Credit Risk Modeling in Python

Teaching you the programming behind how banks decide who should get a loan. You will learn risk modeling theory and advance your Python modeling skills.

With Nikolay Georgiev

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Course Overview

Credit risk modeling is the place where data science and fintech meet. It is one of the most important activities conducted in a bank and the one with the most attention since the recession. This course is the only comprehensive credit risk modeling course in Python available right now. It shows the complete credit risk modeling picture, from preprocessing, through probability of default (PD), loss given default (LGD) and exposure at default (EAD) modeling, and finally finishing off with calculating expected loss (EL).

58 High Quality Lessons
58 Practical Tasks
6 Hours of Video
Certificate of Achievement

Skills you will gain

data analysisprogrammingpythontheory

What You'll Learn

This course will teach you to understand the main principles behind the bank decision-making systems regarding who and why should get a loan. Enrolling in this course lets you combine your skills in data science and fintech and build a proper credit risk modelling structure.  

Understand the meaning of a credit risk  
Use Anaconda Prompt and Jupyter Notebook 
Generalize data preparation – preprocessing 
Employ a logistic regression 
Apply specific models for decision making 
Calculate expected losses 

Curriculum

“This is the perfect training if you’re into data science and are trying to stand out from the competition with increasingly in-demand skills. I will show you how to combine Python programming with credit risk modeling to create a model in a real-life working environment. Step by step, and exactly how it is performed in the industry.”

Nikolay Georgiev
Director at KBC Group
Credit Risk Modeling in Python

With Nikolay Georgiev

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