Choosing Between Data Science and Actuarial Science

Considering a career in data science or actuarial science? Read on to discover what these fields have in common, how they differ, and which profession is right for you. is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.

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Data science and actuarial science share many similarities. Both careers involve collecting and analyzing data to gain useful insights, then using that knowledge to make decisions. These professionals combine statistics, mathematics, and computer science. However, actuarial science emphasizes finance, while data science uses pure data processing.

The Bureau of Labor Statistics (BLS) projects data science positions to grow by 31% and actuary jobs by 24% from 2020-30, much faster than the average for all occupations.

Data science and actuarial science feature promising projected employment growth. The Bureau of Labor Statistics (BLS) projects data science positions to grow by 31% and actuary jobs by 24% from 2020-30, much faster than the average for all occupations.

Students may have difficulty choosing between these two in-demand fields. Our guide describes both career paths and examines the pros and cons of each job.

What Is a Data Scientist?

IBM describes data science as an interdisciplinary field focused on drawing valuable insights from large amounts of data. Data science relies on the scientific method, statistics, algorithms, and artificial intelligence. Many companies hire data scientists to analyze business operations, help make decisions, and suggest new policies.

Strong communication skills help data scientists summarize and explain their findings. These professionals often prepare reports and presentations to showcase data, sometimes incorporating charts and graphs.

Data scientists often use advanced software to process data and predict outcomes. As a result, becoming a data scientist requires computer programming skills and other technical knowledge. Data scientists also create or modify algorithms to predict data more accurately.

Common Career Paths for Data Scientists

Graduates can work as data scientists, data managers, analysts, and other positions. Data scientists with a biology background may also find specialized roles as bioinformatic technicians.

According to the BLS, data scientists and other mathematical science professionals earned a median annual salary of $98,230 as of 2020.

The BLS projects a 31% job growth rate for mathematical science occupations, including data science, from 2020-2030. By comparison, the bureau projects an average growth for all occupations of 8% in the same period. Most entry-level jobs in data science require a bachelor's degree or higher.

According to the BLS, data scientists and other mathematical science professionals earned a median annual salary of $98,230 as of 2020. Managers in the field can earn higher wages. For example, PayScale reports an average base salary of $155,630 for data science directors as of August 2021.

What Is an Actuary?

Actuaries, or actuarial scientists, use statistics and mathematics to predict financial risks for companies. They collect data and use algorithms to understand the likelihood of financial return or loss. Actuaries may work in teams alongside accounting and finance departments. Most professionals work in an office setting, but may need to travel to meet with clients.

Insurance and pension companies use actuaries to balance financial returns with the uncertainty of future events. The BLS notes that 76% of actuaries worked in the finance and insurance industries as of 2020.

Some actuaries work as consultants, helping companies make business decisions. They can also help businesses determine the cost of insurance or retirement benefits. The emerging field of actuarial data science applies machine learning techniques to traditional actuarial science topics.

Common Career Paths for Actuaries

Actuaries can work in any industry that involves risk modeling, including energy and the environment. Actuaries working in insurance tend to focus on health, life, or property insurance. Other specialty areas include pension and retirement planning or business risk consulting.

The BLS projects an 24% increase in actuary positions from 2020-2030, much faster than the average projected growth for all occupations. As of 2020, actuaries earned a median annual salary of $111,030.

Most entry-level positions in the field require at least a bachelor's degree. Many actuaries earn an actuarial science degree, but some pursue other analytical majors, such as mathematics or statistics.

After several years of experience, actuaries may qualify for higher-paying managerial roles. According to PayScale, actuarial managers earned an average annual salary of $123,560 as of March 2021.

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Similarities Between Data Scientists and Actuaries

As interdisciplinary fields, data science and actuarial science share many commonalities. Data scientists and actuaries study some of the same topics because they both focus on processing and analyzing data. These shared features can make choosing whether to become an actuary vs. a data scientist difficult.

Data scientists and actuaries both:

  • Need a bachelor's degree for most entry-level positions.
  • Can access advanced educational opportunities such as graduate school and professional certifications.
  • Combine aspects of statistics, mathematics, and computer science.
  • Help businesses improve their operations and finances.
  • Use data processing programs to help make predictions and find patterns.
  • Enjoy positive job projections and high demand.
  • Earn lucrative salaries.
  • Can advance to high-paying managerial positions.
  • Usually work in an office environment.
  • Work with a team.
  • Present data and results through reports and visual representations.
  • Communicate their findings to others outside their field such as executives and shareholders.

Differences Between Data Scientists and Actuaries

The differences between data scientists and actuaries might be subtle, but those small distinctions can add up. In the list below, we discuss some reasons why actuarial science differs from data science. Although responsibilities for each job can vary by employer, this list serves as a starting point to distinguish the two fields.


  • Rely on their knowledge of business, economics, and law.
  • May travel to meet clients when working as a consultant.
  • Testify in court as witnesses or provide evidence regarding insurance claims.
  • Need working knowledge of laws and government regulations.
  • Help determine company benefits and insurance rates.
  • Take part in insurance contract negotiations.
  • Must usually pass a series of additional exams and complete post-college training to earn recognition as certified professionals.

Data scientists:

  • Rely on metadata and computer science knowledge.
  • May develop or modify software.
  • Take part in all stages of data handling, including collection, processing, organization, and analysis.

Should You Become an Actuary or a Data Scientist?

Since the two professions seem very similar, choosing between them can prove difficult. A key determining factor is whether you want to focus more on computer science or finance. While actuaries earned a higher median annual income than data scientists as of 2020, both careers offer competitive salaries.

Actuaries usually need to pass several exams and earn professional certification. Some certifications for actuaries require at least six exams and other courses in addition to a college education. Employers often support entry-level actuaries through the certification process. Certifications can also help data scientists advance their careers.

Program availability may also impact your decision. According to the National Center for Education Statistics, 124 postsecondary institutions offer actuarial science degrees at the bachelor's level or higher. Students may need to pursue related degrees in mathematics or statistics instead. By comparison, over 370 schools provide bachelor's and graduate programs in data and information science.

Data Science vs. Actuarial Science: Pros and Cons

Data Science

  • Greater projected growth (31%) from 2020-30, according to the BLS.
  • A versatile and rapidly growing field that can lead to jobs in diverse industries.
  • Work with or develop cutting-edge technology including artificial intelligence and machine learning.
  • Ample opportunities and choices between dedicated degrees in data science.
  • Rapid developments in the field require ongoing self-education.
  • May require studying other topics related to a desired position or field.
  • Work can be challenging with a total of 59 zettabytes (ZB) of data in the world as of 2020 and 175ZB projected by 2025.
  • The tech industry suffers from low employee tenure rates.

Actuarial Science

  • Can lead to higher-paying careers according to BLS median annual salary figures from 2020.
  • A well-established field that adapts new methods and technology to current issues.
  • Offers focused career paths with a clearly defined list of steps to take.
  • Consistently ranked as a top occupation due to benefits such as job security and opportunity for growth.
  • Requires additional certifications involving a series of exams and qualifying steps to take the exams.
  • Most available positions are limited to the insurance and finance industries.
  • Fewer choices of dedicated actuarial science programs compared to other degrees.
  • Might not have the chance to develop cutting-edge technology.

Source: BLS

How to Become a Data Scientist

Students interested in launching a data science career can start by pursuing a bachelor's degree. Learners can begin with general computer science programs, but they may need further studies in data science. Continuing your education at a graduate data science program can open up more work opportunities.

Bachelor's degree-holders can also opt for a data science bootcamp. These intensive career-preparatory programs can last anywhere from several weeks to several months. A data science bootcamp can round out a CV and teach necessary career skills. Students can also learn new programming languages or tools to match their desired positions.

The professional organizations below help new and experienced data scientists find resources and education in the field.

Professional Organizations for Data Scientists

A global organization, ADaSci administers the chartered data scientist professional credential. Along with networking benefits and free access to the group's publications, ADaSci members enjoy discounts on exams and conferences.

DAA embraces a mission to advance the digital world through community. The organization's members can access resources such as the Digital Analytics Cookbook, which details industry best practices.

This nonprofit group promotes improvement in the field through supporting members, encouraging diversity, and promoting ethical methods in data science. DSA maintains a resource library and offers educational opportunities like workshops.

SIGKDD promotes the science of data mining while offering a platform to discuss the field. Its members enjoy benefits like conference discounts, a subscription to the organization's journal, and a chance to win awards in the field.

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How to Become an Actuary

Becoming a professional certified actuary requires a series of exams on top of earning a bachelor's degree. Two dedicated actuary organizations offer the main professional certification exams: the Casualty Actuarial Society (CAS) and the Society of Actuaries (SOA).

In addition to exams from either organization, candidates must pass the prerequisite validation by education experience (VEE) process. The VEE requires aspiring actuaries to pass at least two exams and demonstrate completion of approved courses in finance, economics, and mathematics. Students can apply college courses toward VEE credits and finish some of the prerequisites in school.

After completing the VEE requirements, candidates must take three additional courses and pass a total of six exams to earn the associate credential from CAS. Alternatively, students take two courses and pass seven exams (or six exams and a project) to earn a similar SOA certification.

SOA and CAS certifications mark the most common entry point in the field.

Professional Organizations for Actuarial Scientists

CAS is one of the main organizations offering professional credentials in the field. The society specializes in property and casualty risks. CAS provides seminars, workshops, and platforms for over 9,000 members worldwide.

SOA also offers professional certifications for actuaries. With over 30,000 members, the society ranks as the largest professional actuary organization in the world. SOA publishes journals, magazines, and newsletters.

Established in 1895, the IAA promotes the profession globally. The organization features sections dedicated to different specializations such as consulting, life insurance, and financial risks.

Based out of Washington, D.C., this organization has over 19,500 members and hosts continuing education opportunities. The Academy provides actuarial advice to public policymakers and regularly publishes field-related news.

Frequently Asked Questions

Is data science more difficult to study than actuarial science?

Students of both subjects must tackle complicated topics and stay on top of current practices. However, becoming a professional actuary requires a series of exams in addition to a college education.

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

Actuaries sometimes provide legal testimonies and give advice concerning pensions or insurance. Actuaries also help set insurance prices, negotiate contracts, and manage retirement benefits.

Is actuarial science a good major?

Actuarial science can be a great major if you enjoy mathematics, statistics, and finance. Degrees in actuarial science may be less common than other major programs, but careers can prove lucrative.

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

Which degree you should pursue depends on your personal preferences and career goals. Actuarial science focuses more on finance, while data science involves coding and tech development.

Featured Image: Evgeniia Siiankovskaia / Moment / Getty Images

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