Data Science Certificate case studies: Paul Wee
Paul Wee is a Corporate Actuary for a reinsurance firm based in Singapore. Here he explains what motivated him to do the Certificate in Data Science.
Knowing the untapped powers of data science, I always wanted to learn about it as a new toolkit, to improve how we work with data intensive processes and Excel files. As digitalisation of the insurance industry evolves, and big data becomes a reality, use of data science in actuarial work is becoming a necessity.
When the course was announced, I was keen to learn more and managed to get enrolled on the first programme. At that stage, I only had a basic knowledge of data science. I had attended short training sessions within my company and with the Singapore Actuarial Society, and completed introductory online courses in R, but not Python.
The structure of the course was well paced, as the syllabus was spread out across six modules and three assignments, with plenty of interesting technical material and case studies. There was also background material on Python and other software, as well as relevant links. There was good interaction with the course instructors through the allocated one-to-one review and the scheduled webinar sessions, prior to an upcoming assignment.
I found the coverage of the syllabus relevant; it included topics on data analysis, data visualisation, and how to assess and introduce artificial intelligence into an organisation, with specific sections and case studies from the IFoA Education Team discussing data science from an actuarial angle.
The assignments allowed you to use your preferred tool, programming language and approach, and would not penalise a beginner new to R or Python, as was my case.
By the end of the course, I had gained a broad understanding and appreciation of data science and how actuaries can use it across analytics and data visualisation, as well as how to harness and implement artificial intelligence, together with the regulatory and ethical considerations.
I also have a fresh interest in learning more R and Python, and even an interest in doing some data science or actuarial transformation work. I have gained more clarity on the background behind the technical and complex data science bits, and an appreciation of what data scientists do.
I plan to follow up on some of the background material, for example on neural network reserving, and other areas of research.
It also adds a new paradigm in any transformation efforts in my organisation, as we can use an understanding of data science to improve how our actuarial processes are run and reported, for example to streamline and automate things systematically faster and even going further.
I hope to continue to learn and improve on my R and Python skills, understand more about artificial intelligence and its uses, meet more data scientists, and do more research on the applications of data science in actuarial science.