Data Science vs. Actuarial Science: Which Path Is for You?

Data science and actuarial science both offer access to stable, fast-growing, and well-paid career paths. Either field may interest you if you enjoy analyzing quantitative data, since both focus on using data as a guide for organizational decision-making.

Despite similarities and overlap, data science and actuarial science also differ significantly. Data scientists mainly use modeling techniques to guide business operations while actuaries focus specifically on financial risk management.

Data scientists also work in all kinds of business environments while actuaries are heavily concentrated in insurance and finance.

This guide compares the two professional tracks. Use it to inform your career research as you seek the best match for your interests.

What Is a Data Scientist?

Data scientists specialize in extracting strategic or actionable insights from quantitative data. Their work informs decision-making processes in many business areas, from operations management and marketing to product development and customer service.

In carrying out their duties, data scientists interact with other data-focused professionals including data analysts, data engineers, and data architects. They also liaise with business managers, leaders, and stakeholders, especially when communicating key results or findings.

Their main duties and responsibilities include:

• Sourcing, gathering, collecting, visualizing, and analyzing data to answer targeted questions
• Using data analytics and predictive modeling to make business recommendations
• Validating and testing computerized data management tools

Data scientists carry out these duties in office settings. According to data published by the U.S. Bureau of Labor Statistics (BLS) in May 2022, the five industries with the highest levels of data scientist employment are:

• Computer systems design
• Business management
• Management, scientific, and technical consulting services
• Scientific research and development services
• Insurance

Data quality and availability are the main challenges data scientists face in carrying out their duties. They also encounter complex ethical questions, especially with regards to data privacy and the potential applications of their work.

Career Paths for Data Scientists

Data scientists have an excellent career outlook: The BLS projects 35% job growth in the profession from 2022–2032. That works out to an estimated 59,400 new data scientists jobs and dramatically outpaces expected job growth rates for all professions.

BLS data also points to the profession's excellent earning potential. As of May 2022, the BLS cited median salaries of $103,500 per year for data scientists. The top 10% of data scientist earners made more than $174,790 per year.

Data scientists working in scientific research and development, business management, and computer systems design earn the highest average wages. Industries paying sub-median salaries include insurance and management, scientific, and technical consulting.

In terms of career progression, data scientists move from junior into senior or specialized roles. The C-suite chief data officer or CDO title is the profession's summit.

Particularly important and in-demand data science specializations include:

• Artificial intelligence and machine learning
• Business intelligence
• Cloud computing
• Data mining

These areas are highly relevant in the contemporary business environment. They also figure to benefit from continued demand and/or growth well into the future.

What Is an Actuary?

Actuaries specialize in the data-driven analysis of risk and uncertainty. They use a combination of statistical analysis, probability mathematics, and theoretical modeling drawn from economics and finance.

Actuaries work almost exclusively in office environments, often alongside accounting, finance, and underwriting professionals. The scope and nature of their duties differentiate actuaries from data scientists and other finance professionals. Actuaries concentrate solely on calculating the economic costs and implications associated with uncertainties about future events and outcomes.

Their primary job duties include:

• Analyzing statistics to determine the probabilities of future events, and the potential costs associated with those events
• Using mathematical modeling to test insurance products and business or investment strategies
• Assisting with the management of organizational cash reserves to cover the potential costs of possible future events

In performing their duties, actuaries must balance efficiency with accuracy, one of their key challenges. Another relates to the nature of actuarial work, which is often repetitive.

Career Paths for Actuaries

Actuaries earn high salaries and have excellent job growth prospects. BLS data from May 2022 cites median salaries of nearly $114,000 per year and projected job growth of 23% from 2022–2032.

According to the BLS, 80% of actuaries work in the finance or insurance industries, which also offer the highest average pay rates. Actuaries working in government also enjoy above-average earnings. Notably, actuaries working in the enterprise management industry tend to earn below-median salaries.

You can also access an expanded set of career paths by gaining actuarial science experience or combining it with degrees in related fields. Options include roles in:

• Business consulting
• Enterprise risk analysis and management
• Investment management
• Personal or organizational financial planning

Actuaries can also ascend to upper-level management and executive roles in business, especially if they add a credential like an MBA.

Similarities Between Data Scientists and Actuaries

In comparing actuarial science vs. data science, the most obvious similarity involves quantitative data. Actuaries and data scientists both analyze large quantities of statistical and numerical information, often using specialized software.

Other major similarities include:

Also, actuaries and data scientists both work primarily in office environments and apply their skills to inform organizational decision-making.

Differences Between Data Scientists and Actuaries

One of the main differences between actuaries vs. data scientists relates to the scope of their work. Actuaries have a strong or exclusive focus on risk assessment and management. They concentrate solely on analyzing risks and uncertainties, and their known or potential associated costs.

Meanwhile, data scientists apply their analytical proficiencies across many areas. While any given data scientist job may or may not have a narrow focus, the field itself has much wider business applications.

Other major differences extend to:

Should You Become an Actuary or a Data Scientist?

Both career paths demand an affinity for numbers and a knack for analyzing them. To choose between actuarial science vs. data science, it may help to root your perspectives in how the fields differ.

For instance, data science might be a better choice if you enjoy computer science and predictive analytics. Data scientists incorporate these elements into their work more frequently, and to a deeper degree. Conversely, actuarial science could make a more natural match if you have a stronger interest in finance.

Data science also tends to offer more opportunities to apply creative thinking. As a data scientist, you need to find interesting and engaging ways of communicating the story your analysis tells. Thus, data scientists tend to make more robust use of visualization tools and other unique ways of relaying their findings.

Also, consider program availability: Data science has a higher profile and more colleges offer programs in it. According to the National Center for Education Statistics (NCES), 250 U.S. institutions offer data science programs as of the 2023–24 school year. By comparison, the NCES tracked 155 actuarial science programs.

Data Science vs. Actuarial Science: Pros and Cons

How to Become a Data Scientist

The journey to becoming a data scientist typically begins with formal education. This can take many forms, including:

• Bootcamps
• Certificate or diploma programs
• Undergraduate or graduate degrees

With postsecondary institutions offering data science programs in growing numbers, you can study the field directly. However, you may also pursue a closely related area such as computer science, information science, or mathematics.

Experts suggest that you supplement core learning by fostering knowledge or expertise in a particular domain or industry. Examples include healthcare, social media marketing, and sales. Consider areas of personal interest or high labor market demand.

As an alternate path, you can earn an undergraduate degree in a domain-focused subject. Then, pursue a master's degree in data science to build out your credentials.

During your career, keep your abilities current by engaging in regular professional development and upskilling. Data science changes fast, and you will position yourself for sustained success by changing with it.

Professional certifications may also enhance your employability. Some of the organizations profiled in the next section offer them.

Professional Organizations for Data Scientists

Get an Education in Data Science

Best Bachelor's Degrees in Data Science

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Top Master's Degrees in Data Science

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Ph.D. Programs in Data Science

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Data Science Bootcamps

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How to Become an Actuary

Degree programs in actuarial science offer a direct path into the profession. However, you can also major in many other related subjects, including accounting, business, economics, or mathematics. Some actuaries also come from academic backgrounds in the liberal arts.

You can also study actuarial science or a related subject at the graduate level after earning your bachelor's degree. This will typically add 1-2 years to your educational timeline, but it may help you build deeper or more targeted knowledge.

After completing your education, you must pass a series of challenging exams. Organizations that issue these exams include:

• American Society of Pension Professionals and Actuaries
• Casualty Actuarial Society
• Society of Actuaries

According to the American Academy of Actuaries, the examination process typically takes several years. Developing actuaries may work in entry-level or junior roles while working toward full credentialing.

You can find more information and additional resources through the professional organizations profiled in the next section.

Professional Organizations for Actuaries

More Questions Comparing Actuarial Science vs. Data Science

Is data science more difficult to study than actuarial science?

Both fields are difficult and demand excellent quantitative analysis skills and sharp attention to detail. Broadly speaking, data science has more interdisciplinary elements. Depending on your outlook and interests, this could make it harder or easier to study.

What does an actuary do that's different from a data scientist?

Actuaries focus exclusively on questions of risk, uncertainty, and their potential costs and economic impacts. Data scientists engage with a much broader set of questions and possibilities related to organizational opportunities and decision-making processes.

Is actuarial science a good major?

Actuarial science is an excellent major for analytically minded people seeking access to a stable, lucrative, and well-established career path. It also consistently ranks among the best-paying bachelor's degrees a student can earn.

Is data science a good major?

Data science is a strong choice of major for quantitative thinkers with a knack for tech tools and computing. In September 2023, the BLS also ranked data scientists in third place on its list of the 20 fastest-growing occupations in the United States.

Should I pursue an actuarial science degree or a data science degree?

It depends on your interests. Actuarial science might make a better match if you have a particular interest in economics and finance. Data science aligns well with computer science and technical aptitudes. If you want access to a wider variation of job duties, you may want to choose data science. If you prefer a predictable, well-defined role, consider actuarial science.

Page last reviewed March 12, 2024.