Data Analyst vs. Data Scientist: Key Differences Explained

The titles data analyst and data scientist are often used interchangeably, but the two roles are far from identical. They serve different purposes, require different skill sets and follow distinct career paths.

If you’re deciding which direction to take your career, or choosing between Southern Illinois University Edwardsville’s online BSBA with a Data Analytics specialization and a data science bachelor’s program, understanding the difference matters. This guide breaks down both roles clearly so you can choose the path that fits your goals.

What Is a Data Analyst?

A data analyst uses leading big data tools to examine existing data to find patterns, trends, and insights that help organizations make decisions. Their work is primarily descriptive and diagnostic; they look at what happened and try to explain why. Typical daily work for a data analyst includes:

• Pulling data from databases using SQL
• Cleaning and organizing datasets for accuracy using big data tools like SAS Viya
• Running statistical summaries and identifying trends
• Building charts, dashboards and reports using tools like Power BI or Tableau
• Presenting findings to business stakeholders in plain terms

Data analysts work with structured data, such as sales records, web traffic, financial reports and customer surveys, as well as unstructured data like images, videos, and social media content. They focus on making that data useful to the people who need to act on it. This focus is what distinguishes their day-to-day work from the more exploratory, model-building work of a data scientist.

Most data analyst roles require a bachelor’s degree in data analytics, business analytics, statistics or a related field. For a broader look at how this kind of training supports business strategy, explore our guide on business data analytics.

What Is a Data Scientist?

A data scientist works with data to build models that can predict future outcomes or automate decisions. Their work is primarily predictive and prescriptive; they create tools that say what is likely to happen next and what to do about it. Typical work for a data scientist includes:

• Designing and training machine learning models
• Developing algorithms that identify patterns in unstructured data (text, images, audio)
• Running experiments to evaluate model accuracy
• Writing complex code in Python or R to build and test statistical models
• Collaborating with engineers to deploy models into production systems

Many individuals might start with a bachelor’s degree in data analytics and then progress to a graduate degree in data science. Data scientists often need a graduate degree, typically a master’s or PhD in data science, computer science or statistics. Entry into data science with a bachelor’s degree alone is possible but typically requires strong self-study in machine learning and statistical modeling.

Data Analyst vs. Data Scientist: Key Differences

The table below summarizes how these two roles differ across focus, tools, education and pay. If you’re weighing the two paths side by side, such as when deciding between SIUE’s BSBA with a Data Analytics specialization and a graduate data science program, these distinctions can help clarify which skill set and career trajectory fits you best.

BLS does not track “data analyst” as its own occupation code, so the analyst-side figures above reflect Operations Research Analysts, the closest related BLS category. Even with that caveat, the pay and growth gap between the two paths is clear: data science roles command a meaningful premium, largely because they require more advanced technical training.

Skills Comparison

Both roles draw on a shared analytical foundation, but the tools and techniques diverge sharply as you move from descriptive analysis into predictive modeling. Here’s how the required skill sets compare.

Data Analyst Skills

Data analysts need a foundation in statistics and data tools, plus strong communication abilities. Because their job is to translate numbers into decisions non-technical stakeholders can act on, data visualization skills matter just as much as the technical ones.

Technical skills:

• SQL: Daily requirement for querying databases
• Excel: Essential for analysis and reporting at most companies
• Python or R: Increasingly required even for entry-level roles
• Tableau / Power BI: Standard visualization platforms
• Statistical analysis: Averages, distributions, correlations, A/B testing

Soft skills:

• Communication: Translating findings for non-technical audiences
• Critical thinking: Questioning data quality and spotting inconsistencies
• Attention to detail: Small errors in data preparation compound downstream

Data Scientist Skills

Data scientists build on an analyst’s foundation but add advanced modeling and engineering capabilities. Where an analyst explains what already happened, a data scientist builds the systems that predict what happens next.

Technical skills:

• Advanced Python / R: Custom model development, not just analysis
• Machine learning: Supervised and unsupervised learning, model evaluation
• Deep learning frameworks: TensorFlow, PyTorch (for AI-heavy roles)
• Cloud platforms: AWS, Google Cloud or Azure for data pipelines and model deployment
• Statistics: Advanced probability, Bayesian methods, experimental design

According to O*NET (a comprehensive online database that provides detailed information about occupation skills, knowledge, abilities, and work activities), data scientists analyze and manipulate large datasets using statistical software, apply modeling techniques to predict outcomes and build the algorithms that support machine learning systems. That mix of programming, analytical reasoning and quantitative modeling is exactly what structured degree programs in data analytics and computer science are designed to build.

Salary Comparison

Both roles offer strong compensation. The U.S. Bureau of Labor Statistics reports that the median annual wage for data scientists was $112,590 in May 2024. Operations research analysts, a closely related role data analysts often occupy, earn a median of $91,290 annually.

Pay for data analysts varies significantly by experience and industry. BLS data on operations research analysts, the closest related occupation, shows the lowest-paid 10% earning less than $53,910 annually, while the highest-paid 10% earn more than $159,280.

Data scientists see an even wider range. The lowest-paid 10% earn less than $63,650 annually, while the highest-paid 10% earn more than $194,410, reflecting how much specialization and seniority affect pay in this field.

The pay gap reflects the advanced education and specialized skill set required for data science roles. Even so, top-earning analysts with strong technical skills can out-earn a typical data scientist, particularly in senior or specialized positions.

Career Paths

Both roles offer clear paths for advancement, but they branch in different directions. Data analysts tend to move laterally across business functions, while data scientists typically specialize more deeply into research or engineering leadership.

Data Analyst Career Path

Most data analysts begin in junior or entry-level analyst roles and advance through the following progression. Each step typically adds more independent analysis and broader stakeholder responsibility:

• Junior Data Analyst: Reporting, data cleaning, dashboard support
• Data Analyst: Independent analysis, stakeholder reporting
• Senior Data Analyst: Project ownership, mentoring junior staff
• Analytics Manager / BI Director: Team leadership, strategy, cross-functional influence

Data analysts can also pivot laterally into product analytics, marketing analytics, financial analysis or supply chain analytics. Because the underlying skills transfer well across business functions, this flexibility is one of the role’s biggest career advantages.

Data Scientist Career Path

Data scientists follow a more vertical, specialization-driven path. Advancement usually comes from deepening technical expertise rather than branching into new business functions:

• Junior Data Scientist: Model building under supervision
• Data Scientist: Independent model development and evaluation
• Senior Data Scientist: Research leadership, complex problem ownership
• Machine Learning Engineer / Principal Scientist: Production model systems, architecture
• Chief Data Officer / VP of Data: Organizational strategy for data and AI

The World Economic Forum’s Future of Jobs Report 2025 names AI and machine learning specialists and big data specialists among the fastest-growing roles globally through 2030. Data science career paths feed directly into these in-demand specializations, which is part of why senior data science talent commands such a premium.

Which Should You Choose?

The right answer depends on your interests, your tolerance for advanced mathematics and the kind of problems you want to solve. Neither path is objectively better; the fit comes down to how you want to support data-driven decision-making day to day.

Data analytics is often the more accessible, faster path into the workforce. It may be the better fit if you:

• Enjoy working with structured data and communicating findings
• Want to enter the workforce with a four-year degree
• Are interested in roles inside business, healthcare, marketing or operations
• Prefer making insights accessible rather than building algorithmic systems

Data science asks for a longer runway and deeper technical investment. It may be the better fit if you:

• Are drawn to machine learning, AI and predictive modeling
• Are willing to pursue a graduate degree
• Have strong programming skills and want to develop them further
• Want to work on prediction, automation and AI-driven products

AACSB-accredited programs in business analytics, including SIUE’s BSBA with a Data Analytics specialization, provide the bachelor’s-level foundation for a data analytics career. These programs develop the SQL, Python, visualization and business skills employers expect from entry-level analyst hires. INFORMS notes that its members, professionals working across analytics, data science and operations research, focus on driving better decision-making by turning data into action across industries.

Choosing Your Data Career Path

Data analysts and data scientists both turn data into value, but they do it in different ways. Analysts explain what already happened and help stakeholders act on it, while scientists build the models that predict what happens next and automate the response.

If you’re drawn to structured analysis, clear communication and a faster path into the workforce, data analytics is likely the better fit. If you want to build predictive systems and are ready to invest in graduate-level training, data science may be the stronger match.

Ready to build the skills employers are looking for? Learn more about SIUE’s online BSBA with a Data Analytics specialization.

Frequently Asked Questions

Here are answers to some of the most common questions about choosing between data analyst and data scientist career paths. These questions cover the skills, education and tools that most often come up when comparing the two roles.

Do data analysts and data scientists need the same math background?

Not exactly. Data analysts need a solid grounding in statistics, but data scientists need considerably more advanced math, including probability theory, linear algebra and the calculus that underlies machine learning models.

Can a data analyst become a data scientist?

Yes, many data scientists start as data analysts and move into data science after building programming and machine learning skills, often through a graduate degree or intensive self-study.

Which role is better for someone without a strong coding background?

Data analyst roles are generally more accessible, since SQL and spreadsheet tools carry more of the day-to-day workload than the heavier programming required in data science.

Do both roles work with the same tools?

They share some tools, such as Python and SQL, but data scientists rely much more heavily on machine learning frameworks and cloud platforms that go beyond what most analyst roles require.

Is a graduate degree required to become a data scientist?

Not always, but most employers strongly prefer a master’s or PhD for data science roles, while a bachelor’s degree remains the standard entry point for data analyst positions.

About Southern Illinois University Edwardsville

Southern Illinois University Edwardsville (SIUE) is a public university located in Edwardsville, Illinois, offering a full range of undergraduate and graduate programs both on campus and online. The SIUE School of Business holds accreditation from AACSB International, placing it among roughly 2% of business schools worldwide that hold dual accreditation for both business and accounting.

For students pursuing a data analytics career, SIUE’s online BSBA with a Data Analytics specialization combines AACSB-accredited business fundamentals with hands-on analytics training. Graduates leave the program with both the credential and the practical skill set employers look for in entry-level analyst roles.