From a non-specialist’s point of view, distinguishing between an actuary and a data scientist can be difficult. Both rely on in-depth data processing. The results that both generate are used to better understand the present and predict future circumstances. Even given their commonalities, these two professions exist as distinct specialties, each with their own career paths and requirements.
What Does an Actuary Do?
Actuaries are common in any field that relies heavily on risk management. An actuary uses statistics and mathematics to determine the risk level of a particular prospect or circumstance. Assessing risk is an integral part of many industries, from insurance to financial speculation. An actuary’s job is based on the premise that, given enough data, the right calculations will yield actionable insights that businesses can take to mitigate risk.
Actuaries are sometimes referred to as “the first data scientists.” Before computers and algorithmic programming, actuaries spent hours poring over pages to find patterns in the figures that made up their data. Today, actuaries rely on statistical processing software to determine patterns.
Actuaries’ Essential Skills
Actuaries rely on certain skills to draw accurate conclusions from data.
- Problem-Solving: Risk management is a complex field, and the data sets are never the same. While this makes each job unique, it requires a high degree of problem-solving. Using external knowledge to extract relevant insights is a critical skill.
- Computing Knowledge: Actuaries must have strong computer skills, both to understand how software processes data and to share results across different platforms.
- Mathematics and Statistics: To effectively analyze data, an actuary must be familiar with statistics, probability, calculus, and accounting, as well as recognizing and interpreting trends within a data set.
The Present and Future of the Profession
The U.S. Bureau of Labor Statistics (BLS) reports the median annual salary of actuaries in 2018 was $102,880. The outlook for jobs is strong, with the BLS projecting growth of 20% between 2018 and 2028. This is much higher than the average growth forecast (5%) for all other jobs. Before beginning their careers, actuaries require certification from organizations such as the ASA (Associate of the Society of Actuaries), FSA (Fellow of the Society of Actuaries), or CERA (Chartered Enterprise Risk Analyst).
What Does a Data Scientist Do?
Like actuaries, data scientists spend their time analyzing data and making statistical predictions. However, unlike actuaries, who work primarily in insurance and deal chiefly with numbers, data scientists use a wide range of data to make predictions about a broader range of topics.
The information that data scientists work with isn’t just numerical. They use abstract collections of data to generate solutions to a variety of complex problems. Because their work and techniques are applicable to delivering a wide range of insight, data scientists can be found in almost any industry, from tech to healthcare.
Necessary Skills for Data Scientists
Data scientists must have a set of specialized skills.
- Critical Thinking: Thinking critically about a problem helps data scientists formulate hypotheses and narrow down data sets to offer accurate insights.
- Programming: While generalized computer skills still form part of a data scientist’s qualifications, they need to know far more about computers than other professions. Learning the syntax and forms for languages such as R, Python, and C# offers data scientists the ability to automate data processing. With the sheer size of the data pools these scientists deal with, programming is a core skill.
- Mathematics: Data scientists should have a strong background in linear algebra, calculus, and statistics. Many companies require data scientists to have an advanced degree in mathematics or a related field.
Job Outlook for Data Science
According to the BLS, computer and information research scientists made a median annual salary of $118,370 in 2018, with the top 10% of earners making $183,820. The BLS predicts jobs in the field will grow 16% between 2018 and 2028.
Comparing Actuary vs. Data Scientist
Actuaries and data scientists differ in several ways. A data scientist’s scope is often broader than that of an actuary, while actuaries are more familiar with the nuances of statistical probability than data scientists. Data scientists often develop programs to help them process data, while actuaries use existing programs to help them determine risk. Finally, actuaries deal primarily with financial risk, while data science can be applied to any field that relies on large amounts of data.
Quality education is essential whether a student is interested in a career in data or actuarial science. At Maryville University, students can choose between two degree options to prepare for these paths. The Online Bachelor of Science in Mathematics program teaches students fundamentals including calculus and number theory, and offers a minor in data science with courses in coding and machine learning. The Online Bachelor of Science in Data Science focuses on mathematical principles like statistics and probability, as well as math modeling and machine learning skills, and offers a concentration in actuarial science that provides students with the skills they need for a rewarding career as an actuary.
Learn more about how Maryville University can help you prepare for a career in the digital age today.
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