The fields of business analytics and data science share the goal of using large amounts of data to understand information and solve problems. The most significant differences between data science and business analytics are the level of technical knowledge required of practitioners and how that knowledge is used.
Syracuse University offers master’s programs online in both fields: a Master of Science in Business Analytics taught by faculty in the Martin J. Whitman School of Management, and a Master of Science in Applied Data Science taught by faculty in both the Whitman School and the School of Information Studies.
Through the Master of Science in Business Analytics, students learn how to interpret data to improve processes. Students may end up assuming roles in which they use their knowledge to guide their organizations in making evidence-based, actionable business decisions. The Master of Science in Applied Data Science helps students better understand complex data, applying analytical and technical skills to execute data-driven decisions. As a result, a number of career opportunities outside of data analysis exist for professionals with a data science degree.
Learn about other career opportunities outside of a data analyst that exist for professionals with a data science degree
In today’s data-driven business world leveraging data analytics and data science has become essential for organizations to gain valuable insights make smarter decisions, and remain competitive. However, the terms “business analytics” and “data science” are sometimes used interchangeably, causing confusion about how they differ.
This article will clearly explain the key distinctions between business analytics and data science, providing a comprehensive comparison of these two data-focused disciplines.
Defining Business Analytics
Business analytics refers to the practice of analyzing data to drive business strategy, performance, and decision making. The core goal is to extract meaningful insights from available data sources to inform planning, forecasting, management, and other business functions.
Common types of business analytics include:
-
Descriptive analytics – Understanding what happened by analyzing past data on metrics like sales, costs and operational performance.
-
Diagnostic analytics – Determining why something happened by assessing relationships and drilling down into the factors behind metrics.
-
Predictive analytics – Estimating what will happen in the future by identifying trends and patterns in historical data.
-
Prescriptive analytics – Recommending the best course of action to take to achieve a desired future outcome.
While data scientists focus on building datasets, business analysts apply analytical techniques to interpret and communicate data insights in a business context.
Key responsibilities include data modeling, statistical analysis, visualization, reporting, and translating data findings into strategic recommendations. Business analysts typically have strong critical thinking, communication, and business acumen.
What is Data Science?
Data science focuses on extracting insights from raw, unstructured data using advanced statistical modeling and machine learning techniques. Data scientists work to organize, clean, merge, and structure datasets to support downstream analysis.
Core data science responsibilities include:
-
Data wrangling – Gathering data from diverse sources, cleaning it, and transforming it into a usable format.
-
Data mining – Applying machine learning algorithms to reveal patterns, relationships, and insights within large, complex datasets.
-
Statistical modeling – Developing and running mathematical and predictive models to derive quantitative insights from data.
-
Data visualization – Creating charts, graphs, and other visuals to clearly communicate key data findings and trends.
-
Programming – Writing code and scripts to automate data tasks using languages like Python, R, MATLAB, SQL, and Scala.
Data scientists combine strong analytical abilities with advanced technical skills like coding and computational statistics. Their work feeds into business analytics and enables deeper data-driven decision making.
Key Differences Between Data Science and Business Analytics
While data science and business analytics are complementary disciplines that work hand-in-hand, some notable differences exist:
-
Focus – Data science focuses on preparing raw data for analysis while business analytics is concerned with deriving insights from data.
-
Goals – Data science aims to provide clean, structured data. Business analytics turns data into strategic recommendations.
-
Methods – Data science relies on machine learning and statistical algorithms. Business analytics leverages descriptive, predictive, and prescriptive models.
-
Required Skills – Data science demands programming and quantitative skills. Business analytics requires critical thinking and business acumen.
-
Toolsets – Data science uses Python, R, SQL, etc. Business analytics employs Excel, Tableau, Power BI, and statistical packages.
-
Audience – Data scientists support analysts, executives, and other roles. Business analysts communicate insights to leadership to drive strategy.
While distinct roles, effective collaboration between data scientists and business analysts enables impactful data-driven decision making and competitive advantage.
Complementary Skill Sets
Though different disciplines, data science and business analytics skill sets are highly complementary within organizations.
Data science expertise equips analysts to:
- Access clean, consistent data prepared for analysis.
- Leverage robust datasets and models developed by data scientists.
- Seek guidance interpreting complex machine learning model outputs.
Meanwhile, business analytics skills allow data scientists to:
- Ensure data initiatives align with business objectives and decision needs.
- Clearly communicate data findings to leadership using visuals and translations of statistical concepts.
- Receive feedback for refining datasets and models to be more business relevant.
With collaborative integration of their respective strengths, data scientists and business analysts can maximize the business value extracted from data.
Career Trajectories
The career paths for data scientists versus business analysts also show key differences:
-
Data Science – Data science roles tend to require advanced degrees in technical fields like computer science, applied math and statistics, or machine learning. Extensive skills in programming, algorithms, and modeling are needed.
-
Business Analytics – Business analyst positions typically demand bachelor’s degrees in business disciplines. Critical thinking, communication abilities, and business strategy knowledge are emphasizes over technical skills.
-
Crossover Potential – Some data scientists transition into analytics roles as they seek to apply their models directly for business impact. Analysts may pick up data science skills to expand their capabilities.
While distinct careers, crossover potential exists. Some organizations are even beginning to blend both roles into emerging positions like “business data scientists”.
Which is Right for You?
When deciding between pursuing data science or business analytics, consider your interests, abilities, and career goals.
Data science suits those who:
- Love statistics, math, programming, and algorithms.
- Enjoy abstract/theoretical problem solving and model building.
- Have advanced technical and analytical capabilities.
- Want to leverage quantitative skills for business insights.
Business analytics aligns with those who:
- Are strategically-minded with strong critical thinking abilities.
- Excel at extracting and clearly communicating data insights.
- Are interested in directly influencing business strategy.
- Enjoy interfacing between technical teams and company leadership.
Assess your strengths and passions to determine which field better fits your talents and interests. Both provide exciting, high-demand data-driven career paths.
Powerful Partnership
Though distinct disciplines requiring different skills, business analytics and data science work symbiotically to realize the full value of data for organizations.
Data science unlocks transformative business insights that were previously inaccessible before the advent of advanced analytics and machine learning. Business analytics ensures those insights get translated into data-driven actions that boost performance, profits, and competitive advantage.
A collaborative partnership between data scientists and business analysts enables companies to capitalize on data’s immense potential as a strategic asset. Mastering the synergy between these two domains is key to leveraging data analytics and science for business success.
Data Science vs. Business Analytics Courses and Curriculum Structure
Students in the master’s in data science and the master’s in business analytics programs are required to take core courses and analytics application courses, with some courses overlapping across the two programs. However, the business analytics program allows greater flexibility for students with career goals beyond just analytics, requiring fewer technology-focused courses and offering a wide variety of electives in technical and business topics. The data science program requires several courses in more technical topics and only offers electives that focus on technology and analysis.
The business analytics curriculum is driven more by the student’s goals.
- Core courses: Students take two core analytics courses and four courses that apply analytics to essential business areas.
- Elective courses: Students choose six in-depth electives from The Whitman School of Management and School of Information Studies. These include technical courses such as data warehousing and data analytics, as well as business courses such as strategic brand management and finance.
Learn more about the curriculum for Syracuse University’s Master of Science in Business Analytics.
The data science course sequence is much more focused on the how-to of data science.
- Core courses: There are six required core courses in analytical topics, including data mining and big data analytics. Students must then choose one or two courses in applying analytics to business-related fields.
- Elective courses: Students also must select four or five electives, which include technical topics such as text mining and natural language processing.
Learn more about the curriculum for Syracuse University’s Master of Science in Applied Data Science.
What Does a Data Scientist Do?
Many students in master’s in data science programs pursue roles in fields such as engineering and IT. They may be employed as data scientists/engineers, statistical programmers or database administrators. In these more technical roles, professionals manage large amounts of data, create visualizations and design and deploy algorithms that support decision-making tools.
Potential data science responsibilities include:
- Finding opportunities in data sets by mining data and writing algorithms to support decision-making processes.
- Creating an analysis foundation that can help others solve business problems.
- Managing large data sets by using methods such as linear discriminant analysis and multilinear regression selection.
- Designing and structuring databases.
Business Analyst vs Data Scientist
What is the difference between data analytics and data science?
What Is Data Science? Whereas data analytics is primarily focused on understanding datasets and gleaning insights that can be turned into actions, data science is centered on building, cleaning, and organizing datasets.
What is the difference between a data scientist and a business analyst?
A data scientist explores patterns and trends of all possible scenarios. A Business Analyst explores patterns and trends specific to the business. There is a lack of clarity of the problems that are needed to solve using data sets. Operations are a bit more costly than business analysis.
What is the difference between business analysis and data science?
Data Science can be considered a superset of Business Analysis. In Layman’s terms, Business Analysis mainly focuses on business-oriented problems and well-known and established methods to solve those problems whereas Data Science involves finding the best way to predict certain results using various algorithms.
What’s the difference between coding and Statistics in Business Analytics?
In contrast, it’s rare for professionals in business analytics to use coding to analyze data. Instead, they use statistical methods to study data and gain insights. While both fields rely on statistics for data analysis, statistical concepts are more important in business analytics.
