A complete data science case study: preprocessing, modeling, model validation and maintenance in Python
Instructor365 Careers
The curriculum covers the essential pillars of expected loss: Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
Instruction includes the Basel II regulatory environment, covering SA, F-IRB, and A-IRB approaches.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This instructional slide introduces initial data preprocessing concepts, specifically highlighting weight of evidence and information value as key topics.
An instructor explains statistical modeling concepts, specifically logistic and linear regression, using hand gestures to emphasize key points in this educational presentation.
The instructor introduces key model validation metrics, specifically highlighting Area Under the Curve and Gini Coefficient as essential concepts for evaluating credit risk models.
This slide introduces the course instructor, displaying the title 'The author' alongside the official logo for the Norwegian Business School to verify his professional affiliation.
Learner signals suggest some Python code is outdated or uses deprecated syntax, requiring manual updates to function correctly.
One sampled review suggests that the pace can feel rapid, with some learners finding it necessary to watch videos multiple times to grasp the content.
The curriculum aligns well with the stated goal of teaching credit risk modeling to beginners and students.