Data Science is no longer just a buzzword and organisations across the board have made it a part of their day-to-day operations. However, there is one more domain that has a significant connection with data science — actuarial sciences. It is also one of the important aspects businesses need to consider in order to avoid risks.
Actuary: A Fortune Teller & An Advisor
An actuary is a pillar of financial security, has extensive mathematical knowledge and skills. An actuary measures the probability and risk of future events and also, predicts the financial impact on a business and their clients.
One of the primary things that actuaries need to have is a deep mathematical base. They must know the nuts and bolts of mathematics, statistics, and financial theory.
There was a time where actuarial science was only about working in banks and companies with a finance background. Today, numerous public and private companies from different industries are making the best use of actuarial science. While many companies are hiring a single actuary to take care of their risk assessment, there are companies that are setting up big teams of actuaries.
Pivoting From Actuary To Data Scientist
Can actuaries make a transition to data scientist job roles? Even on Quora, this is a burning topic. Data Science has become the trendiest career path and because of that, we see several sub-domain emerging and even the skills required to become a data science professionals are significantly more.
Given their statistics and Math background, actuaries have an edge over their data science counterparts, especially when it comes to making the pivot to data scientist role. While programming is definitely a must-have, mathematics is crucial to data science. Therefore, it is very simple for an actuary to segue into data science.
How Actuaries Can Become a Data Scientist?
Data is something that both actuaries and data scientists crave — the more data these professionals get, the better they do well at their jobs. However, more than actuarial science, data science is invading industries which include the insurance domain as well. And as data science continues to become bigger and better, actuaries are getting intrigued to explore more aspects using their skills and not dwell in the insurance industry.
Data science provides you with a massive playground to test and make the best out of your skills.
3 Ways To Move From Actuarial Science To Data Science
1) Learn About The Data Science Role
Being an actuary, you might be having almost the same set of skills and knowledge when it comes to solving problems and predicting the uncertainty. However, that doesn’t mean the way of work would also be the same. Starting from tools, the entire process might differ.
The best way to understand the job of a data scientist is by learning from someone who is already working in the domain. If you are working for an organisation that also has a data science department, you can reach out to the department and learn from your associates.
2) Build Familiarity With Programming & Tools
In data science, programming and the right set of tools play a major role. So, when you are planning to make a transition make sure you learn the much-needed programming language such as Java, Perl, C/C++, Python, R, SQL, etc.
Talking about tools, experience with tools such as SAS, Apache Spark, MATLAB, Microsoft Excel, ggplot2, Tableau, Jupyter etc. adds an extra flavour to your portfolio.
3) Start Working On Big Data Projects
Working on a Big Data project would definitely give you a heads-up. Even though you are working as an actuary, try working on a big data project or be a part of a team that is working on one. It would be great if you do a project that is beneficial for the organisation as well, as you would be able to understand the nuts and bolts in real-time.
Pick a topic (something that you are curious or passionate about) and get your hands dirty — not only with exploring, cleaning, and graphing but also with coding.
It is completely okay even if you fail or the outcomes are completely different than expected. Just make sure when you present your results, you present it in a way that everyone in the room understands — make the best use of data visualization.
