In 2025, organizations across industries—from healthcare and finance to technology—are harnessing data more aggressively than ever to inform decisions, optimize workflow and forecast emerging trends. This growing reliance on analytics is fueling a sharp rise in demand for data-centric roles across the professional landscape.
Two of the most prominent career paths in this space are business analytics and data science. While both fields revolve around data, they differ significantly in terms of skills, responsibilities, salaries and career trajectories.
For those considering a graduate degree or a career change, understanding the distinctions between business analytics and data science is crucial. Choosing the path that aligns with your strengths and goals can open doors to growing job markets and greater career satisfaction. According to projections, management analyst roles—including business analysts—will see steady growth through 2034, while data scientist positions remain among the fastest-growing occupations in the United States.
This article offers a clear comparison of business analytics vs. data science to help you make informed decisions about your education and career path.
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What Is Business Analytics?
Business analytics involves using data, statistics and modeling to solve business problems and support decision-making. It focuses on understanding what happened (descriptive analytics), why it happened (diagnostic analytics), and increasingly, what is likely to happen next (predictive analytics).
Business analysts operate at the intersection of data, operations and strategy. Key responsibilities include:
- Data analysis: Identifying trends and patterns to inform business decisions
- Technology evaluation: Assessing tools and systems that align with team goals
- Communication: Presenting data insights clearly through reports, charts or presentations
- Project development: Assisting in planning and executing initiatives to meet objectives
- Change management: Helping teams adopt new systems or processes smoothly
Common tools in business analytics include:
- Excel for calculations and data organization
- SQL for querying databases
- Tableau and Power BI for interactive data visualization
- Python and AI tools for predictive modeling and advanced analytics
Job titles in this field include business analyst, business intelligence (BI) analyst, data analyst and operations analyst. Business analytics is ideal for individuals who enjoy problem-solving, teamwork and making evidence-based decisions.
What Is Data Science?
Data science is the practice of extracting insights from large, often complex datasets using techniques such as machine learning, statistical modeling and data mining. Unlike business analytics, which typically focuses on structured business data, data science often works with unstructured data and emphasizes predictive modeling, advanced algorithms and automation. It combines mathematics, statistics, programming and domain expertise to solve both technical and strategic problems.
Data scientists typically work independently or in specialized teams to tackle complex challenges. Core responsibilities include:
- Data collection: Gathering information from existing sources or designing new experiments
- Data preparation: Cleaning and organizing raw data for analysis
- Experimentation: Testing new methods for data collection or analysis
- Automation: Developing scripts and tools to streamline data workflows
- Exploratory analysis: Identifying patterns, trends, and key insights in large datasets
- Communication: Presenting findings through clear visualizations and reports
- Strategic insight: Connecting data to business goals and decision-making
Common tools used by data scientists include:
- Python for analysis, modeling and automation
- R for statistical modeling and visualization
- SQL for efficient database querying
- TensorFlow and PyTorch for machine learning and deep learning projects
- Hadoop and Spark for processing large-scale datasets
Typical job titles include data scientist, machine learning engineer, data engineer and data architect. As AI becomes more integrated into business operations, roles like applied AI specialist and AI engineer are also emerging, blending traditional data science with advanced AI for predictive and generative modeling.
Data science is well-suited for individuals who enjoy coding, statistical analysis and building intelligent systems that drive innovation.
What Are The Differences Between Data Science and Analytics?
While both data science and business analytics revolve around data, they differ significantly in focus, required skills, tools and day-to-day responsibilities. Understanding these distinctions can help you choose the path that best aligns with your interests and career goals.
Focus of Work
Business analytics uses historical and operational data to answer business questions, identify trends and support decision-making. In contrast, data science focuses on predicting future outcomes, building automated systems and solving complex technical problems through modeling and machine learning.
Skills
Business analysts rely on strong communication, business acumen and analytical thinking to translate data into actionable insights for non-technical audiences. Data scientists require programming expertise, a solid foundation in mathematics and statistics, and experience with machine learning and AI to build and deploy predictive models.
Tools
Business analysts typically use tools like Excel, SQL, Tableau and Power BI, platforms designed for data organization, dashboard creation and clear visual storytelling. These tools support quick, accessible decision-making across teams.
Data scientists work with more technical tools such as Python, R, TensorFlow and big data platforms like Hadoop and Spark. These enable them to process large datasets, develop sophisticated algorithms and apply advanced analytical techniques.
Approach to Problem-Solving
Business analysts focus on interpreting existing data to recommend practical solutions. Data scientists take a more experimental approach, testing hypotheses, building models and uncovering hidden patterns to inform future strategies. With their complementary skill sets, business analysts and data scientists often work together in teams to address the most demanding business-critical challenges.
Training and Daily Responsibilities
Business analysts often come from backgrounds in business, management or information systems, with an emphasis on aligning data with strategy. On the job, they review dashboards, meet with stakeholders, analyze trends and deliver actionable insights.
Data scientists come from a diverse range of undergraduate programs, including all STEM fields, as well as the social sciences, English, and other areas. Successfully integrating other disciplines with data science often leads to new insights and opportunities.
Shared Ground
Despite their differences, both roles share key elements:
- A commitment to data-driven decision-making
- Strong analytical and problem-solving skills
- Collaboration with teams and stakeholders
- Use of data visualization to communicate findings
- Increasing adoption of AI and advanced analytics tools
Ultimately, both fields play vital roles in modern organizations: one focused on strategic guidance through analysis, the other on innovation through technical expertise.
Career Paths and Job Outlook
Business analytics and data science offer distinct but rapidly growing career opportunities across industries.
Business Analytics
Careers in business analytics often lead to roles in management, consulting or product strategy. Analysts are in demand across sectors such as technology, finance, healthcare, retail, manufacturing and consulting. They help organizations improve operations, interpret data for strategic decisions and enhance efficiency.
In 2025, demand is especially strong in finance and healthcare, where organizations manage vast amounts of operational and customer data. Employment in business analytics is projected to grow 9 percent between 2024 and 2034, with strong opportunities at smaller consulting firms that specialize in niche industries or targeted business functions.
Data Science
Data science offers diverse career paths in artificial intelligence, machine learning and data engineering. With experience, professionals can advance to senior roles such as lead data scientist or chief data officer. The rise of AI has also created demand for specialized roles like applied AI specialist and AI engineer, positions that blend traditional data science with cutting-edge AI applications.
Employment for data scientists is projected to grow 34 percent from 2024 to 2034. In 2025, demand is particularly high in tech companies, research institutions and AI-driven startups.
As organizations generate and collect more data than ever before, they increasingly seek professionals who can analyze that information to drive innovation, streamline operations, design new products and improve marketing strategies. The expanding role of AI further boosts opportunities for professionals who can combine data science and business analytics expertise with AI fluency.
Salary Comparisons
As of 2025, business analysts earn an average annual income of $101,000, with the top 10 percent of earners making more than $170,000. Compensation varies by industry, location, education level and job scope, particularly for roles involving management or strategic decision-making.
Data scientists command higher average incomes, averaging $112,500 per year. Earners in the top ten percent earn more than $194,000 annually. Salaries are highest in AI-focused roles and positions within tech companies, research institutions and startups.
While data scientists generally earn more, experienced business analytics professionals—especially those in leadership—can also achieve competitive, high-level compensation.
Choosing the Right Path
Deciding between business analytics and data science begins with understanding your interests and strengths. Ask yourself:
- Do you prefer communicating insights to support business decisions, or coding and developing predictive models?
- Are you more drawn to management, strategy and operations, or to technical specialization in AI and machine learning?
- Do you enjoy analyzing past trends or using advanced statistics to predict future outcomes?
Both fields require curiosity, problem-solving skills and the ability to turn data into actionable insights. Reflecting on your goals and preferred work style can help you determine which path is the better fit.
For those ready to take the next step, Merrimack College offers online master’s programs in both business analytics and data science, flexible, career-focused options designed to prepare you for success in either field.
How a Master’s Degree Can Boost Your Career
A master’s degree can significantly enhance your career prospects, earning potential, and competitiveness in the job market. Key benefits include:
- Developing advanced analytical and technical skills that are highly valued in business analytics and data science
- Expanding your professional network through connections with peers, faculty and industry leaders
- Increasing your earning potential and qualifying for high-demand, competitive roles
- Gaining specialized expertise in areas like AI, analytics or management to open doors to leadership or technical positions
Merrimack College offers flexible online master’s programs in business analytics and data science designed to meet industry demands. These programs bridge theory and real-world application through hands-on learning, taught by expert faculty in a collaborative online environment that fosters teamwork and professional growth.
Students can further enhance their credentials by pursuing an Applied AI Certificate or the new AI Engineering Certificate through Merrimack’s Master of Science in Computer Science (MSCS) program, integrating AI skills with analytics and data science training.
With practical experience and cutting-edge skills, Merrimack graduates are well-positioned for career advancement in today’s rapidly evolving data-driven economy.
Data-Driven Futures
Business analytics and data science are complementary but distinct paths. Your choice should match your skills, interests and career goals. Both fields offer strong demand, competitive salaries and opportunities for growth.
Merrimack’s online master’s programs can prepare you for either career, providing the skills and flexibility needed to succeed. Connect with an advisor or begin your application to start your journey.