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
Requirements
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
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. 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-worldMost 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- Kolmogorov-Smirnov- Assessing Population Stability- Maintaining a modelAlong 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.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!
Overview
Section 1: Introduction
Lecture 1 What does the course cover
Lecture 2 What is credit risk and why is it important?
Lecture 3 Expected loss (EL) and its components: PD, LGD and EAD
Lecture 4 Capital adequacy, regulations, and the Basel II accord
Lecture 5 Basel II approaches: SA, F-IRB, and A-IRB
Lecture 6 Different facility types (asset classes) and credit risk modeling approaches
Section 2: Setting up the working environment
Lecture 7 Setting up the environment - Do not skip, please!
Lecture 8 Why Python and why Jupyter
Lecture 9 Installing Anaconda
Lecture 10 Jupyter Dashboard - Part 1
Lecture 11 Jupyter Dashboard - Part 2
Lecture 12 Installing the sklearn package
Section 3: Dataset description
Lecture 13 Our example: consumer loans. A first look at the dataset
Lecture 14 Dependent variables and independent variables
Section 4: General preprocessing
Lecture 15 Importing the data into Python
Lecture 16 Preprocessing few continuous variables
Lecture 17 Preprocessing few continuous variables: Homework
Lecture 18 Preprocessing few discrete variables
Lecture 19 Check for missing values and clean
Lecture 20 Check for missing values and clean: Homework
Section 5: PD Model: Data Preparation
Lecture 21 How is the PD model going to look like?
Lecture 22 Dependent variable: Good/ Bad (default) definition
Lecture 23 Fine classing, weight of evidence, and coarse classing
Lecture 24 Information value
Lecture 25 Data preparation. Splitting data
Lecture 26 Data preparation. An example
Lecture 27 Data preparation. Preprocessing discrete variables: automating calculations
Lecture 28 Data preparation. Preprocessing discrete variables: visualizing results
Lecture 29 Data preparation. Preprocessing discrete variables: creating dummies (Part 1)
Lecture 30 Data preparation. Preprocessing discrete variables: creating dummies (Part 2)
Lecture 31 Data preparation. Preprocessing discrete variables. Homework.
Lecture 32 Data preparation. Preprocessing continuous variables: Automating calculations
Lecture 33 Data preparation. Preprocessing continuous variables: creating dummies (Part 1)
Lecture 34 Data preparation. Preprocessing continuous variables: creating dummies (Part 2)
Lecture 35 Data preparation. Preprocessing continuous variables: creating dummies. Homework
Lecture 36 Data preparation. Preprocessing continuous variables: creating dummies (Part 3)
Lecture 37 Data preparation. Preprocessing continuous variables: creating dummies. Homework
Lecture 38 Data preparation. Preprocessing the test dataset
Lecture 39 PD model: data preparation notebooks
Section 6: PD model estimation
Lecture 40 The PD model. Logistic regression with dummy variables
Lecture 41 Loading the data and selecting the features
Lecture 42 PD model estimation
Lecture 43 Build a logistic regression model with p-values
Lecture 44 Interpreting the coefficients in the PD model
Section 7: PD model validation
Lecture 45 Out-of-sample validation (test)
Lecture 46 Evaluation of model performance: accuracy and area under the curve (AUC)
Lecture 47 Evaluation of model performance: Gini and Kolmogorov-Smirnov
Section 8: Applying the PD Model for decision making
Lecture 48 Calculating probability of default for a single customer
Lecture 49 Creating a scorecard
Lecture 50 Calculating credit score
Lecture 51 From credit score to PD
Lecture 52 Setting cut-offs
Lecture 53 Setting cut-offs. Homework
Lecture 54 PD model: logistic regression notebooks
Section 9: PD model monitoring
Lecture 55 PD model monitoring via assessing population stability
Lecture 56 Population stability index: preprocessing
Lecture 57 Population stability index: calculation and interpretation
Lecture 58 Homework: building an updated PD model
Section 10: LGD and EAD Models: Preparing the data
Lecture 59 LGD and EAD models: independent variables.
Lecture 60 LGD and EAD models: dependent variables
Lecture 61 LGD and EAD models: distribution of recovery rates and credit conversion factors
Section 11: LGD model
Lecture 62 LGD model: preparing the inputs
Lecture 63 LGD model: testing the model
Lecture 64 LGD model: estimating the accuracy of the model
Lecture 65 LGD model: saving the model
Lecture 66 LGD model: stage 2 – linear regression
Lecture 67 LGD model: stage 2 – linear regression evaluation
Lecture 68 LGD model: combining stage 1 and stage 2
Lecture 69 Homework: building an updated LGD model
Section 12: EAD model
Lecture 70 EAD model estimation and interpretation
Lecture 71 EAD model validation
Lecture 72 Homework: building an updated EAD model
Section 13: Calculating expected loss
Lecture 73 Calculating expected loss
Lecture 74 Homework: calculate expected loss on more recent data
Lecture 75 Completing 100%
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

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