Both data analysts and data scientists work with data, but they do so in different ways.
Data analysts and data scientists represent two of the most in-demand, high-paying jobs, according to the World Economic Forum Future of Jobs Report 2023 [1]. Both roles work with data, but they do so in different ways.
In this article, we'll compare data analysts and data scientists, including their job responsibilities, the skills they use, and what you can do to pursue each career. Afterward, if you want to start working toward a data career by building job-relevant skills, consider enrolling in a Professional Certificate from today's top tech leaders, such as Google's Data Analytics Professional Certificate or IBM's Data Science Professional Certificate.
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
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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, you can build the skills necessary for an entry-level role as a data analyst in less than six months of study. You can practice statistical analysis, data management, and programming using SQL, Tableau, and Python in Meta's beginner-friendly Data Analyst Professional Certificate. Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.
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.
Data analyst | Data scientist | |
---|---|---|
Mathematics | Foundational math, statistics | Advanced statistics, predictive analytics |
Programming | Basic fluency in R, Python, SQL | Advanced object-oriented programming |
Software and tools | SAS, Excel, business intelligence software | Hadoop, MySQL, TensorFlow, Spark |
Other skills | Analytical thinking, data visualization | Machine learning, data modeling |
Start building your programming skills today by enrolling in one of these courses on Coursera today:
In the University of Michigan's Python for Everybody Specialization, you'll learn how to program and analyze data with Python.
In Duke University's Data Analysis with R Specialization, you'll learn how to analyze and visualize data in the R programming language.
The University of Michigan's PostgreSQL for Everybody Specialization teaches SQL skills you can use in an actual, real-world environment.
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.
Yes. Many data analysts go on to become data scientists after gaining experience, advancing their programming and mathematical skills, and earning an advanced degree.
Which you choose is largely a matter of preference. If you’re mathematically minded and enjoy the technical aspects of coding and modeling, a data science degree could be a good fit. On the other hand, if you love working with numbers, communicating your insights, and influencing business decisions, consider a degree in data analytics. Whether you study data science or data analytics, you’ll be building skills for an in-demand, high-paying career.
Working as a data analyst empowers you to apply your analytical thinking skills to help solve business problems. It’s a highly sought-after role that’s typically well compensated. According to the Robert Half Salary Guide 2023, data analysts in the US make, on average, $110,250, depending on skills, location, and experience. Data scientists earn even more — $140,750, on average. Specializing in big data engineering and AI architecture can further increase earning potential. [2]
Coding isn’t typically required for data analysts, though having fluency in Python, R, or SQL can help you to clean, organize, and parse data.
World Economic Forum. "The Future of Jobs Report 2023, https://www3.weforum.org/docs/WEF_Future_of_Jobs_2023.pdf." Accessed March 19, 2024.
Robert Half. "2023 Salary Guide, https://www.roberthalf.com/salary-guide." Accessed March 19, 2024.
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