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 2021. The World Economic Forum Future of Jobs Report 2020 listed these roles at number one for increasing demand across industries, followed immediately by AI and machine learning specialists and big data specialists [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. 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

Read more: What Does a Data Analyst Do? A Career Guide

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

Read more: What is a Data Scientist? Salary, Skills, and How to Become One

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

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.  

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.

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.

professional certificate

Google Data Analytics

This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.

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Average time: 6 month(s)

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Skills you'll build:

Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study

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Related articles

• What Does a Data Analyst Do? A Career Guide

• What Is Data Analysis? (with Examples)

• 5 SQL Certifications for Your Data Career

Article sources

1. World Economic Forum. "The Future of Jobs Report 2020, https://www.weforum.org/reports/the-future-of-jobs-report-2020." Accessed March 26, 2021.

2. Robert Half. "2020 Technology Salary Guide, https://www.roberthalf.com/sites/default/files/documents_not_indexed/2020_Salary_Guide_Technology_NA.pdf." Accessed March 26, 2021.