The COVID-19 pandemic has permanently changed the nature of business, forcing leaders to innovate under the pressure of unprecedented disruption.
Uncertainty has touched every aspect of the crisis, so it’s no surprise that more organizations are harnessing data analytics and artificial intelligence (AI) technology, widely recognized for its problem-solving and predictive power, to survive. A 2020 study by Sisense found that the pandemic has accelerated the use of analytics across industries, with 50% of companies relying on data more than ever before. From forecasting coronavirus hotspots to predicting consumer demand, organizations are expanding beyond the traditional applications of analytics to adapt to the challenges of our new environment.
The only problem is, many enterprises aren’t seeing the results they expected from their investments. While there are many reasons why analytics projects fail, a common culprit is a shortage of workers with a combination of foundational analytics skills and domain-specific expertise. To avoid this mistake, one framework recommends that organizations invest in data literacy across job roles and sectors.
Pace University, for example, has been taking steps to mitigate the data science talent shortage by preparing students for the data-driven world we are living in. A team of faculty and researchers at Pace was recently awarded a five-year grant for a project with the goal of incorporating data science literacy in undergraduate biology and environmental sciences curricula.
As universities continue to graduate analytics professionals, a plethora of opportunities are cropping up at companies for employees with competencies in both analytics and related disciplines, such as business analytics, information systems, and natural sciences. With knowledge on how to turn raw data into insights and expertise within a specific field, they are able to lead transformations that build capable, resilient, and adaptable businesses. And with the ability to fill the data science skills and talent shortage, they can become indispensable assets to their current and future employers.
Earning a master’s degree in data science can equip you with the critical technical and professional skills required to lead the next generation of data-driven transformations across a wide range of sectors. For those interested in using data for business applications, the choice often comes down to a master’s in data science or a master’s in business analytics.
The Difference Between Data Science and Business Analytics
“Data science” and “business analytics” are often used interchangeably to describe the process of analyzing data to make better business decisions, but there are significant differences between the two domains.
Here are some of the core differences between data science and business analytics:
- Scope: Data science is broad, with the goal of gathering high-level insights for business use, whereas business analytics is specific, with the goal of solving business problems and guiding business decisions.
- Objective: The objective of data analysis for business analytics professionals is to uncover trends and improve business decision-making. For data science professionals, on the other hand, the objective of data analysis is to understand what drives these trends and to predict future outcomes.
- Approach: Data science professionals approach their work through statistical and mathematical models, while business analytics professionals take an integrative approach, combining mathematical, operations, and business models.
- Communication: Professionals in either field must be able to communicate their insights to experts and stakeholders, but business analytics professionals must go one step further by developing and presenting actionable strategic recommendations.
Master’s in Data Science vs Business Analytics: Which Degree Should You Pursue?
Exploring the choice between a master’s in business analytics or master’s in data science program can be challenging since graduates of both degree programs develop expertise on how to gather and analyze data.
In general, an MS in Data Science offers an in-depth exploration of foundational programming and math concepts, centered on quantitative theory, while a MS in Business Analytics is more focused on business outcomes and provides knowledge of analytics skills and leadership techniques. The credit hours required and time for completion vary by university, but both programs typically range from 30 to 40 credit hours and might take anywhere from 1-2 years to complete.
Below, we outline an MS in Data Science vs an MS in Business Analytics, covering details on admission requirements, core courses, and learning outcomes for each of these program offerings.
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Aside from factors related to curriculum and degree requirements, it’s critical to weigh other aspects that might be important to you. Some examples of program components you may want to consider based on your unique needs include:
- Program flexibility: If you are a working professional, you’ll want to consider an online master’s program that offers greater flexibility in your schedule.
- Experiential learning opportunities: A program that integrates a project-based Capstone is a great choice for those who value practical, real-world experiences.
- Specialization choices: Specialization options offer an opportunity to develop a niche skill set. Some examples of program specializations for an MS in Data Science are “Business Analytics” and “Information Systems.”
- Networking experiences: For those who value the importance of establishing professional connections at graduate school, a program with a strong network of industry-connected alumni and faculty can be a good choice.
Careers with a Master’s in Data Science
Remember those businesses that are investing in analytics projects but aren’t seeing the results they expected? They’re becoming more aware of the mishaps that can occur if they don’t have experts on their team who can bridge the data skills gap in their organizations. Students earning a master’s in data science are equipped to fill these roles and can expect to to launch a career in one of the most sought-after fields in the world.
According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is expected to grow by 22% between 2020 and 2030, placing it among some of the fastest growing occupations today. Those considering a career in data science can also look forward to a substantial salary, as the average annual earnings for data scientists was $126,830 in 2020.
A master’s degree is generally required for entry-level data science jobs, as it gives you the training necessary for this technical field. The following are some in-demand positions for graduates of master’s in data science program:
- Data scientist
- Machine learning engineer
- Computer systems analyst
- Data architect
- Statistician
- Data engineer
Learn more about what you can do with a Master’s in Data Science.
Careers with a Master’s in Business Analytics
According to PayScale, the average salary for master’s in business analytics holders is $74,000 per year— higher than those at the bachelor level. Like data scientists, business analysts are in high demand, with expected job growth of 14% between 2020 and 2030.
While entry-level jobs commonly require a bachelor’s degree, a master’s can boost your career and increase your earning potential. Common master’s in business analytics job positions include:
- Management analyst
- Data analyst
- Market research analyst
- Business intelligence developer
- Project manager
- Operations research analyst
About the Online MS in Data Science at Pace University
The Pace University online Master of Science in Data Science was designed to help students take advantage of professional opportunities in the next generation of quantitative solutions. Our STEM-designated curriculum leverages the Seidenberg School’s decades of experience in online education to explore theoretical and practical approaches to data governance, machine learning, predictive analytics, and more. This flexible, 100% online program fits a combination of hands-on experience and asynchronous activities into your schedule, building the expertise you need to guide the future of data-driven organizations.