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 - July 2019
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 (PhD 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 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
- 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:
· Notebook files
· Quiz questions
· Access to Q&A where you could reach out and contact the course tutor.
Signing up for the course today could be a great step towards your career in data science. Make sure that you take full advantage of this amazing opportunity!
See you on the inside! Who this course is for:
You should take this course if you are a data science student interested in improving their skills You should take this course if you want to specialize in credit risk modeling The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills This course is for you if you want a great career Screenshots