Data Analytics vs Data Science: What's the Difference and Which Role Do You Need?

You’ve decided your business needs help with data. Then you look at job boards and see 2 very different titles: data analyst and data scientist. The salaries differ, the skill lists differ, and the job descriptions often overlap in confusing ways.

Hiring the wrong one is an expensive mistake. A data scientist hired to build weekly sales reports will get bored and leave. An analyst asked to build a machine learning model from scratch may struggle. Getting it right starts with understanding what each role is actually for.

This guide compares data analytics and data science by goals, skills, tools and pay, then helps you decide which role your business should hire first.

The short answer

Data analytics answers questions about the business using data you already have. What happened? Why? Which option is better? The output is usually a report, a dashboard or a recommendation.

Data science builds new ways to use data, usually through statistical models and machine learning. What will happen next? Can we predict which customers will cancel? Can we automate this decision? The output is often a model or a data product.

AWS frames analytics as a subset of data science: analysts interpret existing data, while data scientists build new methods and tools that analysts and systems can use. In practice the line is blurry, and many people do some of both.

Key takeaway: Analytics explains and guides decisions with existing data. Data science predicts and automates using models. Most small and midsize businesses need analytics first.

Side-by-side comparison

What a data analyst does

A data analyst is the person who turns your data into answers people can use this week. A typical week might include:

• Pulling data from the POS, CRM, accounting or marketing tools with SQL or exports
• Cleaning and combining data sets so the numbers are reliable
• Building and maintaining dashboards for managers
• Investigating questions such as “why did margin drop in March?”
• Presenting findings in plain language with a clear recommendation

Analysts lean heavily on business context. Knowing that a sales dip lines up with a supplier problem or a holiday matters as much as the technical work.

Common tools include Excel and Google Sheets, SQL, and BI platforms such as Power BI or Tableau. For a beginner’s view of the work itself, see what is data analytics. If you’re weighing BI tools, our Power BI vs Tableau guide compares the 2 most common choices.

What a data scientist does

A data scientist uses statistics and programming to build models that predict or automate. The U.S. Bureau of Labor Statistics describes data scientists as using analytical tools and techniques to extract meaningful insights from data, including cleaning data, building and testing machine learning models, and explaining findings to technical and nontechnical audiences.

Typical projects include:

• Churn prediction: flagging customers likely to cancel so the team can intervene
• Demand forecasting: predicting sales by product and location to plan inventory
• Recommendation systems: suggesting products based on purchase history
• Pricing models: testing how price changes affect demand
• Experiments: designing and analyzing A/B tests rigorously

Data scientists usually work in Python or R, use notebooks and machine learning libraries, and often rely on cloud data platforms. Their work increasingly overlaps with AI engineering. For background, see machine learning vs deep learning.

Skills and education compared

BLS lists a bachelor’s degree in mathematics, statistics, computer science or a related field as the typical entry-level education for data scientists and notes that some employers prefer a master’s or doctoral degree. Analyst roles are often more open to people from business, economics or other backgrounds who can show strong SQL, spreadsheet and BI skills. Our guide on how to become a data analyst covers that route.

Pay and job outlook

BLS doesn’t publish a separate “data analyst” category, but it does track data scientists and closely related analyst occupations:

BLS describes both growth rates as much faster than average. Pay varies widely by location, industry and experience, and entry-level analyst salaries usually sit below these medians. For a broader look at in-demand roles, see tech careers in 2026.

Which role should your business hire first?

Use these questions to decide.

Hire a data analyst first if:

• Your reporting is manual, slow or inconsistent
• Leaders disagree about basic numbers like revenue or margin
• Data is spread across several systems with no shared dashboards
• Your main questions are “what happened?” and “why?”
• You don’t yet have years of clean historical data

Consider a data scientist if:

• You already have reliable reporting and clean, well-organized data
• You have a specific, high-value prediction problem, such as churn, fraud or demand
• The prediction would drive a repeated decision at scale, such as daily pricing or inventory
• You have engineering support to put a model into production
• You can afford a longer timeline before seeing results

For many small businesses, the honest answer is: hire an analyst, or train someone internally, and use off-the-shelf tools for prediction. Many BI, CRM and ecommerce platforms now include built-in forecasting and AI features that cover common needs without a custom model. Our guide to generative AI for small business covers some of those options.

Other roles you might hear about

• Business analyst: focuses on processes and requirements, often bridging business and IT teams.
• Data engineer: builds and maintains the pipelines and warehouses that analysts and scientists rely on.
• Analytics engineer: models and transforms data in the warehouse so it’s ready for reporting.
• Machine learning engineer: puts models into production and keeps them running reliably.

Where to start

1. List your top 5 data questions for the next year. Mark each as “explain” (analytics) or “predict or automate” (data science).
2. Check your data foundation. Do you have clean, consistent data in one place? If not, analytics and data engineering come first.
3. Write the job description around outcomes, such as “build a weekly sales dashboard leadership trusts,” not a long list of tools.
4. Test with a project. Before hiring full time, consider a short contract project to prove value and clarify what you need.
5. Plan the path. Many companies start with an analyst, add data engineering as data grows, and bring in data science once a clear prediction use case appears.

Conclusion

Data analytics and data science are close cousins with different jobs. Analytics turns existing data into reliable answers and better day-to-day decisions. Data science builds models that predict and automate, and it depends on the clean data foundation analytics creates. For most businesses, an analyst delivers value sooner and at lower cost. Bring in data science when you have the data, the infrastructure and a specific prediction problem worth solving.

Sources

• AWS: What is data science? — definition of data science and how analytics relates to it
• AWS: What is data analytics? — definition and types of data analytics
• U.S. Bureau of Labor Statistics: Data scientists — duties, education, median pay and job outlook
• U.S. Bureau of Labor Statistics: Operations research analysts — median pay and job outlook