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

Data analysts and data scientists represent two of the most in-demand, high-paying jobs in 2022. The World Economic Forum Future of Jobs Report 2020 listed these roles as number one for increasing demand across industries, followed immediately by AI, machine learning, and big data specialists [1].

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

Read more: What Is Data Analysis? (With Examples)

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 visualisation software, and statistical analysis. Common tasks for a data analyst might include:

• Collaborating with organisational leaders to identify informational needs

• Acquiring data from primary and secondary sources

• Cleaning and reorganising data for analysis

• Analysing 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. They might automate their own machine learning algorithms or design predictive modelling 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 analyse data accuracy

• Building data visualisation tools, dashboards, and reports

• Writing programs to automate data collection and processing

Read more: How to Become a Data Scientist

Data science vs. analytics: Qualifications

Most data analyst roles require at least a bachelor’s degree in computer science, data analysis, or statistics. Data scientists typically require a bachelor’s degree in data science and earn a master’s degree in one of the specialised areas. Some qualifying specialisms include:

• Cloud computing

• Cybersecurity

• Networking

• Steganography

If you’re just starting, working as a data analyst first can be an excellent way to launch a career as a data scientist. Gaining Professional Certificates or taking courses in relevant subject matter can also be a great way to build your CV and demonstrate the skills needed to stand out to employers when seeking a position in this field.

Data skills for scientists and analysts

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

Get started with Coursera

Both data analysts and data scientists rely on strong foundational skills in data analytics. To take your first step towards a career in one of these areas, consider signing up to complete Google Data Analytics Professional Certificate to learn in-demand skills in under six months. From here, you can choose to begin your career as an analytics professional or go on to complete more advanced coursework to move towards a career in data science.