Overview of credit risk, its importance in finance, and how Python is used in modeling and analysis.

Configure your Python environment with necessary libraries and tools for credit risk modeling projects.

Understand the structure and characteristics of real-world credit risk datasets used for modeling.

Learn essential data cleaning, transformation, and preparation techniques specific to financial datasets.

Prepare data specifically for Probability of Default (PD) model development, handling missing values and feature engineering.

Implement and estimate various PD models using Python, understanding statistical techniques and model selection.

Validate your PD models using appropriate statistical tests and metrics to ensure their accuracy and robustness.

Learn how to integrate PD models into decision-making processes for credit assessment and risk management.

Understand strategies and techniques for continuously monitoring PD model performance over time to detect degradation.

Explore Loss Given Default (LGD) and Exposure At Default (EAD) models, crucial components in comprehensive credit risk assessment.

Deep dive into building and validating LGD models to quantify the loss expected if a default occurs.

Learn to develop EAD models to estimate the outstanding exposure at the time of default.

Integrate PD, LGD, and EAD models to calculate the Expected Loss (EL), a key metric in credit risk management.