What’s the Difference Between a Data Analyst and a Data Scientist?

Data analysts and data scientists are two of the most in-demand, high-paying jobs, according to the World Economic Forum Future of Jobs Report 2025 [1]. Both roles work with data, but they do so in different ways.

In this article, we'll compare data analysts and data scientists, including their job responsibilities, the skills they use, key differences, and what you can do to pursue each career. Afterward, if you want to start working toward a data career by building job-relevant skills, consider enrolling in either Google's Data Analytics Professional Certificate or IBM's Data Science Professional Certificate.

Data analysts and data scientists: What do they do? 

Data analysts typically work with structured data to solve tangible business problems using tools like SQL, R, or Python programming languages, data visualization software, and statistical analysis. Common tasks for a data analyst might include:

• Collaborating with organizational leaders to identify informational needs

• Acquiring data from primary and secondary sources

• Cleaning and reorganizing data for analysis

• Analyzing data sets to spot trends and patterns that can be translated into actionable insights

• Presenting findings in an easy-to-understand way to inform data-driven decisions

Data scientists often deal with the unknown by using more advanced data techniques to make predictions about the future. They might automate their own machine learning algorithms or design predictive models that can handle both structured and unstructured data. This role is generally considered a more advanced version of a data analyst. Some day-to-day tasks for data scientists might include:

• Gathering, cleaning, and processing raw data

• Designing predictive models and machine learning algorithms to mine big data sets

• Developing tools and processes to monitor and analyze data accuracy

• Building data visualization tools, dashboards, and reports

• Writing programs to automate data collection and processing

Data analyst vs data scientist job outlook and salary

Data professionals are in demand and well-paid for their valuable skills. According to the US Bureau of Labor Statistics (BLS), the number of data scientists—including analysts—is projected to grow by 36 percent between 2023 and 2033, indicating much faster-than-average job growth over the next decade [2].

These data professionals also earn strong pay. According to Glassdoor, data analysts in the United States earn an average salary of $86,592, while data scientists earn an average salary of $119,626 [3,4].

Difference between data analyst and data scientist skill sets

Data scientists and data analysts both work with data, but each role uses a slightly different set of skills and tools. Many technical skills involved in data science build off of those data analysts use. Here’s a look at how they compare.

Ways to build your data skill set on Coursera with powerful industry leaders:

• Develop Python fundamentals with the IBM Applied Data Science Professional Certificate

• Learn how to work with Excel and R in the IBM Data Analytics with Excel and R Professional Certificate

• Leverage AI to gain quick insights with the Microsoft Copilot for Data Science Specialization

• Explore the benefits of data analytics in cloud computing with the Google Cloud Data Analytics Professional Certificate

• Use Power BI to connect to data sources and transform them into meaningful insights with the Microsoft Power BI Data Analyst Professional Certificate.

Educational requirements for data analytics vs. data science

Most data analyst roles require at least a bachelor’s degree in a field like mathematics, statistics, computer science, or finance. Data scientists (as well as many advanced data analysts) typically have a master’s or doctoral degree in data science, information technology, mathematics, or statistics.

While a degree has generally been the primary path toward a career in data, some new options are emerging for those without a degree or previous experience. By earning a Professional Certificate in data analytics, you can build the skills necessary for an entry-level role as a data analyst in less than six months of study.

You can practice statistical analysis, data management, and programming using SQL, Tableau, and Python in Meta's beginner-friendly Data Analyst Professional Certificate. Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Build your data skills on Coursera

Data professionals use their knowledge of data to help organizations make better, more informed decisions. Build the analytical skills you need to start your data career with one of these educational programs on Coursera:

For foundational data analytics skills, enroll in the Google Data Analytics Professional Certificate. Learn key analytical skills like data cleaning, analysis, and visualization along with critical data tools, including SQL, R, and Tableau.

To prepare for a career as a data scientist, try the IBM Data Science Professional Certificate. Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL.

To unlock AI efficiency for data, consider the Microsoft Copilot for Data Science Specialization. Gain hands-on experience using Copilot to generate code, analyze data, build simple generative models, and mitigate bias in AI-driven processes.

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