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

Written by Coursera Staff • Updated on

Both data analysts and data scientists work with data, but they do so in different ways.

[Featured image] A data scientist in a coral sweater and glasses sits in front of a computer with programming language on the screen.

There’s no question that data skills are in high demand in 2023. In fact, roles such as data analyst and data scientist are expected to increase 30 to 35 percent by 2027, according to the 2023 World Economic Forum Future of Jobs Report [1]. Whether you have a background in data or you are looking to switch careers, both data analytics and data science represent two popular career choices that are set to increase in demand across industries in the upcoming years. 

While the two roles may sound similar, there are distinct differences between the two. Knowing how to distinguish between the roles can help you make informed choices when deciding how to build your knowledge base, what skills to highlight on your CV, and how to land your first position.

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. As a data analyst, you will likely perform job responsibilities such as:

  • Collaborating with organisational leaders to identify informational needs

  • Acquiring data from primary and secondary sources

  • Using business analytics tools to make informed company recommendations

  • Migrating data between different systems

  • 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, on the other hand, often deal with the unknown by using more advanced data techniques to make predictions about the future. Data science typically uses concepts from maths, statistics, and engineering. For example, a data scientist might use statistical methods to create predictive algorithms based on large sets of 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 programmes to automate data collection and processing

  • Recommend solutions to complex systems problems

  • Abide by ethical data guidelines 

Data science vs. analytics: Educational requirements

Both data analyst and data scientist roles typically require at least a bachelor’s degree in a field like mathematics, statistics, computer science, or finance. However, data scientists typically require more advanced education to land positions. 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.

If you’re just starting out, working as a data analyst first can be a good way to launch a career as a data scientist. Gaining professional certifications 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 trying to land a position in this field.  

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Data skills for scientists and analysts

Data scientists and data analysts both 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.

Top skills to cultivate as a data analyst

  • Basic data analytic techniques to synthesise findings and make recommendations

  • Basic maths and statistics skills

  • Basic programming skills in languages such as R, SQL, and Python

  • Communicating effectively across multi-disciplinary teams

  • Managing data within organisational structures

  • Modelling, cleaning, visualising, and enriching data

  • Creative problem-solving skills

Top skills to cultivate as a data scientist

  • Advanced skills in calculus, statistics, probability, and linear algebra

  • Proficiency in programming languages such as Python, R, SAS, MATLAB, and SQL

  • Familiarity with software frameworks like Hadoop, TensorFlow, and Apache Spark

  • Strong data modelling and visualisation skills

  • Machine learning and artificial intelligence

  • Ethical data management

  • Product management and delivery

  • Clear communication skills

  • Innovating thinking and creative problem-solving skills

Get started with Coursera

Both data analysts and data scientists rely on strong foundational skills in data analytics. Take your first step towards a career in one of these areas with the 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.

Article sources

  1. World Economic Forum. "The Future of Jobs Report 2023," Accessed August 14, 2023.

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