Credit Risk Modeling in Python
Credit Risk Modeling in Python, available at $124.99, has an average rating of 4.52, with 75 lectures, 46 quizzes, based on 6536 reviews, and has 29661 subscribers.
You will learn about 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 This course is ideal for individuals who are You should take this course if you are a data science student interested in improving their skills or You should take this course if you want to specialize in credit risk modeling or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills or This course is for you if you want a great career It is particularly useful for You should take this course if you are a data science student interested in improving their skills or You should take this course if you want to specialize in credit risk modeling or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills or This course is for you if you want a great career.
Enroll now: Credit Risk Modeling in Python
Summary
Title: Credit Risk Modeling in Python
Price: $124.99
Average Rating: 4.52
Number of Lectures: 75
Number of Quizzes: 46
Number of Published Lectures: 75
Number of Published Quizzes: 46
Number of Curriculum Items: 121
Number of Published Curriculum Objects: 121
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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-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
– 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.
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!
Course Curriculum
Chapter 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
Chapter 2: Setting up the working environment
Lecture 1: Setting up the environment – Do not skip, please!
Lecture 2: Why Python and why Jupyter
Lecture 3: Installing Anaconda
Lecture 4: Jupyter Dashboard – Part 1
Lecture 5: Jupyter Dashboard – Part 2
Lecture 6: Installing the sklearn package
Chapter 3: Dataset description
Lecture 1: Our example: consumer loans. A first look at the dataset
Lecture 2: Dependent variables and independent variables
Chapter 4: General preprocessing
Lecture 1: Importing the data into Python
Lecture 2: Preprocessing few continuous variables
Lecture 3: Preprocessing few continuous variables: Homework
Lecture 4: Preprocessing few discrete variables
Lecture 5: Check for missing values and clean
Lecture 6: Check for missing values and clean: Homework
Chapter 5: PD Model: Data Preparation
Lecture 1: How is the PD model going to look like?
Lecture 2: Dependent variable: Good/ Bad (default) definition
Lecture 3: Fine classing, weight of evidence, and coarse classing
Lecture 4: Information value
Lecture 5: Data preparation. Splitting data
Lecture 6: Data preparation. An example
Lecture 7: Data preparation. Preprocessing discrete variables: automating calculations
Lecture 8: Data preparation. Preprocessing discrete variables: visualizing results
Lecture 9: Data preparation. Preprocessing discrete variables: creating dummies (Part 1)
Lecture 10: Data preparation. Preprocessing discrete variables: creating dummies (Part 2)
Lecture 11: Data preparation. Preprocessing discrete variables. Homework.
Lecture 12: Data preparation. Preprocessing continuous variables: Automating calculations
Lecture 13: Data preparation. Preprocessing continuous variables: creating dummies (Part 1)
Lecture 14: Data preparation. Preprocessing continuous variables: creating dummies (Part 2)
Lecture 15: Data preparation. Preprocessing continuous variables: creating dummies. Homework
Lecture 16: Data preparation. Preprocessing continuous variables: creating dummies (Part 3)
Lecture 17: Data preparation. Preprocessing continuous variables: creating dummies. Homework
Lecture 18: Data preparation. Preprocessing the test dataset
Lecture 19: PD model: data preparation notebooks
Chapter 6: PD model estimation
Lecture 1: The PD model. Logistic regression with dummy variables
Lecture 2: Loading the data and selecting the features
Lecture 3: PD model estimation
Lecture 4: Build a logistic regression model with p-values
Lecture 5: Interpreting the coefficients in the PD model
Chapter 7: PD model validation
Lecture 1: Out-of-sample validation (test)
Lecture 2: Evaluation of model performance: accuracy and area under the curve (AUC)
Lecture 3: Evaluation of model performance: Gini and Kolmogorov-Smirnov
Chapter 8: Applying the PD Model for decision making
Lecture 1: Calculating probability of default for a single customer
Lecture 2: Creating a scorecard
Lecture 3: Calculating credit score
Lecture 4: From credit score to PD
Lecture 5: Setting cut-offs
Lecture 6: Setting cut-offs. Homework
Lecture 7: PD model: logistic regression notebooks
Chapter 9: PD model monitoring
Lecture 1: PD model monitoring via assessing population stability
Lecture 2: Population stability index: preprocessing
Instructors
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365 Careers
Creating opportunities for Data Science and Finance students
Rating Distribution
- 1 stars: 66 votes
- 2 stars: 63 votes
- 3 stars: 505 votes
- 4 stars: 2205 votes
- 5 stars: 3697 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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