Master Credit Risk Modeling in Python: A Data Science Case Study
What you will learn:
- Master Python for credit risk modeling
- Build a complete credit risk model from scratch
- Gain a deep understanding of credit risk theory
- Learn advanced data preprocessing techniques
- Apply state-of-the-art credit risk modeling algorithms
- Develop models compliant with Basel regulations
- Evaluate model effectiveness and performance
- Master linear and logistic regression in Python
- Gain a competitive edge in the job market
- Solve real-world data science problems
Description
Unlock the secrets of credit risk modeling with our comprehensive Python course. Dive deep into a real-world case study, covering everything from data preprocessing and modeling to model validation and maintenance. This course is your gateway to a rewarding career in data science.
Led by a seasoned expert with a PhD from the Norwegian Business School and experience teaching at top institutions like HEC and UT Austin, you'll gain practical knowledge and valuable skills highly sought after by employers.
This course goes beyond the basics, providing you with:
- A comprehensive understanding of credit risk modeling theory.
- Hands-on experience with real-world data and techniques used by financial institutions.
- The ability to build and evaluate robust models compliant with Basel II and Basel III regulations.
- A strong foundation in data preprocessing and model building techniques like linear and logistic regression, weight of evidence, information value, and more.
- A differentiated portfolio showcasing your expertise in credit risk modeling.
Beyond video lessons, you'll receive valuable resources including lecture notes, notebook files, homework exercises, quizzes, slides, downloads, and access to a dedicated Q&A forum for support.
Don't just learn about credit risk modeling – master it. Enroll today and take your data science career to the next level!