Data Analyst vs. Data Scientist: What’s the Difference?

Data analysts and data scientists represent two of the most in-demand, high-paying jobs, alongside AI and machine learning specialists and digital transformation specialists, according to the World Economic Forum Future of Jobs Report 2023 [1].

While there’s undeniably plenty of interest in data professionals, it may not always be clear what the difference is between a data analyst and a data scientist. Both roles work with data, but they do so in different ways.

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. Typical 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 translate into actionable insights

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

Data scientists often deal with the unknown using more advanced data techniques to make predictions. They might automate ML algorithms or design predictive modelling processes to handle 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

Data science vs. analytics: Educational requirements

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 from Google or IBM—available on Coursera—you can build the skills necessary for an entry-level role as a data analyst in less than six months. Upon completing the Google Certificate, you’ll have access to a hiring consortium of more than 130 companies.

If you’re just starting, working as a data analyst first can be a good way to launch a career as a data scientist.  

Data skills for scientists and analysts

Data scientists and analysts both work with data, but each role requires a slightly different set of skills and tools. Many skills involved in data science build on those used by data analysts.

Here’s a look at how they compare:

Get started with Coursera.

Take the first step on your career path in data science by earning a Data Analyst Professional Certificate from IBM or Google. To learn more about the path from data analyst to data scientist, including recommendations for skills, courses, and guided projects, check out our Data Science Career Learning Path.

Frequently asked questions (FAQ)