Data Science Degrees: Analytics MBA vs. Master’s in Data Science

Data science is a field notorious for its stringent educational requirements. The majority of data scientists have a master’s degree of some sort. Using data from over 15,000 users, we found that more than 65% of data scientists have at least a master’s degree.

The two most common professional degrees for data scientists are an M.S. in Data Science and an MBA specializing in business analytics. Although there is an overlap between these two types of programs, analytics MBAs and data science master’s have some marked differences.

A business analytics MBA, for example, generally focuses on data analysis and big data’s role in business. A data science master’s program is much more quantitively-oriented, focusing on the math, statistics, programming, and experimental design used in data science and machine learning.

The big question then becomes: Which one should you take? If you’re considering a professional degree in data science, consider the following questions:

• What is an analytics MBA?
• What are the critical differences between analytics MBAs vs. an M.S. in Data Science?
• Which type of degree should I pursue?

The Master of Business Administration (MBA) degree is a professional, two-year degree traditionally offered through a university’s business school. When students enroll in an MBA program, they’re typically required to choose a specialization, which could include subjects like accounting, marketing, or economics.

The Master of Business Administration (MBA) degree is a professional, two-year degree traditionally offered through a university’s business school. When students enroll in an MBA program, they’re typically required to choose a specialization, which could include subjects like accounting, marketing, or economics.

However, in the last ten years, many schools have begun offering MBA data science specializations. These may be alternately described as an MBA in data science, an MBA in data analytics, a business analytics MBA, or an analytics MBA.

This type of MBA program focuses on the intersection of business and big data; students learn how to use analytics and data science techniques to influence and impact business decisions and generate value for the company

While data science MBAs and Master’s degrees share similarities, there are distinct differences in curriculum and what you can expect to learn.

While MBAs in business analytics and M.S. in Data Science degrees share similarities, there are distinct differences in curriculum and what you can expect to learn.

Generally, MBAs in analytics focus heavily on business analytics and intelligence. In other words, you’ll build domain expertise in how businesses can use data to influence business decisions and generate revenue.

The data science master’s degree, on the other hand, doesn’t necessarily have a business focus. Instead, these degrees cover various topics, including machine learning, statistical analysis, and even data visualization.

MBAs Specializing in Business Analytics

MBA programs are typically offered by a university’s business school and focus primarily on business analytics. These programs allow students to focus on using data science techniques in business to generate value and make informed decisions.

Like most MBA programs, the focus is on business theory. Therefore, data science is used to apply and study those business theories. Key elements of this program include:

• Being offered by a university’s school of business.
• Business analytics or data analytics focused.
• The focus is on business theory, with data science as a tool for studying business theory.
• This may include coursework in data visualization, analytics, and business intelligence.
• Typically looks at analytics’ use in marketing and business intelligence.

Programming coursework varies by MBA program, and some require intermediate skills in languages like Python or SQL. Ultimately, if you pursue an MBA, you may have to supplement your programming studies with additional coursework or a boot camp.

Data Science Master’s

M.S. in Data Science programs typically offer through a university’s computer science, math, or engineering department. Therefore, these degrees tend to be focused on the math theory of data science rather than the practical applications of data science in business.

These degree programs also dive deeply into foundation knowledge of data science, including concepts like statistical modeling and machine learning. A few key distinctions include:

• Offered by university’s mathematics, engineering, or computer science schools.
• Scientifically driven, focused on mastering core data science concepts.
• Dives deeply into programming, including coursework focused on SQL, Python, R, or Java.
• Focus on mathematics and statistics.
• Not necessarily focused on analytics or business; may include study in subject areas like public health or urban planning.

One thing to note: Many data science positions require business acumen. Knowing this, business sense is often taught on the job, and many master’s programs in data science offer business coursework.

It’s well known that you need at least a bachelor’s degree to launch a career in data science, given the advanced statistics and mathematics background required. Historically it has been unclear how common rates of higher education have been for data scientists, however.

As mentioned earlier, by analyzing data from more than 15,000 users, we gathered the following insights about the demographics and characteristics of data scientists. Instead of drafting an anecdotal conclusion, we’ll let the data speak for itself.

A few things stand out. First, it’s exceedingly difficult to become a data scientist without a bachelor’s degree. Second, a master’s degree has become increasingly common among data scientists. Here’s what the data shows:

• About 90% of all data scientists had a bachelor’s degree.
• A record 65% of all data scientists have a master’s degree.
• 28% of data scientists completed a PhD.

Also note that only 3% of data scientists have completed certificates. This runs contrary to what many certificate companies want you to believe - that you need certifications to become a data scientist.

Choosing to pursue an M.S. in Data Science or Analytics MBA comes down to career goals and the type of work you want.

The MBA will better prepare you for your work if you’re passionate about business and learning how to apply data science to influence business outcomes.

Jobs for Analytics MBAs

If you’re interested in statistics and science or data science, for example, designing A/B testing experiments, then a traditional data science master probably makes more sense. Generally, a business analytics MBA will prepare you for these types of data science roles:

• Business analyst
• Product manager/analyst
• Marketing analyst
• Business intelligence analyst
• Growth marketing analyst
• Business ops roles

One advantage of an MBA is that it offers more job diversity if you choose a career outside of data science. For example, an analytics MBA would also prepare you for finance, accounting, and marketing roles.

Jobs for M.S. in Data Science

A traditional data science master’s prepares candidates for data science, machine learning, and research-related roles. You might choose a data science master’s degree if you’re interested in these roles:

• Data/Machine Learning Engineer
• Data Scientist
• Research Scientist
• Data Analyst
• AI/Deep Learning Specialist

Let’s apply a different lens to your evaluation of the two pathways. Instead of focusing on what we gain by choosing one program over the other, let’s consider our potential losses. What are we losing by choosing an analytics MBA over M.S. in Data Science (and vice versa)?

Analytics MBA

a.) Technical: By choosing an analytics MBA, you might miss out on the in-depth study of machine learning, deep learning, and advanced statistical modeling that an M.S. in Data Science offers. The technical focus here is more on basic statistics, data visualization tools, and entry-level programming, aimed at business applications.

b.) Practical: The analytics MBA’s strength in integrating data analytics into business functions and emphasizing real-world applications might come at the expense of the hands-on technical experience in cutting-edge fields like AI or computer vision, which is provided in an M.S. in Data Science.

c.) Soft: While focusing on leadership, management, and networking skills in an analytics MBA, you might lose out on the deeper technical collaboration and specialized communication skills found in a data science program.

M.S. in Data Science

a.) Technical: Opting for an M.S. in Data Science will allow you to delve into technical areas such as machine learning and big data processing, but you may lose out on the business-oriented data interpretation and visualization that an MBA provides.

b.) Practical: The M.S. in Data Science’s focus on scientific methodologies, specialized projects, and lab work might mean sacrificing the broad business strategy, marketing, finance, and operations insights found in an analytics MBA.

c.) Soft: Choosing an M.S. in Data Science might lead to a limited emphasis on leadership, management training, or networking opportunities compared to an analytics MBA. While gaining in-depth technical and practical experience, you might miss out on the comprehensive soft skills that prepare you for various roles within the business world.

When it comes to cost, there is no clear-cut answer as to which program is more expensive. It depends on what’s available in terms of geographical proximity, as well as the individual tuition fees these universities charge for these programs.

Looking at averages, an analytics MBA is typically more expensive, with tuition ranging from 40,000 dollars to over 100,000 dollars for the entire program, along with additional costs for books, materials, and potential travel for networking events. In the other corner, an M.S. in Data Science is, on average, less expensive, with most tuition ranging from 20,000 dollars to 60,000 dollars for the complete program.

The best way to prepare for your master’s, whether an analytics MBA or an M.S. in Data Science, is through practice. To get better, build projects that tickle your fancy. Having hands-on experience is always the best way to prepare yourself. Not only are you exposed to unfamiliar environments, but you also learn to understand concepts on your own.

You can also try honing your statistics skills with this resource. Or, you can opt for something even better: a learning path to better monitor your progress.

The decision between an analytics MBA vs. an M.S. in Data Science involves evaluating both gains and losses. From technical to practical and soft skills, each program has unique offerings and trade-offs.

Costs, too, are a vital factor, and understanding the financial investment required for each path is key. Careful consideration of your career goals and interests, coupled with research into specific programs, will guide you to the choice that aligns best with your future aspirations.