Credit Risk Modeling in Python
A complete data science case study: preprocessing, modeling, model validation and maintenance in Python
4.59 (7204 reviews)
33 202
students
7 hours
content
Nov 2024
last update
$119.99
regular price
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
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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 Despite minor shortcomings such as outdated code, arbitrary classing methods, and occasional inconsistencies, the overall value is high.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- Outdated code causing issues with the current Pandas libraries, demanding manual troubleshooting and adaptation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
Related Topics
2474716
udemy ID
24/07/2019
course created date
14/08/2019
course indexed date
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