Python Data Science Guide! – Python Data Science Straightforward!
A complete data science case study: preprocessing, modeling, model validation and maintenance in Python
- 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
Hi! Welcome to Python Credit Risk Modeling. A tutorial that teaches you how banks use python data science modeling to improve their performance and comply with regulatory requirements. This is the perfect tutorial for you, if you are interested in a python data science career.
· The tutorial is suitable for beginners. We start with theory, initial data and gradually solve a complete in front of you
· Everything we cover is up-to-date and relevant in today’s development of Python models for the banking industry
· It 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 datasets used in this tutorial is an actual real-world example
· You get to differentiate your python 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 python data science task is solved in the real-world
Most python data science tutorials cover several frameworks, and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine.
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.
- 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
Make sure that you take full advantage of this amazing opportunity!
Who this tutorial is for:
- You should take this tutorial if you are a data science student interested in improving their skills
- You should take this tutorial if you want to specialize in credit risk modeling
- The tutorial is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
- This tutorial is for you if you want a great career
Hi I’m Al Ardosa the Fellow Actuary. I’ve been making tutorials since 2013. I’m here to help you do the same. I’ve majored in Computer Science and do advanced studying methods. My purpose is to make sure you understand every concept in these tutorials. If you get stuck with anything, send me a message, I’m here to help.
I’ve been working as a senior software developer and tech lead in Lazada and other tech companies for many years, and is now taking all that I’ve learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer.