Basic difference and similarity in actuary and data scientist

The post is written by Niharika Thareja on our forums and was a part of weekly Act! We appreciate her for pulling out such a useful information and hope it will remove the confusion as to how Actuaries and Data Scientists have differences.

What is data scientist? If go by the name, data scientist would be who plays and experiments with data. Now we put up the explanation in the following points:

· Part mathematicians, part computer scientist and part trend spotter.

· Collects enormous amount of messy data, points and use their formidable skills in mathematics, statistics and programming to clean and put into good use.

· Explore and examine data from variety of angles to determine hidden weakness, trends and opportunities.

·Belongs to business world and IT world and can say who digs and unearth the business insights that no one thought to look before.

· Solve business related problems by using technics- Python, R, etc. and turns high tech ideas to turn out to profit.

· Possess good communication skills as well to acquire the data and explain his team or co-workers or to responded what he’s up to.

This may sum to:

“Data Scientist = deep learning + pattern recognisation + data preparation + analysis + data utilization”

As per the article in magazine of IFoA: “A data scientist exists in the intersection of three skills sets: coding/programming, mathematics and domain knowledge. Coding allows them to manipulate data and create algorithms. Mathematics and statistics allow them to use data to predict future outcomes. Then data scientist need to understand people and business rules to solve practical business problems. People with all three of these skills are rare and valuable.”

In brief we can say, to become a data scientist , from technical view – a individual should have knowledge of mathematics, statistics, machine learning, software engineering, data cleaning, python, R, SAS, SQL, C/C++, JAVA, Big data platforms- Hadoop, and as from business view – analytical problem solving capabilities, effective communication, industry knowledge and curiosity to solve.

Now we compare it to ACTUARY. For that, a brief explanation regarding this can be found here –


· DATA– Actuary works on insurance data mainly whereas data scientist work effectively with instructed data.

·TECHNICS– Actuary uses predeveloped system whereas data scientist need to develop new algorithms as per data variability.

·FOCUS– Actuary primary focus on financial risk and issues and data scientist focuses on understanding implication across all business units.

· Actuary predicts loss, estimate price and reserving whereas data scientist have wide problems and need to find every answer.

While they both revolve around data, there are certain lines drawn between these two. For instance, actuaries are found primarily in the insurance industry and risk management. On the other hand data scientist can be found in virtually any industry. If we have a comparative lookout of actuarial science and data science, prior is about the study of finance and related fields and activities and latter is about studying different data sets, their relationship and analysis.

Thus we can say there is a gap between the working of an actuary and data scientist. But the worry is that the gap is negatively affecting the employment prospects of actuaries. Actuarial employees are increasingly expecting their staff to have the same skillset as data scientist. In recent survey of actuarial employers by Singapore Actuarial Society gave the report that almost half expected new actuaries to write codes, manipulate data and use statistical software.

Let us know what you think of Actuaries and data scientists. Do you think it is a mismatch or a match? You can use forums to express your views!

Niharika Threja
Niharika has Graduated in B.Com (H) from DU and cleared 3 exams CT1 CT3 and CT5. She have good command over Ms Word, Excel(Advance) and PowerPoint and in languages both Hindi and English. She is able to handle Number, Maths, Estimations etc.

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