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 .
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.
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
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 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|
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.
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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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
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 2020, data analysts in the US make between $83,750 and $142,500, depending on skills and experience. Data scientists earn even more — $105,750 to $180,250. Specializing in big data engineering and AI architecture can further increase earning potential. 
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.
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.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.