Business Analytics vs. Data Science: What’s the Difference?

Companies are exploring new ways to unlock their data and use it for process improvement, enhanced decision-making and automation. To realize those benefits, organizations need employees who are comfortable retrieving, analyzing and interpreting data. This has resulted in a growing demand for data-driven roles.¹

As someone interested in a career in data, you may consider business analytics vs. data science. Both disciplines involve analyzing and interpreting data, but their goals, tools and career paths differ in important ways. Understanding how they diverge can help you make a more informed decision about your next step.

In this post, you’ll learn the differences between data science and business analytics, including common responsibilities and career opportunities in each field.

Defining Business Analytics

Business analytics focuses on using existing data for informed decision-making. Analysts may obtain data through their company’s operations or through publicly available resources. They review, process and visualize data to identify relationships and patterns that aren’t immediately clear from a cursory analysis.²

Business analytics is useful for a broad array of scenarios. For example, organizations may rely on analytics to monitor key indicators and track customer sentiment. They can also use analytics to resolve business problems and identify opportunities.²

Common tools used in business analytics include Microsoft Excel, which supports complex data analysis. It also offers data visualization tools such as charts and scatter plots. For more advanced needs, analysts often integrate Power BI into their workflows. It is another Microsoft product that’s designed exclusively for data analytics and visualization.³

Complex databases may require knowledge of Structured Query Language (SQL) to retrieve and analyze data. SQL is a programming language primarily used by business analysts and database administrators, and is usually included in business analytics curricula.³

With a degree in business analytics, you may pursue roles such as business analyst, management analyst or market research analyst. Positions that involve interpreting existing data to guide decisions often align well with this skill set.⁴

Defining Data Science

Data science uses math, statistics, artificial intelligence (AI), machine learning and programming to interpret an organization’s data and make decisions. Individuals who work in data science typically begin by examining existing data before building customized models to predict future outcomes.⁵

A typical day as a data scientist may begin by defining a problem that data can answer. The individual will collect relevant data sources and begin the cleansing and pre-processing stage. After transforming and loading the data into a storage repository, they can begin the modeling and analysis process.⁵

Data scientists may test several models before identifying one that delivers reliable insights. After applying the model and retrieving results, they can prepare data visualizations or reports to share with relevant stakeholders.⁵

Most data scientists are comfortable using Python and R programming languages. Python includes several libraries well-suited for data analysis, including Pandas and Matplotlib. R is commonly used for statistical analysis.⁵

With a data science master’s degree, you may qualify for a role as a machine learning engineer, database administrator, data scientist or data engineer.⁶

Key Differences Between Business Analytics and Data Science

Business analytics and data science differ in their goals, technical complexity and scope. Data science aims to resolve highly complex problems using advanced statistical models and algorithms, while business analytics interprets results using statistics.⁷

Business analytics is generally less complex than data science. While analysts may benefit from learning SQL, they usually don’t need to know other programming languages. That isn’t the case for data science, which requires Python and R knowledge.^3,5

The scope of each discipline is different, too. While business analytics uses descriptive analysis and visualizations to showcase insights, data science relies on predictive analysis. This requires selecting an appropriate statistical model to yield accurate forecasts.^2,7

Data scientists may enter the field with a bachelor’s degree in computer science or data science. Some roles may require a master’s degree in either discipline.⁶

Individuals interested in business analytics can benefit from a bachelor’s degree in business. Earning a master’s in business analytics may open the door to career advancement opportunities.⁴

Career Opportunities and Salaries

According to the U.S. Bureau of Labor Statistics, demand for data-related jobs is projected to grow much faster than average. Between 2024 and 2034, employment for data scientists is expected to increase by 34%.⁸ Demand for management analysts, a close cousin of business analysts, is projected to increase by 9% during the same period.⁹

Based on Payscale data, business analysts earn an average annual salary of $69,920.¹⁰ Data scientists earn an average of $102,938 annually.¹¹

Individuals with a business analytics or data science background can find roles in many industries, including healthcare, tech and finance. It’s a versatile skill set that is useful across many settings.

Elevate Your Career With an Online Master’s in Business Analytics

Data science and business analytics share a common foundation in data-driven problem-solving. While they vary in complexity and use distinct tools, business analytics and data science are highly complementary. When choosing a data discipline, consider your interests and career goals. If you gravitate toward solving intriguing problems using complex models, data science may be a good fit. Business analytics is suitable for professionals interested in interpreting and visualizing existing data.

William & Mary Raymond A. Mason School of Business offers a data science and business analytics program for aspiring professionals. Our Online Master’s in Business Analytics (MSBA) blends both disciplines into one program. As a student, you’ll learn R, SQL and Python, helping you excel in data-driven roles. Our curriculum includes courses in machine learning, prescriptive analytics, decision modeling and AI.

The William & Mary Online MSBA offers asynchronous courses, so you can study from the comfort of your home or office on your schedule. It may be completed in as little as 16 months, allowing you to qualify for an in-demand role as a data scientist or business analyst.

To learn about the W&M Online MSBA admissions process, schedule an appointment with an admissions outreach advisor or contact us directly.