Credit Risk Modeling in Python

A complete data science case study: preprocessing, modeling, model validation and maintenance in Python

4.56 (7251 reviews)

Udemy

platform

English

language

Data & Analytics

What you will learn

Improve your Python modeling skills

Differentiate your data science portfolio with a hot topic

Fill up your resume with in demand data science skills

Build a complete credit risk model in Python

Impress interviewers by showing practical knowledge

How to preprocess real data in Python

Learn credit risk modeling theory

Apply state of the art data science techniques

Solve a real-life data science task

Be able to evaluate the effectiveness of your model

Perform linear and logistic regressions in Python

Course Gallery

Credit Risk Modeling in Python – Screenshot 1 — Screenshot 1 – Credit Risk Modeling in Python

Credit Risk Modeling in Python – Screenshot 2 — Screenshot 2 – Credit Risk Modeling in Python

Credit Risk Modeling in Python – Screenshot 3 — Screenshot 3 – Credit Risk Modeling in Python

Credit Risk Modeling in Python – Screenshot 4 — Screenshot 4 – Credit Risk Modeling in Python

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Comidoc Review

Our Verdict

Credit Risk Modeling in Python offers extensive coverage and detailed explanation of essential concepts, making it an excellent course for anyone interested in this field. Despite minor shortcomings such as outdated code, arbitrary classing methods, and occasional inconsistencies, the overall value is high. The engaging instructor offers a wealth of knowledge and expertise that can significantly enhance your credit risk modeling skills and boost your professional career.

What We Liked

Comprehensive coverage of credit risk modeling, suitable for both beginners and experienced professionals
In-depth explanation of key concepts like PD, LGD, and EAD, aided by practical examples
Enthusiastic and clear instructor, providing valuable insights into the field of credit risk modeling
Neatly explained coding part using Python, which can significantly enhance your skills in this area

Potential Drawbacks

Outdated code causing issues with the current Pandas libraries, demanding manual troubleshooting and adaptation
Arbitrary cluster selection for dummy variables and subjective heuristics used for fine/coarse classing in Weight of Evidence part
Lack of in-depth explanation and motivation for certain methods, which might frighten experienced statistical modelers
Some default mark values in the course may not align with conventional practices (1 for bad borrowers instead of 0 for good borrowers)
Minor inconsistencies in data names that can cause confusion during the learning process