Credit Risk Modeling In Python 2021
A complete data science case study: preprocessing, modeling, model validation, and maintenance in Python
What you’ll 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
- No prior experience is required. We will start from the very basics
You’ll need to install Anaconda and Python. We will show you how to do that step by step
Brand new course!!
Hi! Welcome to Credit Risk Modeling in Python. The only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you if you are interested in a data science career. Here’s why:
· The instructor is a proven expert (Ph.D. from the Norwegian Business school, who has taught in world-renowned universities such as HEC, the University of Texas, and the Norwegian Business school).
· The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you
· Everything we cover is up-to-date and relevant in today’s development of Python models for the banking industry
· This is the only online course that shows the complete picture in credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation – PD, LGD, and EAD) including creating a scorecard from scratch.
Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon
· We are not going to work with fake data. The dataset used in this course is an actual real-world example.
You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace
· What is most important – you get to see first-hand how a data science task is solved in the real-world
Most data science courses cover several frameworks but skip the pre-processing and theoretical part. This is like learning how to taste the wine before being able to open a bottle of wine.
We don’t do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ‘’friendliest’’ format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness.
Size: 3.15 GB