I studied Electrical Electronic Engineering, I’m from an engineering background, Analog and Digital Integrated Circuits, which is also a subset of Electrical Electronic Engineering.
I did my PhD research: I worked on computational finance topics, optimizing accuracy and acceleration of numerical methods for options pricing.
My current role now is in *****. I work in the global risk analytics team.
What I do is create models to forecast expected credit losses for key portfolios within *****.
I joined the ARPM course in February last year, 2021. It's been a very useful course within my team and within *****.
What I really like about the course is the bottom-up theoretical coverage of the topic from the first principles.
Any models you've heard about that you haven't actually might not have used within your own team, you have an opportunity to learn how these models are created right from scratch, for example, the risk identification, what are the variables that fit into this model depending on what model you're looking at.
The video lectures are very good. You have those at your fingertips 24/7.
You can watch the videos at any time at your own pace.
That's something I really like about the course.
I'm sure I've watched some videos several times, not just once.
When you have an opening topic, you can go back and watch the videos as you look through the theory and connect the two, and it's very good.
I've really found that some of the theoretical coverage, once you go through the video, everything just falls in place and it's really clear.
Another thing I like about this course is that it really covers a very broad range of quantitative skills, both on the buy side, sell-side, even insurance side, you can really gain from the modeling approach using Data Science and Quant finance.
It covers very broad topics, if you have any misgivings on what quant finance is, what do people do in credit, what do people do in loss forecasting, and things like that, you get full coverage, this is what I've seen here.
I have seen topics and models I have covered before on my side and topics of models I work with now. I think that's a very good thing.
Another thing I really like about this course is the Python code base.
For every theoretical aspect you cover, there is a corresponding code base to go with the definition.
You don't just learn the theory, you know how to implement it, you have exact specifications of how you implement this model.
Packages within Python call and code functions are already prewritten for you as well.