Career in Actuarial Science VS Data Science

The second Best Business Job of 2018 v/s the Sexiest job of the 21st century.

Actuarial Science and Data Science are two booming ‘Sciences’ which convert data to dollars. Actuary is a financial manager who uses statistical techniques to assess, manage and mitigate the risks (What is a risk- an actuarial definitions) involved in financial investments, insurance policies and other risky ventures. Data Scientists take huge amounts of structured and unstructured data points and use their skills in mathematics, statistics and programming to clean, massage and organize them.

Even though they both use statistical techniques and software knowledge to analyze data and report conclusions, they differ in their core foundation, thus, there are certain lines drawn between these two.

Qualification
The course is offered by several actuarial societies around the globe such as the Institute and Faculty of Actuaries (UK), Society of Actuaries (USA) to name a few. Qualifying as an Actuary calls for passing several rigorous actuarial examinations, numbers differing by the Institute. A career in Actuarial Science demands studying along with job, till you qualify, which is not in the case of Data Science.
A formal degree in Statistics along with programming skills in R, Python is a good headstart to a career in Data Science, however the path from a junior Data Scientist to a ‘Data Scientist’ requires immense domain knowledge which comes with experience.

Techniques and Tools used

While actuaries and data scientists effectively predict future outcomes, the methodology by which they accomplish this feat is different. Techniques such as asset adequacy analysis, basic chain and ladder method, profit testing are more specific to the actuarial profession whereas Data mining and Machine Learning are the bread and butter of any data science professional.
Actuaries generally use SAS, Excel, VBA, and SQL on a frequent basis and also have additional knowledge of domain specific software such as ResQ, which is used in General Insurance for Reserving and Prophet, which is used in Life Insurance for Actuarial Modelling. A data scientist is considered to be more programming savvy, owing to greater requirement of programming in day to day work and generally has solid a command over C++, R, Python, Hadoop to name a few.

Career Prospects

Career prospects are ever increasing in both the fields. The prospects of actuarial science are majorly in the Insurance sector, followed by consultancies. Though there is saturation at entry level actuarial jobs in few countries such as India (Companies hiring actuarial freshers in India), there is a shortage of professionals at the higher levels. Data Scientist roles traverse every industry ranging from the traditional research sector to marketing, retail, finance, manufacturing, IT.
The starting salary of a data scientist is usually higher than that of a student actuary, which increases significantly with experience and exam passes. In the long run, both the professions pay well.

Challenges

Actuarial Science and Data Science have an impeding problem of demand and supply of respective professionals. There are considerable number of student actuaries, but owing to difficult exams and the length and breadth of the course, the requirement at senior level exceeds the count of fully qualified actuaries. Data Science being a multidisciplinary field, demanding the professional to have varied skills, the no. of data scientists available is very less in comparison to the amount of data being produced.

Actuaries and Data Scientists are just so similar yet so different. Be it Actuarial Science or Data Science, diving deep into data to derive valuable insights is the rapidly spreading buzzword nowadays! For further studies, latest updates or interview tips on data science and actuarial, subscribe to our emails.

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