise systems.
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On the other hand, data scientists’ responsibilities primarily involve developing and implementing predictive models and algorithms to solve strategic business problems. They start by understanding the problem they’re trying to solve and identifying the data needed to solve it.
However, the roles have similarities as well. “The main goal of both data analysts and data scientists is to use data to inform strategy and business decision-making,” Thota explains. “They aim to leverage historical data assets to extract insights that drive business growth.” Data gathering, data cleaning, and data analysis are responsibilities that both data analysts and data scientists share.
Thota identifies the following responsibilities for data analyst and data scientist roles:
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Data Analyst vs. Data Scientist Skills
While data analysts and data scientists require similar skills to perform data cleansing, transformation, and analysis, each career path requires specific hard and soft skills.
“Data scientists need to have a more comprehensive understanding of statistical modeling and machine learning algorithms, as well as programming and cloud computing skills,” Thota says. “On the other hand, data analysts need to be adept at using data analysis tools, data visualization, and reporting techniques and possess excellent communication and collaboration skills to work effectively with stakeholders.”
Hard skills that data analysts need include:
- Ability to use data querying tools such as Excel, SQL, and Python
- Proficiency in analytical tools such as Tableau, Power BI, or QlikView
- Knowledge of statistics
- Expertise with data cleansing techniques.
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Soft skills that can help data analysts in their job include strong communication and collaboration skills to work effectively with team members.
Data scientists also rely on hard technical skills, including:
- Strong programming skills in languages such as Python and R
- Expert-level understanding of machine learning algorithms and statistical modeling techniques
- Familiarity with big data technologies like Hadoop, Spark, or NoSQL databases.
Soft skills that can benefit data scientists include problem-solving skills and aptitude to identify and solve complex data problems, and the ability to work in a team environment.
In the following chart, Thota highlights the key skill sets required for data analyst vs. data scientist roles, as well as some common skills that are useful for both positions:
Data Analyst vs. Data Scientist Salary
Though data analysts and data scientists have some overlap in their skills and responsibilities, data scientists can command significantly higher salaries. “Data scientists, on average, make a higher salary than data analysts due to their specialized skill set and robust understanding of computer science and machine learning,” Thota explains.
According to Glassdoor, the average total pay for data analysts in the U.S. is around $70,000, with senior-level titles making upwards of $114,000. In contrast, the average annual salary for data scientists is just over $126,000, with the potential for lead data scientists and those with extensive experience to make more than $162,000 in total pay.
The U.S. Bureau of Labor Statistics (BLS) notes that the job outlook is well above average for operations research analysts, a job category that includes data analysts. From 2021 to 2031, data analyst jobs are projected to grow by 23%, much faster than average for all occupations.
For data scientists, the job outlook is even better, according to the BLS. This field is projected to see employment growth of 36% between 2021 and 2031, with 40,500 new jobs expected to be added during that time.
Data Analyst vs. Data Scientist: The Bottom Line
Data analysts and data scientists both use data to inform strategy and business decision-making by extracting insights from data that drive business growth. These two in-demand career paths offer professionals the opportunity to use data-driven decision-making to shape an organization’s future.
However, each role has its unique focus, so the path you choose should reflect your career interests. If you’re more interested in using data to flex your domain knowledge and analytical skills and help to explain the factors that lead to an outcome, then you may enjoy being a data analyst. But if you like developing models and algorithms that can drill down into historical data to predict future results, you’d likely prefer being a data scientist.
Which Career Path Is Right for You?
When determining which career direction might be the best fit, Thota emphasizes that it’s essential to understand the complexity of the problems you prefer to work with. “Data analysts usually work with structured and well-defined datasets to solve specific business problems, while data scientists may work with unstructured datasets to predict and quantify the unknown,” he says.
It’s also important to think about your aptitude for programming and your appetite for tactical vs. strategic solutions.
“While both data analysts and data scientists use programming tools like SQL, Python, R, and Tableau, the way they use them and the objectives they aim to achieve can be different,” Thota says. “Do you prefer communicating insights or building scalable systems? Data analysts are valued for the insights they bring to the table, whereas data scientists are expected to build scalable models that provide predictive capabilities.”
He adds that business leaders often rely on data analysts to provide quick insights or tactical recommendations to help them course-correct or mitigate immediate risks. Data scientists, on the other hand, should be comfortable with longer-range outcomes. “Most of the projects that data scientists work on are strategic, and it may take months or even years to operationalize predictive models fully,” Thota concludes.
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