Credit Risk Modeling in Python from Udemy

Hi. Welcome to Credit Risk Modeling in Python. This is 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, holding a PhD from the Norwegian Business school and having 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 provides a complete picture of credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation - 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.

What's inside

Learning objectives

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
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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
Show more
Show less

Syllabus

Introduction

What does the course cover

What is credit risk and why is it important?

Expected loss (EL) and its components: PD, LGD and EAD

Capital adequacy, regulations, and the Basel II accord

Different facility types (asset classes) and credit risk modeling approaches

Basel II approaches: SA, F-IRB, and A-IRB

Why Python and why Jupyter

Setting up the working environment

Setting up the environment - Do not skip, please!

Installing Anaconda

Jupyter Dashboard - Part 1

Jupyter Dashboard - Part 2

Installing the sklearn package

Dataset description

Our example: consumer loans. A first look at the dataset

Dependent variables and independent variables

General preprocessing

Importing the data into Python

Preprocessing few continuous variables

Preprocessing few continuous variables: Homework

Preprocessing few discrete variables

Check for missing values and clean

Setting cut-offs

Check for missing values and clean: Homework

PD Model: Data Preparation

How is the PD model going to look like?

Dependent variable: Good/ Bad (default) definition

Fine classing, weight of evidence, and coarse classing

Setting cut-offs. Homework

Information value

Data preparation. Splitting data

PD model: logistic regression notebooks

Data preparation. An example

Data preparation. Preprocessing discrete variables: automating calculations

PD model monitoring

Data preparation. Preprocessing discrete variables: visualizing results

Data preparation. Preprocessing discrete variables: creating dummies (Part 1)

Data preparation. Preprocessing discrete variables: creating dummies (Part 2)

PD model monitoring via assessing population stability

Data preparation. Preprocessing discrete variables. Homework.

Data preparation. Preprocessing continuous variables: Automating calculations

Data preparation. Preprocessing continuous variables: creating dummies (Part 1)

Data preparation. Preprocessing continuous variables: creating dummies (Part 2)

Population stability index: preprocessing

Data preparation. Preprocessing continuous variables: creating dummies. Homework

Data preparation. Preprocessing continuous variables: creating dummies (Part 3)

Data preparation. Preprocessing the test dataset

PD model: data preparation notebooks

PD model estimation

The PD model. Logistic regression with dummy variables

Loading the data and selecting the features

Build a logistic regression model with p-values

Interpreting the coefficients in the PD model

PD model validation

Out-of-sample validation (test)

Evaluation of model performance: accuracy and area under the curve (AUC)

Evaluation of model performance: Gini and Kolmogorov-Smirnov

Applying the PD Model for decision making

Calculating probability of default for a single customer

Creating a scorecard

Calculating credit score

From credit score to PD

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Credit Risk Modeling in Python with these activities:

Review Statistics Fundamentals

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Solidify your understanding of statistical concepts like regression analysis and hypothesis testing, which are crucial for credit risk modeling.

Browse courses on Statistical Inference

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Review key statistical concepts and formulas.
Work through practice problems related to regression.
Focus on understanding statistical significance.

Review 'Financial Econometrics Using System Estimation: Structural Models and Computation'

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Deepen your understanding of the econometric foundations of credit risk models.

View Alter Ego on Amazon

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Focus on chapters related to regression and model validation.
Relate the econometric concepts to the Python code used in the course.
Identify the assumptions underlying the models.

Create a Glossary of Credit Risk Terms

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Improve your understanding of credit risk terminology by creating a glossary of key terms and definitions.

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Identify key terms from the course materials.
Research and define each term clearly and concisely.
Organize the terms alphabetically in a glossary format.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Review 'Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms'

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Gain a deeper understanding of credit risk measurement methodologies to complement the Python-based modeling techniques taught in the course.

View Credit Risk Management In and Out of the... on Amazon

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Read the chapters related to VaR and credit risk models.
Summarize the key concepts and methodologies.
Relate the concepts to the Python modeling techniques.

Logistic Regression Exercises

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Reinforce your understanding of logistic regression by completing practice exercises.

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Find datasets suitable for logistic regression.
Implement logistic regression models using Python.
Interpret the model results and evaluate performance.

Write a Blog Post on Weight of Evidence (WoE)

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Solidify your understanding of Weight of Evidence (WoE) and Information Value (IV) by explaining these concepts in a blog post.

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Research WoE and IV in detail.
Write a clear and concise explanation of WoE and IV.
Include examples of how WoE and IV are used in credit risk modeling.
Publish the blog post on a platform like Medium or LinkedIn.

Build a Simple Credit Scorecard

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Apply the concepts learned in the course by building a simplified credit scorecard using a publicly available dataset.

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Find a suitable open-source dataset of loan applications.
Preprocess the data using Python and relevant libraries.
Develop a logistic regression model to predict default.
Create a simple scorecard based on the model coefficients.
Evaluate the performance of the scorecard.

Career center

Learners who complete Credit Risk Modeling in Python will develop knowledge and skills that may be useful to these careers:

Credit Risk Analyst

A credit risk analyst assesses the creditworthiness of individuals or businesses, determining the level of risk associated with lending money. This involves analyzing financial data and using statistical models to predict the probability of default. This course, focused on credit risk modeling in Python, directly aligns with the responsibilities of a credit risk analyst. It helps build a foundation by teaching how to preprocess data, build state of the art models, and evaluate their effectiveness. The coverage of techniques like logistic regression, Area Under the Curve, Gini Coefficient, and Kolmogorov-Smirnov, all within a Python framework, provides highly relevant skills. Furthermore, working with a real-world dataset in the course simulates the practical challenges faced by a credit risk analyst, offering a valuable and applied learning experience.

See salaries and explore the career path for Credit Risk Analyst

Data Scientist

Data scientists apply statistical techniques and programming skills to analyze complex datasets and extract meaningful insights. The role often involves building predictive models. If you want to apply data science to finance, this is a good place to start. This course, focusing on credit risk modeling in Python, is especially relevant for data scientists working in the finance or banking sectors. The course teaches key techniques like weight of evidence, information value, fine and coarse classing, and linear and logistic regression. These are fundamental in data science and essential for building credit risk models. The course gives you the opportunity to differentiate your portfolio by showcasing skills in a highly demanded area.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

A quantitative analyst, or quant, uses mathematical and statistical methods to solve problems in finance and risk management. This role often requires advanced modeling skills. For those interested in quantitative analysis, this course provides valuable exposure to credit risk modeling techniques in Python. The curriculum covers not only building models but also data preprocessing and theoretical foundations. The coverage of topics such as linear regression, logistic regression, and assessing population stability are all crucial for a successful career in quantitative finance. The course also demonstrates the practical application of these techniques with a real-world dataset.

See salaries and explore the career path for Quantitative Analyst

Financial Modeler

Financial modelers create models to forecast financial performance, analyze investment opportunities, and assess risk. This often involves using software and programming languages to build and test models. This course offers a practical and focused approach to credit risk modeling in Python which can be extremely useful. The emphasis on pre-processing data and evaluating model effectiveness, coupled with the coverage of key techniques like linear and logistic regression, provides a strong foundation for financial modeling. The hands-on experience of building a complete credit risk model using a real-world dataset helps create a useful portfolio piece.

See salaries and explore the career path for Financial Modeler

Risk Manager

Risk managers identify, assess, and mitigate risks across various aspects of an organization, with a key focus on financial risk. Risk managers often use models to quantify and manage different types of risk. This course, dedicated to credit risk modeling in Python, gives insight into the techniques used to assess and manage credit risk. By understanding topics such as probability of default, loss given default, and exposure at default, along with methodologies like linear and logistic regression, you can enhance your ability to manage credit-related risks effectively. The course's detailed coverage of data preparation and model evaluation provides a practical skillset.

See salaries and explore the career path for Risk Manager

Data Analyst

Data analysts collect, process, and perform statistical analysis on data. They identify trends and insights to help inform business decisions. This course helps data analysts build a valuable skillset in credit risk modeling using Python. This is especially useful for those working or aspiring to work in the financial sector. The course's coverage of data preprocessing, statistical techniques, and model evaluation enhances analytical capabilities. Understanding and applying techniques like weight of evidence, information value, and area under the curve, as taught in this course, provides a practical and in-demand skill set.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business intelligence analysts examine data trends to help companies make smarter business decisions. They visualize data, create reports, and develop dashboards that track key performance indicators. This course may be useful for business intelligence analysts, particularly those in the financial sector, who want to enhance their analytical skills with a focus on credit risk. The course's coverage of data preprocessing, statistical modeling, and model evaluation can inform the development of more insightful and predictive business intelligence reports related to credit risk. The techniques and models learned can be integrated into dashboards and reports to provide a comprehensive view of credit risk.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

Machine learning engineers design, develop, and deploy machine learning models. They work on a variety of applications, often in collaboration with data scientists. This course may be useful for machine learning engineers interested in applying their skills to the financial domain, specifically in credit risk management. The detailed coverage of linear and logistic regression techniques, along with the hands-on experience of building a credit risk model in Python, provides a practical foundation. This allows them to contribute to projects involving credit scoring, risk assessment, and fraud detection.

See salaries and explore the career path for Machine Learning Engineer

Financial Analyst

Financial analysts evaluate financial data, prepare reports, and make recommendations to guide investment decisions. While a financial analyst may not directly build credit risk models on a regular basis, an understanding of the principles and techniques involved is beneficial. This course may be useful for providing a deeper understanding of credit risk assessment. The knowledge gained can inform financial analysis and decision-making processes, particularly when evaluating companies or projects with credit-related risks. Understanding the metrics and models discussed in the course helps.

See salaries and explore the career path for Financial Analyst

Management Consultant

Management consultants advise organizations on how to improve their performance and efficiency. This role may involve analyzing data, developing strategies, and implementing solutions. This course may be useful if working with financial institutions. Having a foundational understanding of credit risk modeling, coupled with Python skills, can provide insights into the models and techniques used by financial institutions to manage risk. This knowledge can be valuable when advising clients on risk management strategies, regulatory compliance, and performance improvement initiatives.

See salaries and explore the career path for Management Consultant

Compliance Officer

Compliance officers ensure that an organization adheres to regulatory requirements and internal policies. This course may be useful for compliance officers in the financial industry. An understanding of credit risk modeling principles and techniques, as covered in this course, can enhance their ability to assess and monitor compliance with credit risk regulations. The course's coverage of Basel II accord and regulatory requirements can provide valuable context for ensuring regulatory adherence and effective risk management practices.

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Actuary

Actuaries assess and manage financial risks using statistical and mathematical models. They primarily work in the insurance industry, but they may also have roles in banking and finance. While the focus of the course is on credit risk, actuaries may find the techniques useful in modeling other types of financial risks. The course's coverage of statistical modeling and risk assessment principles can be applied to various actuarial tasks, such as pricing insurance products or evaluating the financial health of companies. The programming skills learned in this course can be useful for automating tasks.

See salaries and explore the career path for Actuary

Investment Banker

Investment bankers advise companies on raising capital through the issuance of stocks and bonds. They also advise on mergers and acquisitions. While investment bankers are not directly involved in building credit risk models, an understanding of credit risk is useful when evaluating potential investments. This course may be useful for developing a better understanding of the creditworthiness of companies and the risks associated with lending. This knowledge allows for more informed investment decisions and client recommendations.

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Software Engineer

Software engineers design, develop, and test software applications. This course may be useful to those software engineers working in the financial sector. The Python skills learned in this course can lead to opportunities to develop software for financial analysis. This course helps software engineers to develop a better understanding of credit risk and enables them to better communicate with subject matter experts.

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Project Manager

Project managers plan, execute, and close projects. If a project manager is assigned to a data science team in finance, then this course could be useful for better understanding the work done by the team. This course may be useful for project managers to learn about credit risk modeling, especially if they are currently managing related projects. The knowledge in this course helps someone new better communicate with subject matter experts.

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Credit Risk Modeling in Python

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