Data analyst vs. data scientist

Data is everywhere. With the right tools and skills, you can use data to make predictions and solve complex problems. If you’re interested in working with data, you may want to consider becoming a data analyst or data scientist. Learn more about the differences between data scientist and data analyst career paths.

The program cards/tables featured on this page were last updated in June 2026. For the most current program information, please refer to the official website of the respective school.

The world is becoming increasingly reliant on data, and that’s a great sign for anyone interested in a data-driven career. According to the U.S. Bureau of Labor Statistics (BLS), some data occupations — including data scientists and analysts — are projected to grow by as much as 34% between 2024 and 2034. That’s much higher than the average for all occupations.

What does a data analyst do?

When a company wants to make sense of data — whether it’s been collected in-house or elsewhere — they often rely on data analysts to make sense of all the information. Data analysts may be responsible for cleaning and formatting data before identifying trends that can help business leaders make strategic decisions.

Conducting data analysis involves a variety of tools, skills and computing languages to perform statistical analyses and answer questions to solve organizational challenges. A data analyst may use a query language like SQL, programming language like R and SAS, and visualization tools like Power BI and Tableau in the course of their work. This often involves figuring out how to deal with missing data.

Strong communication skills are also useful in data analysis. Data analysts are often required to convey their findings to outside teams or stakeholders, explaining their reasoning and research to justify their conclusions.

What does a data scientist do?

Data scientists’ work is focused on creating the algorithms and predictive models that data analysts use to collect, sort, and analyze information. They help to develop tools and methods to extract information, create automation systems to eliminate routine work, and build data frameworks tailored to their organization.

While data scientists often perform different tasks from data analysts, these roles can overlap. As a more senior role, a data scientist often has a background in data analysis. This allows them to understand how analysts approach their work and build solutions that generate relevant insights.

Soft skills, such as business intuition, critical thinking, and innovative problem solving, are also important in this advanced position. If you can stay one step ahead of your organization’s challenges, you can prove to be a highly valuable asset and stay competitive as a professional.

• A data analyst makes sense out of existing data through routine analysis and writing reports. A data scientist works on new ways to capture, store, manipulate, and analyze that data.
• A data analyst works toward answering business-related questions. A data scientist works to develop new ways to ask and answer those questions.
• A data analyst relies on database software, business intelligence programs, and statistical software. A data scientist uses Python, Java, and machine learning to manipulate and analyze data.

No matter which path you choose, keep in mind that both careers might require at least a bachelor’s degree in a quantitative field, such as mathematics, computer science, or statistics. If you love working with numbers and enjoy computer programming, becoming a data analyst or scientist will give you the opportunity to develop actionable insights for your organization.

Data analyst vs. data scientist: Education and work experience

Education: As mentioned above, becoming a data analyst or data scientist might require at least a bachelor’s degree in a quantitative field. However, some analysts may have a bachelor’s degree in business with an analytics focus. According to a 2025 report by O*NET OnLine, 5% of business intelligence analyst jobs require an associate’s degree, 68% of business intelligence analysts require a bachelor’s degree, and 23% require a master’s degree.

A 2023 Burtch Works study of salaries for data scientists and predictive analytics roles [PDF, 1 MB] revealed that 31% of professionals surveyed held a bachelor’s degree, 57% held a master’s degree and 12% had earned a doctoral degree. The study also found that professionals with advanced degrees earned higher salaries than those with a bachelor’s degree.

Work experience: The Burtch Works study also noted that employers are placing a strong emphasis on hiring knowledgeable candidates who require little to no training. You may be able to gain relevant experience in a data science boot camp or master’s program in data science.

Data analyst vs. data scientist: Roles and responsibilities

A data analyst or data scientist’s role and responsibilities can vary by industry and organization. It can be helpful to read through job descriptions to gain an understanding of what a particular company expects from its data professionals. As you do, keep in mind that some companies use the two position titles interchangeably. This means that in some cases, job postings for data scientists will really involve typical data analyst skills and responsibilities, and vice versa.

Here are a few common job responsibilities for each role to help you determine whether an employer is looking for skills in data analytics or data science.

Data analyst responsibilities:

• Data querying with SQL
• Data analysis and forecasting with Excel
• Creating dashboards with business intelligence software
• Performing various types of analytics (descriptive, diagnostic, predictive, or prescriptive)

Data scientist responsibilities:

• Mining data with APIs or ETL pipelines
• Cleaning data with programming languages such as Python and R
• Performing statistical analysis
• Creating programming and automation techniques to simplify day-to-day processes
• Developing data infrastructures

Data analyst vs. data scientist: Skill comparison

To better understand how data analyst skills and data scientist skills compare, take a look at some of the more common tools and processes each role relies on.

Data analytics vs. data science: How the two careers are different

While data analysts and data scientists can work on the same teams and projects within an organization, their career paths are not necessarily the same. Explore the professional opportunities for both roles to help you determine the right option for you.

Career growth

In an entry-level data analyst role, your main responsibilities will likely involve reporting and creating dashboards. From there, you might move on to a strategic or advanced analytics role, which can prepare you to manage a team after a few years in the field. Finally, you may continue your education and transition to a data scientist role.

As an entry-level data scientist, you may work with a team to conduct advanced research and analytics and gain relevant experience working with algorithms and statistical models. Depending on your goals, you may hone your leadership skills and become a data science manager. You may take your career even further and take on a director-level position or become a freelance data consultant.

Other related careers

For more information on careers in data science, check out these helpful guides:

• Tech bootcamp guide
• Data analytics bootcamp guide
• Coding bootcamp guide
• Online IT courses
• STEM education resources

Data last updated: June 2026