Key Differences Between a Data Analyst and a Data Scientist.

In 2025, data analysts and data scientists rank among the most sought-after and high-paying roles. According to the World Economic Forum’s Future of Jobs report, demand for data professionals is soaring—yet the distinction between a data analyst and a data scientist often remains unclear to many..

Data analysts and data scientists: What do they do?

One of the biggest differences between data analysts and scientists is what they do with data.

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 modeling processes 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 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