Data science and data analytics are two of the fastest-growing career paths in the country, but they are not the same thing. If you are weighing a Bachelor of Arts in Business Analytics at Keiser University against a more technical data science route, understanding where each field starts and stops will help you pick the right one.
Both fields turn raw data into something useful. The difference is what each one does with it and how far down the pipeline it goes. This guide breaks down the core distinctions, compares the day-to-day responsibilities of data analysts and data scientists, and shows you where a business analytics degree fits into the picture.
What Separates Data Science From Data Analytics?
Data analytics focuses on analyzing past and present data to answer specific business questions. Data analysts clean, interpret, and visualize structured data so stakeholders can make informed business decisions. They are the historians of the data world, explaining what happened and why.
Data science operates further upstream. Data scientists build algorithms, design predictive models, and work with both structured and unstructured data to forecast what will happen next. Where a data analyst focuses on reporting and diagnostic analytics, a data scientist focuses on predictive analytics and prescriptive analytics.
Gabriel Isaacs, Chair of the College of Business at Keiser University and a working COO with Lean Six Sigma, PMP, and ISO 9000 certifications, puts it this way:
“Business analytics is focused on converting raw data into actionable information for decision-makers. Data science is more heavily oriented toward IT. Data science is upstream, and analytics is what you do with what data science produces.”
— Gabriel Isaacs, Chair of the College of Business, Keiser University
That upstream-downstream framing is the simplest way to think about data analyst vs data scientist. One role feeds the other.
Data Analyst vs Data Scientist: Roles, Skills, and Types of Data
What Data Analysts Do
Data analysts gather, clean, and interpret data sets to answer defined business problems. Their responsibilities usually include data collection, data cleansing, trend identification, data visualization, and reporting.
They work primarily with structured data inside relational databases, using tools like SQL (Structured Query Language), Microsoft Excel, Tableau, and Python. A data analyst focuses on translating technical findings into clear, qualitative action items for non-technical leaders.
Most data analysts enter the field with a bachelor’s degree in a quantitative discipline, computer science, or business. An Associate of Arts in Business Analytics can serve as a stepping stone for students building toward a four-year degree.
What Data Scientists Do
Data scientists design new algorithms and models to make predictions about the future. They handle data processing at a much larger scale, working with big data platforms like Apache Spark and Apache Hadoop, and building machine learning models with frameworks like TensorFlow and PyTorch.
The role demands strong skills in statistical analysis, regression analysis, data modeling, machine learning algorithms, and natural language processing. Data scientists often need an advanced degree in computer science, statistics, or mathematics, though career changers with strong portfolios are increasingly competitive.
Data scientists are also responsible for exploratory data analysis, identifying hidden patterns and correlations across massive data sets that include unstructured data such as text, images, audio, and video.
Side-by-Side Comparison
Data analysts produce dashboards, reports, and actionable insights to support day-to-day business decisions. Data scientists build predictive and prescriptive models, train machine learning systems, and conduct open-ended research. Analysts operate closer to business stakeholders. Scientists operate closer to R&D.
Both roles require strong analytical skills and clear communication, but the technical depth, the types of data involved, and the tools are quite different.
Machine Learning, Business Intelligence, and the Tools that Matter
The toolkits for data analysts and data scientists overlap in places but diverge quickly. Data analysts primarily rely on SQL, data visualization tools like Tableau and Power BI, and business intelligence platforms for dashboarding and reporting. Data scientists lean heavily on programming languages like Python and R, machine learning techniques, statistical modeling, and big data technologies.
Common data science tools include Jupyter Notebooks for exploratory data analysis, cloud computing platforms for data storage and scalability, and machine learning frameworks for predictive modeling. Data analysts lean on data visualization software and business intelligence tools to communicate findings to leadership.
But here is a perspective most competing programs do not talk about: tools change fast, and fixating on them can backfire.
“People focus too much on learning specific tools rather than understanding what those tools are trying to accomplish. A tool that is cutting-edge today may be obsolete in a year. If you understand the fundamentals, you can adapt as new tools emerge.”
— Gabriel Isaacs, Chair of the College of Business, Keiser University
This is one of the strongest arguments for a degree that teaches the full analytics pipeline, from data collection and data cleansing to data transformation, data modeling, and data visualization, rather than a bootcamp that trains you on one platform.
Real-World Applications: How Data Analytics Solves Business Problems
Data analytics and data science are not abstract disciplines. They touch every industry. Healthcare organizations use data analytics for patient care optimization and risk management. Financial institutions rely on it for fraud detection and data security. Retailers apply it to inventory management, customer behavior analysis, and personalized marketing. Manufacturers use it to predict maintenance needs and improve supply chain efficiency.
Isaacs shared a case study from his own career running electronics manufacturing operations in China. A small but consequential percentage of units were showing software failures, and the cost in warranty claims and insurance was enormous.
“We introduced Six Sigma analysis and built data collection into every step of the manufacturing process. Data science principles were critical: we used them to determine what type of data we needed, at what frequency, from which sources, and how to validate it. Then the business analytics team analyzed those patterns to identify root causes and make decisions.”
— Gabriel Isaacs, COO, Resource Conservation Technology/VIZCO
His example illustrates the upstream-downstream relationship in practice. Data scientists set up the data pipelines and collection architecture. Data analysts and the business analytics team then turned that information into decisions that reduced error rates and saved the company real money.
He also pointed out a reality about scale that most people miss:
“If an airport has a 99.999% safety record, that sounds impressive, but with a million flights a year, that fraction of a percent represents real crashes. You have to reach 99.99999% to operate safely at that scale. That level of precision requires deep data control.”
— Gabriel Isaacs, Keiser University
That kind of insight, rooted in years of operational experience, is what distinguishes a program led by faculty who work in the field from one built around theory alone.
Future Trends: Why Data Professionals Are in High Demand
The job market for data professionals is among the strongest in the economy. According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 34 percent from 2024 to 2034, making it the fourth-fastest-growing occupation in the country. Roughly 23,400 openings for data scientists are expected each year over that decade.
Analytics-adjacent roles are growing rapidly too. Operations research analysts, a category that includes many business analyst positions, are projected to grow 22 percent over the same period. The World Economic Forum has listed data analysts among the fastest-growing jobs through 2030.
The convergence of artificial intelligence, big data, and cloud computing is accelerating demand across every sector. Data governance, data security, and ethical data use are emerging as critical skill areas. According to industry projections, advanced analytics and data science positions could see demand exceed supply by 30 to 40 percent by 2027.
Whether you lean toward data analytics or data science, the career outlook is strong. The question is which path matches your strengths.
How To Choose: Data Analytics or Data Science?
Data analytics is likely your path if you prefer working with structured data, creating dashboards, collaborating with business teams, and translating numbers into clear stories.. Business analysis roles reward communication, curiosity, and the ability to solve business problems using existing data.
Data science may be a better fit if you are drawn to building algorithms, experimenting with machine learning, handling unstructured data, and predicting future trends. Data science careers reward deep programming skills, comfort with ambiguity, and a strong foundation in statistical methods.
Data analysis is often a stepping stone toward data science. Many data professionals start in analytics, build their technical skills on the job, and transition into data science roles over time. The skills and knowledge gained in data analytics are highly transferable.
Isaacs noted that the most successful graduates are not tool specialists. They are people who understand the entire data science life cycle, from data collection and data wrangling through data warehousing, data mining, and data-driven decision making, and can adapt regardless of which platform an employer uses.
“Business analytics looks different in every organization. A graduate joining an established analytics department will apply their tools and process knowledge immediately. A graduate joining a company starting from scratch should be able to help build that capability from the ground up.”
— Gabriel Isaacs, Keiser University
How Keiser University Prepares Business Analysts and Data Professionals
Keiser University’s Bachelor of Arts in Business Analytics is a 121-credit-hour program that covers the full pipeline: business intelligence, applied artificial intelligence, Python and R programming, data mining, data warehousing, cloud computing, data visualization, database management, data governance, and a capstone or internship.
The curriculum is designed to produce graduates who can step into analytics roles on day one, whether at an enterprise with an established data team or at a growing company building its analytics function from scratch.
Keiser’s model is built for working adults, career changers, and military veterans. Classes are available online, on campus across Florida, or in hybrid format. Small class sizes mean direct access to faculty who, like Isaacs, bring real industry experience into every lesson. Financial aid, scholarships, and tuition assistance are available to those who qualify.
Students who want to build broader technical depth can also explore Keiser’s Bachelor of Science in Software Engineering or Bachelor of Science in Information Technology for paths that lean more heavily into data engineering, data architecture, and data science.
Frequently Asked Questions
Is data analytics easier than data science?
Data analytics has a lower barrier to entry. Many data analysts start with a bachelor’s degree and foundational skills in SQL and data visualization. Data science typically requires deeper programming, statistical modeling, and machine learning knowledge, and may call for an advanced degree.
Can a data analyst become a data scientist?
Yes. Data analysis is a common stepping stone. Analysts who build skills in machine learning (ML), predictive analytics, predictive modeling, and programming languages like Python can transition into data science roles with additional education or certifications.
What tools do data analysts use vs. data scientists?
Data analysts primarily use SQL, Microsoft Excel, Tableau, and business intelligence tools. Data scientists use Python, R, TensorFlow, big data platforms, and machine learning frameworks. Both roles use data visualization and statistical analysis, but the depth of technical skills differs.
Is a business analytics degree worth it?
Business analytics is one of the fastest-growing fields in the economy. A degree that covers the full data pipeline, from data collection through data modeling and prescriptive analytics, positions graduates for roles across healthcare, finance, manufacturing, retail, and government.
Does Keiser University offer a data analytics program online?
Keiser University currently offers the Bachelor of Arts in Business Analytics on-campus at Flagship campus and the AA in Business Analytics is offered online, with flexible scheduling designed for working professionals, career changers, and military veterans.
—
Since 1977, Keiser University has been empowering students to achieve their career goals through career-focused, institutionally accredited education. As one of Florida’s largest private, non-profit universities, Keiser is accredited by SACSCOC (learn more about Keiser University accreditation), ensuring degrees are respected by employers and other institutions nationwide. Founded by Dr. Arthur Keiser and Evelyn Keiser, the university is built on a student-centered model designed to support working adults, transfer students, and first-time college learners.
As a Keiser University student, you will benefit from:
- Over 100 associate, bachelor’s, master’s, and doctoral degree programs aligned with today’s workforce
- Flexible learning options, including on-campus, online, hybrid, and one-class-at-a-time formats
- Personalized academic and career support, with dedicated faculty and career services
- Financial aid, scholarships, and tuition guidance to help make education more accessible
- Proud membership in the Hispanic Association of Colleges and Universities (HACU)
- More than 45 years of educational excellence with a proven focus on student success and outcomes
Contact Keiser University today to learn how our accredited programs, financial aid options, and career-focused approach can help you move forward with confidence. Call toll-free 888-KEISER-9, contact a Keiser campus near you, or schedule a campus tour to take the first step toward your career.
After experiencing profound personal loss, Sydney Nau found her purpose in nursing. Today, she leads with compassion while advancing her education and transforming lives through patient-centered care.
