Data science vs. data analytics: what’s the difference?
By: Amanda Phagan, Edited by: Joey Morris
Published: January 22, 2025
If you are interested in building in-demand skills like problem-solving, statistical analysis, and storytelling, you might consider becoming a data scientist or data analyst. But what's the difference between these two roles? While similar at a glance, they are distinct career paths with different focus areas.
Read on to learn more about data science vs. data analytics.
Data science vs. data analytics
Both data scientists and data analysts handle complex data sets and identify key patterns that matter to organizations across various industries. However, they approach and use data differently.
Let's start by covering the definitions of each role:
Data scientist
A data scientist uses advanced techniques like machine learning algorithms and predictive modeling to make projections. This person also spends time gathering and organizing raw data, developing tools to ensure data accuracy, building data visualization models, and using programming languages to automate data processing.
Data analyst
A data analyst works with structured data to solve problems using data visualization models and statistical analysis. They also collaborate with stakeholders to inform data-driven decisions, organize existing data, and present findings in an easy-to-understand way.
The key differences between data scientists and data analysts
Many employers classify data scientist roles as "more advanced" than data analyst positions. Data scientists must have a deeper understanding of probability and statistics to accurately predict and forecast. Data scientists also use machine learning and artificial intelligence to create their own predictive models.
Data analysts spend more time making sense of existing data and presenting their findings using data visualization. For this reason, they typically have more interest in communication and consulting than data scientists.
Required skills
Data scientist
- Advanced probability and statistics: Data scientists must have a deep understanding of the mathematical aspects of classifying data and making predictions.
- Programming languages: Handling complex data sets typically requires proficiency in programming languages like Python, R, and Java.
- Big-data tools: Data scientists can benefit from a familiarity with platforms such as Hadoop and Spark.
- Machine learning and artificial intelligence: Unlike data analysts, data scientists need to be familiar with machine learning and AI techniques to create their own predictive models.
- Innovative problem-solving: Data scientists should be able to think outside the box to develop new problem-solving techniques.
- Storytelling: Since data scientists make predictions and forecasts, they must be able to use their models to tell a story related to the broader organization or industry.
Data analyst
- Probability and statistics: Data analysts need strong math skills to interpret data.
- Data analysis software: Data analysts can benefit from learning programming languages like Python, R, and SQL, but proficiency in Excel is foundational for basic data analysis.
- Data visualization: Data analysts should be comfortable using data visualization tools such as Tableau and PowerBI, which can be helpful for creating visual elements for presentations.
- Report-building: Data analysts spend a lot of time communicating their findings with a broader team, so they must know how to build detailed, clear, and error-free reports.
- Problem-solving: A data analyst's main goal is to solve problems in a business, industry, or market context, so problem-solving skills are crucial.
- Communication: Data analysts must have good written and verbal communication skills so they can effectively collaborate with various stakeholders.
Education requirements
Employers typically require data analysts to have at least a bachelor's degree in statistics, computer science, or a related field. They may prefer applicants with a master's degree in data analytics, as the degree paves the way for advanced statistical analysis, data management, and data visualization. Data analysts stand out from the crowd by taking courses and completing certificates in Excel, SQL, Python, and R.
Data scientists should have an advanced degree, such as a master's in data science, in addition to a bachelor's degree. Some aspiring data scientists use their master's degree program as a chance to specialize in areas of interest like business analytics, machine learning, artificial intelligence, or data engineering. Data scientists who want to grow their professional skillset may consider courses in advanced statistics, programming languages, data management, machine learning, artificial intelligence, and big-data technologies.
Salary and career outlook
Both data scientists and data analysts earn high salaries in the U.S. According to the Bureau of Labor Statistics (BLS), in 2023, data scientists earned a median annual salary of $108,020. The BLS expects data science jobs to increase by 36% from 2023 to 2033, which is much faster than the average for all occupations.
Though data analysts typically make less than data scientists, that isn't always the case. In this field, your salary depends on experience, education, specialization, and employer. For example, per the BLS, market research analysts made a median of $74,680 per year as of May 2023. The BLS projects jobs for these professionals to grow by 8% from 2023 to 2033. Management analysts, on the other hand, made a median salary of $99,410 in 2023. The Bureau projects these jobs will grow by 11% during the same period.
Data science vs. data analytics: Which degree is right for me?
If you're trying to decide between a data science and data analytics degree, rest assured that both are rewarding choices for people who love organizing numbers, tracking trends, and solving problems.
Ultimately, you should ask yourself if you prefer using existing data to communicate big-picture takeaways or delving deep into raw data to make predictions about the future. You should also consider factors such as school options, financial assistance, and program duration.
Not sure which path is right for you? Consider completing a quick online course on a topic such as statistics or machine learning to discover what you enjoy most.