When I was choosing courses for my MS in Business Analytics degree, I found myself feeling quite confused. My uncertainties revolved around two main questions: What will I learn? And what career opportunities will be available to me? Understanding the distinction between business analytics and data science became essential.

To begin my discovery path, I jumped to the jobs section in my search. One of the most attractive qualities of these degrees was the salaries. According to ONET OnLine, a government-sponsored database, a business analyst earns an average of $88,550 per year while a data scientist earns $122,840 per year. The figures looked attractive to me, but why were they so different? I had to go back to the basics and find out.

Are Data Science and Business Analytics Relevant Degrees?

Finding the differences between data science and data analytics might not be an isolated query just for professionals. Internet use has increased by 70% since this past spring — making the appropriate use of data essential. The sectors of business, healthcare, entertainment, manufacturing, transportation, banking, and others, precisely monitor data to make business decisions. According to my findings, there is nothing but growing opportunities in these fields. Even the general public will soon use some of the technologies and strategies to use data more effectively and efficiently. That’s why learning the difference between business analytics and data science is relevant to many.

Comparing Business Analytics to Data Science

What is Business Analytics?

Business analytics serves as a bridge between information technology and business, utilizing analytics to provide data-driven recommendations. This discipline requires a deep understanding of business as well as proficiency in data, statistics, and computer science.

What Does a Business Analyst Do?

According to LinkedIn Talent Solutions, a business analyst acts as a communicator, facilitator, and mediator, striving to enhance processes and increase efficiency through technology, strategy, and analytical solutions.

Skills of a Business Analyst

The Naveen Jindal School of Management describes the following marketable skills for individuals who earn an MS in Business Analytics:

• Interpretation: Business analysts must clean and interpret vast amounts of data to make it useful.
• Data Visualization and Storytelling: Proficiency in data visualization is essential. Business analysts utilize visual elements like charts, graphs, and maps to present trends, outliers, and patterns effectively.
• Analytical Reasoning: Skills in logical reasoning, critical thinking, communication, research, and data analysis are vital for applying descriptive, predictive, and prescriptive analytics to solve business challenges.
• Mathematical and Statistical Skills: The ability to collect, organize, and interpret numerical data is crucial for modeling, inference, estimation, and forecasting in business analytics.
• Written and Communication Skills: Strong communication skills are key to influencing management decisions and recommending business improvements.

What is Data Science?

Data science is the process of studying data through statistics, algorithms, and technology to derive solutions and predict outcomes for specific problems.

What Does a Data Scientist Do?

Data scientists apply machine-learning algorithms to various forms of data—numbers, text, images, videos, and audio—to extract insights. As described by Hugo Bowne-Anderson in the Harvard Business Review, “Data scientists establish a solid data foundation for robust analytics, employing online experiments, among other methods, to foster sustainable growth.”

They also build machine learning pipelines and develop personalized data products to enhance their understanding of business and customer behavior, leading to more informed decision-making. In the tech realm, data science encompasses infrastructure, testing, machine learning, decision-making, and the creation of data products.

Skills of a Data Scientist

Core skills necessary for data science include:

• Statistical Analysis: Familiarity with statistical tests and likelihood estimators is essential for detecting patterns and anomalies.
• Computer Science and Programming: Data scientists work with massive datasets and must be adept at programming languages such as Python, R, and SQL to uncover solutions.
• Machine Learning: A solid understanding of algorithms and statistical models that allow computers to learn from data is crucial.
• Multivariable Calculus and Linear Algebra: Knowledge of these mathematical concepts is important for building machine learning models.
• Data Visualization and Storytelling: After analyzing the data, data scientists must effectively present their findings to both technical and non-technical audiences using data visualization tools.

Data science is a broad and complex topic. To learn more, consider reading the blog posts “What Data Scientists Really Do” in the Harvard Business Review, “Top 10 Skills for a Data Scientist” in Towards Data Science, and “What is Data Science” on Thinkful.

So, Which Path Is Right for Me?

Business analysts take a hands-on approach, managing and interacting with data, while data scientists focus more on data development. In my opinion, a business analyst can transition into a data science role with additional training and experience. I chose to pursue business analytics for its flexibility and the opportunities it offers.