The article outlines how data analysts extract insights from structured data for business use, while data scientists apply advanced techniques to raw data to forecast trends and solve complex problems.
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Approved and verified accurate by the assistant dean of the College of Engineering and Technology on April 21, 2025.
The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.
Both of these professionals work with data and various technological tools. However, data analysts typically work with structured data and have a business purpose, whereas data scientists work with raw data, develop algorithms and have a forward-looking objective.(See disclaimer 1)
There are plenty of technology-related careers to consider as you think about what you want your future to look like. First, you can start narrowing down your career options by taking a closer look at the differences between data analysts vs. data scientists, such as their differing roles and responsibilities, essential skills needed and career pathways.
If you’re trying to decide between a career as a data scientist vs. data analyst, it can be helpful to take a look at the types of industries in which these professionals might work.
Data analysts are often hired by the following industries:
Some of the top industries hiring data scientists include the following:
When comparing a data analyst to a data scientist, there are some differences in their respective career pathways. A data analyst is typically expected to have a bachelor’s degree, although the major can vary. Common majors for data analysts include computer science, statistics and math. However, computer science is highly recommended because data analysts are expected to be able to use computer programming languages.(See disclaimer 2 )
Aspiring data analysts can also benefit from gaining some work experience by searching for relevant internships in the field. It’s also common for them to undergo a period of on-the-job training when they start working in a data analyst role. A data analyst might later decide to earn various professional certifications.(See disclaimer 2)
The typical data scientist career path is slightly longer than that of the data analyst. Starting in high school, future data scientists can benefit from taking as many math classes as possible, particularly calculus, statistics and probability.(See disclaimer 3)
Aspiring data scientists should then earn a bachelor’s degree in a field such as math, statistics, engineering, business or computer science.
Unlike data analysts, data scientists are also commonly expected to have a graduate degree,(See disclaimer 3 )such as a master’s in data science or doctorate in data science. Some data scientists may also have work experience pertaining to the industry they are working on. For example, a data scientist who works for an asset management company might previously have worked in the finance industry in a role other than data scientist.(See disclaimer 3)
Are you still not quite sure whether you want to become a data analyst or a data scientist? Because both careers can benefit from a degree in computer science,(See disclaimer 2,3) you could enroll in a bachelor’s degree program now while continuing to think about which role might suit you best. A Bachelor of Science in Computer Science with an Emphasis in Big Data Analytics degree program could be a good fit.
Grand Canyon University offers a diverse selection of STEM degrees for students who enjoy working with computers and data. Start your postsecondary education by earning a Bachelor of Science in Computer Science with an Emphasis in Big Data Analytics, which teaches object-oriented programming, algorithms, data structures, big data processing and much more. If you aspire to pursue potential career advancement, apply for enrollment to the Master of Science in Data Science degree, which explores advanced competencies in machine learning operations, data modeling and data visualizations.
Get started today by filling out the form on this page to request a consultation with a university counselor.
The U.S. Bureau of Labor Statistics (BLS) is the agency responsible for tracking employment data in the United States, as well as making job growth projections. Although the BLS doesn’t offer data specific to data analysts, it may be helpful to know that the role of the data scientist is sometimes perceived as the more advanced version of the data analyst.(See disclaimer 1) That is, a data analyst might decide to work their way up to the role of data scientist.
According to the BLS, data scientists earned a median annual salary of $108,020 as of May 2023.(See disclaimer 6) It’s possible that data analysts earned somewhat less than this, given that data scientists hold a more advanced role than analysts.(See disclaimer 1)
The BLS states that the projected job growth rate for data scientists is 36%, much faster than average, from 2023 through 2033. This indicates that employers expect to hire about 73,100 new data scientists during this time period.(See disclaimer 7)
The terms data analyst and data scientist may sometimes be used interchangeably, but they are not the same type of professional. The main difference between a data analyst and a data scientist is that an analyst combs through existing, structured data in order to identify patterns, whereas a data scientist applies advanced techniques and technologies to raw data in order to build predictive models and discover solutions to complex problems.
Take a closer look at the roles and responsibilities of a data scientist vs. data analyst.
In other words, data analysts typically explore data sets to identify trends and generate insights based on those findings. Their goal is to use those insights to solve business problems, such as determining how much inventory to keep in stock or how to competitively price a new product.
Some of the typical responsibilities of a data analyst can include:
Data scientists use advanced technological skills and often work with vast data sets, referred to as “big data.” They work with computer modeling and machine learning algorithms, and they can develop predictive modeling processes that may analyze either structured or unstructured data.
Some of the responsibilities of a data scientist may include:
There is some overlap in the technical and soft skills required for a data analyst vs. data scientist, but in general, the two professionals rely on different skills. For example, a data analyst must have knowledge of foundational math and basic statistics, whereas a data scientist needs to know advanced statistics, as well as predictive analytics.(See disclaimer 1)
Other skills a data analyst needs include:(See disclaimer 1)
Some of the skills a data scientist relies on include:(See disclaimer 1)
