Credit Risk Modeling in Python (Udemy.com)

Overall, the course is great. However, it’s not very beginner-friendly for those learning in 2026. The Q&A section is no longer active, and some code snippets are outdated and require manual updates. Additionally, the processed datasets aren't shared; if you struggle with the data preprocessing stage, it becomes difficult to move forward. To sum up, it is a comprehensive course on credit risk. If you are looking to gain this knowledge and are willing to explore and learn independently from scratch, you should definitely join.

This course has extensive coverage of Credit Risk modeling, which was explained in a way where beginners can also understand. The concepts are explained in a detailed way, in such a way one can get a complete understanding of how ECL, EAD, LGD works. I loved the coding part, on how neatly they are explained and executed.

It starts good with well explained concepts and in detail. However, from the later part of the PD the presenter goes a bit faster (or may be it seemed to me like that since there are complex concepts). Overall, it is a good course and I would suggest this course for one who is interested in learning how credit risk modelling happens on the bank's side.

Good introductory course on credit risk modelling. Although I would like to give 5 stars, I am deducting one star for not explaining the underlying concept in calculating the p-values directly in the logistic regression and linear regression scikit-learn models. The author could have at least shared the name of the method used in calculating the p-values.

I've had a fantastic learning experience while working with credit risk modelling data and techniques. I haven't encountered such valuable knowledge on this subject elsewhere. I believe that this newfound expertise will significantly enhance my skills in this area and have a positive impact on my professional career within this field.

Course is what I expected. A mix of Credit Risk fundamental concepts (i.e. Basel II) and modelling using the Basel II approach. On the programming side, it was a good review of pandas and some techniques specially on the data pre-processing side. Time invested learning this is really worth your while.

It was a perfect match. It was a good combination of credit risk theory and python coding. I liked it. It was also tough, not an easy course I must say and a lot to absorb. Would recommend to anyone who wants to polish his/her python skills and/or wants to learn about credit risk analysis

This was one of the toughest course I ever came across. Being a beginner in python, I found it very difficult to comprehend the splitting of data, calculation of p-values and lot of other things. But it was very insightful. A lot of deep delving was involved which is the reason that credit risk modelling is one of the toughest jobs in the market.

I enjoyed the course, it was very well done and presented. My main comment is that I wish the solutions to the homework was provided together with the course materials. It would have saved me some time I spent on dead-ends programming as well as comparing my work with expected results (some conclusions were not intuitive).

A neat introduction into Python for data science and a great course for novice risk managers who target a career in credit risk management. It teaches you - basic skills of handling data in Python - how to clean and preprocess a dataset - how to apply a logistic regression and linear regression towards a preprocessed dataset - how to calculate the basic bank figures (PD, LGD, EAD and EL). In practice, the material and code from this course might be useful if you are building a simple model for a fragmented retail credit portfolio. The instructor does a great job explaning the concepts and has put a lot of effort into the course material.