Python

Credit Risk Modeling in Python 2020 Course

Credit Risk Modeling in Python 2020 Course – Python Best Courses

A complete data science case study: preprocessing, modeling, model validation, and maintenance in Python

What you’ll learn

Credit Risk Modeling in Python 2020 Course – Python Best Courses

  • 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

Requirements

  • 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

Description

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. 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

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.

Throughout the course, we will cover several important data science techniques.

– Weight of evidence

– Information value

– Fine classing

– Coarse classing

– Linear regression

– Logistic regression

– Area Under the Curve

– Receiver Operating Characteristic Curve

– Gini Coefficient

– Kolmogorov-Smirnov

– Assessing Population Stability

– Maintaining a model

Along with the video lessons you will receive several valuable resources that will help you learn as much as possible:

Lectures

Notebook files

Homework

Quiz questions

Slides

Downloads

Access to Q&A where you could reach out and contact the course tutor.

Who this course is for:

  • You should take this course if you want to specialize in credit risk modeling
  • The course is also ideal for beginners, as it starts with the fundamentals and gradually builds up your skills
  • This course is for you if you want a great career

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