Data Science Certificate case studies: Rakesh Khandelwal
Rakesh Khandelwal is a Senior Manager at Allianz Reinsurance Branch Asia Pacific in Singapore. He explains how the Certificate in Data Science has enhanced his knowledge of machine learning and artificial intelligence.
What do you do in your current role?
I am part of the Corporate Actuarial Team managing the reserving of life and health business. I am also working on the transition of IFRS4 reserving processes to IFRS17 basis.
Why did you enrol for the Certificate in Data Science?
I wanted to gain an understanding of machine learning and some knowledge of programming languages, such as R and Python. The aim was to have some exposure to data science to understand what it encompasses.
What is your level of data science experience?
I have very limited data science experience. We still rely on traditional IT tools like MS-Excel, MS-Access, SAS etc for day-to-day work. I do use R in some processes.
What did you like about the programme?
The course is delivered through an online portal, which is very easy to navigate and use. The programme is well structured to meet the needs of actuaries to learn about data science and machine learning. The objective is not to become a data science analyst but to equip you with sufficient knowledge to understand the techniques and languages, as well as background information about machine learning and artificial intelligence.
I liked the flexibility of being able to access the programme based on availability. The course has a number of case studies to practise, as well learning about the use of data science and machine learning in real-life work.
There are threads to each module to enable the participants to comment or post their views. It acts as a discussion forum to ask questions and share your views.
How did you fit the learning into your current work and commitments?
It’s a ten-week online programme with a commitment of around 8-10 hours every week. I spent at least an hour each working day on the modules and 4-5 hours at weekends.
The course involves three assignments at the end of week three, seven and 10. The assignment covers the topics from the modules and made it more applicable to real-life work.
What did you gain at the end?
I got to know about some of the interesting tools like OpenRefine, Tableau, Keyword analyzer. These tools are publicly available free tools and can assist a lot in our data cleaning, chart creation etc.
I didn’t learn how to code using R or Python but the portal has case studies and guidance to practise and learn the programming languages, which I planned to attempt after the end of the programme.
I understand the difference between the types of machine learning techniques and the pros and cons of using each technique. I am much more aware now about data science and its use in artificial intelligence and machine learning. This will help me to have a constructive dialogue with the data scientists who may be assigned to automate any processes in our company.
How will you use it?
I will try to automate some of the actuarial processes that we do on a routine basis.
What do you want to do next in data science?
Since I have access to the online modules for six months after the end of course, I plan to go through some of the modules again and undertake some of the case studies by myself. This gives me time to do revision practice even after the course has finished.