According to its Future of Jobs Report 2025, the World Economic Forum predicts that broadening digital access will be the most transformative trend among workplaces through 2030 — and it ranks AI and big data among the top three skills employers will be looking for. (The other two are networks/cybersecurity and technological literacy.)
That’s why data scientists and data analyst roles continue to top the U.S. Bureau of Labor Statistics (BLS) Fastest Growing Occupations list. These career paths have a lot in common and definitely some overlap; however, when you look closer, you can see the distinctions.
This blog post explores the difference between a data analyst and a data scientist — and explains how to gain the skills and knowledge to pursue either of these exciting roles.
Beyond the Title: Comparing Data Analyst and Data Scientist Roles
Data analysts and data scientists share many foundational skills, but they differ in their primary responsibilities, technical depth, and the types of problems they are typically hired to solve.
Data Analyst
- Analyze data to uncover insights and trends
- Visualize findings through reports, dashboards, and presentations
- Translate complex business questions into data-driven answers
- Requires strong analytical thinking, data storytelling, SQL, and statistics
Data Scientist
- Utilize data to train predictive models and forecast trends
- Design and execute data-driven experiments to evaluate model performance
- Apply machine learning techniques to automate and optimize decision-making
- Requires advanced programming, statistical modeling, machine learning, and algorithm design
Imagine an online retailer wants to improve customer retention. A data analyst examines purchasing behavior to understand why customers stop returning and communicates those findings through a dashboard.
A data scientist takes that analysis a step further and builds a predictive model that identifies customers who are likely to stop returning so the retailer can proactively send personalized offers encouraging them to make another purchase.
It’s worth noting that while the U.S. Department of Labor’s Occupational Outlook Handbook uses the broad terms “data scientist” and “analyst,” actual job titles will vary by company and industry. Among the roles you might see advertised:
- Product Analyst
- Business Intelligence (BI) Analyst
- Research Scientist
- Machine Learning Engineer
- AI Engineer
- Analytics Consultant
AI and Data Science: Adapting to Emerging Technology
As we look at the differences between data analyst and data scientist, it’s also important to consider that both are impacted by the surge in artificial intelligence. The World Economic Forum reports that 86 percent of workplaces said AI and information processing technologies are likely to drive business transformation through 2030. This means that, as GenAI continues to advance and adoption continues to explode, many data-related roles will require proficiency with AI tools.
To help prepare students for a data science career, Harvard Extension School developed the HES-Defined AI Stack, an educational framework from Harvard Extension School that reflects how modern AI systems are designed, built, deployed, governed, and applied in practice. The HES-Defined AI Stack is a core principle of the master’s degree in data science and artificial intelligence.
In addition to a robust curriculum, the data science and artificial intelligence graduate degree program includes a capstone experience in which students work on real-world projects with industry partners like NVIDIA and NASA.
What Does a Data Analyst Do?
Data analysts bridge the gap between data and business decision-makers by transforming complex data into measurable opportunities. Their primary goal is to understand what happened, why it happened, and how those insights can inform strategic decisions.
On a day-to-day basis, data analysts may query structured databases using SQL, clean and organize raw datasets, and analyze operational metrics to uncover trends, revenue opportunities, or process bottlenecks. They then translate their key findings into clear narratives through interactive visual dashboards using tools like Tableau, Power BI, or Excel.
A successful data analyst must blend technical proficiency with exceptional business acumen, domain knowledge, and data storytelling.
Education, outlook and salary for data analysts
Data analyst roles typically require a bachelor’s degree. Traditionally, many analysts came from a business or computer science background. However, as higher education adapted to the changing career landscape, many colleges and universities now offer more specific graduate degrees and graduate certificates in areas like data analytics and business intelligence.
If you’re interested in a career in data analytics, explore options like Harvard Extension School’s Data Analytics graduate certificate.
The BLS doesn’t list “data analyst” as a specific occupation, but this field encompasses roles it does track, such as operations research analyst, information security analyst, and market research analysts — all of which are expected to grow much faster than the average occupation through 2034. It’s easy to understand why there’s a clear demand: Given how vital information has become to nearly everything we do, analysts can find work in every industry, from healthcare and education to entertainment and technology.
The average annual salary varies by type of analyst; for one example, the BLS lists the median pay for operations research analyst at $91,290 in 2024.
What Does a Data Scientist Do?
Data science sits at the intersection of computer science, statistics, mathematics, and software engineering. Depending on the industry and role, data scientists may develop predictive models, optimize business processes, design recommendation systems, detect anomalies, or build AI-powered applications. Their goal is to analyze data, but also to create scalable analytical solutions that can automate tasks, generate predictions, and drive data-informed decisions.
On a day-to-day basis, data scientists may work with both structured and unstructured data to engineer features, run statistical experiments, and train machine learning models. This requires fluent programming in Python or R, experience with machine learning and deep learning frameworks like PyTorch or TensorFlow, and familiarity with cloud architecture like AWS, GCP, or Azure.
A successful data scientist must blend strong programming skills, algorithmic thinking and machine learning expertise with a curiosity to ask the right questions.
Education, outlook and salary for data scientists
When comparing data scientists and analysts, differences can also be found in education level and salary. Becoming a data scientist typically includes study at the graduate level, which likely builds off an existing educational and professional background in computer science, data science, or a related field.
There are a range of options for continuing education, including master’s degree programs and graduate certificates focused on technology.
If you’re interested in a data science master’s, explore options like Harvard Extension School’s Data Science and AI master’s degree program. HES is designed for working professionals, whether you’re continuing your education or looking to pivot to a new career.
According to the BLS, demand for data scientists is anticipated to grow 34 percent by 2034, which is much faster than the average for all occupations. In 2024, the median annual salary for data scientists was $112,590 per year, but that number can vary broadly based on education, experience, location, and level of responsibility within an organization.
The Takeaways: Reviewing the Key Differences Between Data Analytics and Data Science
Both roles dig into data. Both harness the power of information to advance their organization. Both use numbers to tell a story. Both roles are in high demand across industries.
Where they differ comes down to specific skills, duties, and training required.
- Data analysts focus on discovering trends and communicating insights; data scientists focus on building models and analytical solutions.
- Data scientists have more technical depth and need more advanced programming and statistical expertise.
- Data analytics is often a strong starting point for a career in data science, which typically requires an advanced degree and/or experience.
And if you’re wondering which makes more: data scientist or data analyst? According to the U.S. Bureau of Labor Statistics, data scientists earn a higher median salary on average than many data analyst roles. However, compensation varies widely depending on factors such as industry, geographic location, years of experience, level of education, and job responsibilities. As professionals gain experience, both career paths offer opportunities for advancement into specialized or leadership roles.
Digging deeper: how to determine which career path is right for you
You can use the above takeaways to determine which career path is the best fit for you. For example:
- Consider data analytics if you enjoy business problem-solving, visualization, reporting, working with stakeholders, and translating data into decisions
- Consider data science if you enjoy programming, statistics, modeling, experimentation, and building predictive systems
- Consider analytics as a starting point if you’re entering the field and may eventually want to move into data science
Harvard Extension School Data Science Student Stories
When AI went from interesting to critical, HES didn’t scramble. The faculty were already there. They updated courses in real time. They were genuinely engaged with where the field was going.
Research and take advantage of all that Harvard has to offer: the amount and range of opportunities is mind-blowing.
Harvard Extension School stood out for its quality, real-world relevance, and flexibility, making it the perfect choice to advance my skills and stay at the forefront of the industry.
How a Graduate Certificate Prepares You for In-Demand Careers
The expertise, structure, and resources of a formal academic program can help you develop the technical skills and fundamental knowledge you need to enter or advance in a data-driven career. Harvard Extension School (HES) offers several related online graduate certificates designed for working professionals. As you explore what’s entailed in each program, you’ll continue to notice the differences between a data scientist and data analyst.
HES’ graduate certificates prepare students for the next stage of their careers; 86 percent of data science certificate earners and 92 percent of data analytics certificate earners reported confidence in their next steps.
Data Analytics Certificate
The online Data Analytics Graduate certificate from HES focuses on building practical skills in statistics, data management, and data visualization. You’ll also get an introduction to using programming languages such as Python and R for analyzing and reporting.
Data Science Certificate
The online Data Science Graduate certificate from HES provides a foundation in statistical analysis, data structures, machine learning, and predictive modeling. You’ll work with Python and other languages and platforms to analyze and visualize real-world data.
Artificial Intelligence Certificate
The online Artificial Intelligence Graduate Certificate from HES explores natural language processing and deep learning techniques. You’ll learn to apply foundational data science principles and data modeling techniques to AI applications.
If you are interested in gaining deep expertise in data science and artificial intelligence, you may also consider Harvard Extension School’s data science and artificial intelligence graduate degree program.
Next Steps: Discovering Data Science at Harvard
If you’re considering a career move — whether transitioning to an entirely new field or advancing in your existing industry or organization — we invite you to explore the wide range of online graduate certificates at and graduate degrees at Harvard Extension School.
