Data Science Certificate case studies: Min Liang
Min Liang is a Senior Risk Manager at Aviva Singlife in Singapore where he leads the Group Enterprise Risk Function. He explains how he will be taking the techniques he learnt on the Certificate in Data Science course back to his workplace.
Tell us about your current role
My day-to-day work covers risk assessments on material business decisions, review of new insurance products, and monitoring and reporting on the risks faced by the company. I particularly enjoy analysing the external and internal environments in order to understand the risks faced by the company so that actions can be taken to mitigate them. Data analytics are employed heavily in the process of identifying the risks.
Why did you enrol for the Certificate in Data Science?
I always wanted to explore data science techniques in order to improve the data analytic capability within the team. I’m happy that the IFoA and SDSI have come together to launch this Certificate. It has provided me with an overview of data science. I have found the case studies particularly useful as they demonstrate how data-science techniques can be applied practically in the corporate world.
What is your level of data science experience?
I spent a couple of years working in data management and analysis. One of the assignments given to me and my team was to build a data warehouse to streamline the company-wide risk reporting process. To do this we needed to collect, clean and transform the historical data, import the cleaned and transformed data into the data warehouse, and transform the data into insights for analysis and reporting purposes. I believe the techniques obtained from the Certificate are able to further improve the existing process.
What did you like about the course?
I liked the flexibility provided by the programme. It allowed me to study anytime, anywhere as all study materials can be accessed online. In fact, I did most of my study when I was on my way to work. It also allowed me to learn at my own pace, as long as I completed my study within the duration of the programme.
In addition, I found the feedback given by the tutors particularly useful in helping me to improve the weaknesses in my assignments so that I will not make the same mistakes in my work.
How did you fit the learning into your current work/commitments?
I tried to allocate two hours a day for the programme, including Saturday and Sunday. Normally, I did the study in the early morning before I started my work at 8am.
What did you gain at the end?
I have gained knowledge in the following areas:
- An overview of data science
- Various data-science techniques adopted by practitioners to help solve the (numerical and non-numerical) problems faced by their companies
- Various visualisation techniques used to present the information to target audiences
- Practical application of data-science techniques.
I highly recommend this programme for those who are new to data science or have just started their journey in data science. It’s also suitable for data science experts as it can serve as a refresher course to understand market developments in data science.
How will you use it?
I am going to revisit the existing risk reporting and monitoring process adopted by the team and see what areas we can improve on with the knowledge learnt from the programme. I believe the two major areas that can be improved are the data preparation process for risk reporting, and the risk report presented to management.
What do you want to do next in data science?
Ultimately, I want to improve the data analytic capability within the team. In particular, we need to employ more machine learning and AI techniques in risk analysis in order to provide more comprehensive insights for risk decision making.