Brand new course!!

Hi! Welcome to Credit Risk Modeling in Python. The only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you, if you are interested in a data science career. Here’s why:

· The instructor is a proven expert (PhD from the Norwegian Business school, who has taught in world renowned universities such as HEC, the University of Texas, and the Norwegian Business school).

· The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you

· Everything we cover is up-to-date and relevant in today’s development of Python models for the banking industry

· This is the only online course that shows the complete picture in credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation - PD, LGD, and EAD) including creating a scorecard from scratch

· Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon

· We are not going to work with fake data. The dataset used in this course is an actual real-world example

· You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace

· What is most important – you get to see first-hand how a data science task is solved in the real-world

Most data science courses cover several frameworks, but skip the pre-processing and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine.

We don’t do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ‘’friendliest’’ format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness.

Throughout the course, we will cover several important data science techniques.

- Weight of evidence

- Information value

- Fine classing

- Coarse classing

- Linear regression

- Logistic regression

- Area Under the Curve

- Receiver Operating Characteristic Curve

- Gini Coefficient

- Kolmogorov-Smirnov

- Assessing Population Stability

- Maintaining a model

Along with the video lessons you will receive several valuable resources that will help you learn as much as possible:

· Lectures

· Notebook files

· Homework

· Quiz questions

· Slides

· Downloads

· Access to Q&A where you could reach out and contact the course tutor.

Signing up for the course today could be a great step towards your career in data science. Make sure that you take full advantage of this amazing opportunity!

See you on the inside!