# Credit Risk Modeling in Python

with Nikolay Georgiev
4.9/5
(309)

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.

10 hours 58 lessons
58 High Quality Lessons
10 Hours of Content
Certificate of Achievement

## 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, with the most attention since the recession. At present, it is the only comprehensive credit risk modeling course in Python available online – taking you from preprocessing, through probability of default (PD), loss given default (LGD) and exposure at default (EAD) modeling, all the way to calculating expected loss (EL).

### Topics covered

data analysisProgrammingPythonTheory

### What You'll Learn

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

Student feedback

4.9/5

309 ratings
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18.03.2024
Overall, the course is very thorough and with lots of relevant information. I recommend it. However, it should be updated, especially with regard to the notebooks made available. In my first attempt to the final exam, I got a score of 7.2 (thus, passed it) because I could not solve the questions of the third section due to a problem happening with the LinearRegression classe defined in the provided notebook.
02.09.2022
In the PD model, continuous variables are transformed to discrete intervals which doesn't achieve the same discrimination potential as a pure continuous variable. Also fine/coarse classing requires manual work which is very time consuming for a dataset with many features.
21.07.2022
Exceptional course. Every little step including the data preprocessing is nicely done and explanations as to why that would be done given. Every concept is well explained and you finish it feeling confident to work as a credit risk quant!
09.02.2023
A complete course for modelling credit risk. From the basic definitions to a complete preprocessing and estimation of the probability of default from the clients' data.
07.06.2023
Wow, this course is amazing! I feel extremely benefited from this course, and I now have a better understanding of credit risk modelling.
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“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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