The ‘Credit Risk Modeling in Python Course’ course will teach you complete data science case study: preprocessing, modeling, model validation and maintenance in Python. The course also improves your Python modeling skills
The course helps you to build a complete credit risk model in Python. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Credit Risk Modeling in Python Course’ for INR 499.
Who all can opt for this course?
- If you want to sharpen your data science skills, you should enrol in this course
- If you wish to specialise in credit risk modelling, you should enrol in this course
- The training is excellent for novices as well because it starts with the basics and gradually develops your skills
- If you desire a successful job, you should take this course
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 499 ( |
| Duration | 07 Hours |
| Rating | 4.7/5 |
| Student Enrollment | 21,972 students |
| Instructor | 365 Careers https://www.linkedin.com/in/365careers |
| Topics Covered | PD model, LGD and EAD models, Data Science |
| Course Level | Beginner |
| Total Student Reviews | 4,957 |
Learning Outcomes
- Become more proficient in Python modelling
- Use a trending subject to set your data science portfolio apart
- Add a variety of in-demand data science skills to your resume
- Create a comprehensive Python credit risk model
- Succeed in interviews by demonstrating your practical knowledge
- How to use Python to preprocess real data
- Study the theory of credit risk modelling
- Use cutting-edge data science methods
- Fix a real-world data science issue
- Be able to assess your model’s performance
- Use Python to perform logistic and linear regressions
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (38 minutes) | What does the course cover |
| What is credit risk and why is it important? | ||
| What is credit risk and why is it important? | ||
| Expected loss (EL) and its components: PD, LGD and EAD | ||
| Expected loss (EL) and its components: PD, LGD and EAD | ||
| Capital adequacy, regulations, and the Basel II accord | ||
| Capital adequacy, regulations, and the Basel II accord | ||
| Basel II approaches: SA, F-IRB, and A-IRB | ||
| Basel II approaches: SA, F-IRB, and A-IRB | ||
| Different facility types (asset classes) and credit risk modeling approaches | ||
| Different facility types (asset classes) and credit risk modeling approaches | ||
| 2. | Setting up the working environment (17 minutes) | Setting up the environment – Do not skip, please! |
| Why Python and why Jupyter | ||
| Installing Anaconda | ||
| Jupyter Dashboard – Part 1 | ||
| Jupyter Dashboard – Part 2 | ||
| Installing the sklearn package | ||
| 3. | Dataset description (09 minutes) | Our example: consumer loans. A first look at the dataset |
| Our example: consumer loans. A first look at the dataset | ||
| Dependent variables and independent variables | ||
| Dependent variables and independent variables | ||
| 4. | General preprocessing (29 minutes) | Importing the data into Python |
| Importing the data into Python | ||
| Preprocessing few continuous variables | ||
| Preprocessing few continuous variables | ||
| Preprocessing few continuous variables: Homework | ||
| Preprocessing few discrete variables | ||
| Preprocessing few discrete variables | ||
| Check for missing values and clean | ||
| Check for missing values and clean | ||
| Check for missing values and clean: Homework | ||
| 5. | PD Model: Data Preparation (01 hour 56 minutes) | How is the PD model going to look like? |
| How is the PD model going to look like? | ||
| Dependent variable: Good/ Bad (default) definition | ||
| Dependent variable: Good/ Bad (default) definition | ||
| Fine classing, weight of evidence, and coarse classing | ||
| Fine classing, weight of evidence, and coarse classing | ||
| Information value | ||
| Information value | ||
| Data preparation. Splitting data | ||
| Data preparation. Splitting data | ||
| Data preparation. An example | ||
| Data preparation. An example | ||
| Data preparation. Preprocessing discrete variables: automating calculations | ||
| Data preparation. Preprocessing discrete variables: automating calculations | ||
| Data preparation. Preprocessing discrete variables: visualizing results | ||
| Data preparation. Preprocessing discrete variables: creating dummies (Part 1) | ||
| Data preparation. Preprocessing discrete variables: creating dummies (Part 1) | ||
| Data preparation. Preprocessing discrete variables: creating dummies (Part 2) | ||
| Data preparation. Preprocessing discrete variables: creating dummies (Part 2) | ||
| Data preparation. Preprocessing discrete variables. Homework. | ||
| Data preparation. Preprocessing continuous variables: Automating calculations | ||
| Data preparation. Preprocessing continuous variables: Automating calculations | ||
| Data preparation. Preprocessing continuous variables: creating dummies (Part 1) | ||
| Data preparation. Preprocessing continuous variables: creating dummies (Part 1) | ||
| Data preparation. Preprocessing continuous variables: creating dummies (Part 2) | ||
| Data preparation. Preprocessing continuous variables: creating dummies (Part 2) | ||
| Data preparation. Preprocessing continuous variables: creating dummies. Homework | ||
| Data preparation. Preprocessing continuous variables: creating dummies (Part 3) | ||
| Data preparation. Preprocessing continuous variables: creating dummies (Part 3) | ||
| Data preparation. Preprocessing continuous variables: creating dummies. Homework | ||
| Data preparation. Preprocessing the test dataset | ||
| PD model: data preparation notebooks | ||
| 6. | PD model estimation (34 minutes) | The PD model. Logistic regression with dummy variables |
| The PD model. Logistic regression with dummy variables | ||
| Loading the data and selecting the features | ||
| PD model estimation | ||
| Build a logistic regression model with p-values | ||
| Build a logistic regression model with p-values | ||
| Interpreting the coefficients in the PD model | ||
| Interpreting the coefficients in the PD model | ||
| 7. | PD model validation (27 minutes) | Out-of-sample validation (test) |
| Out-of-sample validation (test) | ||
| Evaluation of model performance: accuracy and area under the curve (AUC) | ||
| Evaluation of model performance: accuracy and area under the curve (AUC) | ||
| Evaluation of model performance: Gini and Kolmogorov-Smirnov | ||
| Evaluation of model performance: Gini and Kolmogorov-Smirnov | ||
| 8. | Applying the PD Model for decision making (35 minutes) | Calculating probability of default for a single customer |
| Creating a scorecard | ||
| Creating a scorecard | ||
| Calculating credit score | ||
| Calculating credit score | ||
| From credit score to PD | ||
| From credit score to PD | ||
| Setting cut-offs | ||
| Setting cut-offs | ||
| Setting cut-offs. Homework | ||
| PD model: logistic regression notebooks | ||
| 9. | PD model monitoring (27 minutes) | PD model monitoring via assessing population stability |
| PD model monitoring via assessing population stability | ||
| Population stability index: preprocessing | ||
| Population stability index: calculation and interpretation | ||
| Population stability index: calculation and interpretation | ||
| Homework: building an updated PD model | ||
| 10. | LGD and EAD Models: Preparing the data (16 minutes) | LGD and EAD models: independent variables. |
| LGD and EAD models: independent variables | ||
| LGD and EAD models: dependent variables | ||
| LGD and EAD models: dependent variables | ||
| LGD and EAD models: distribution of recovery rates and credit conversion factors | ||
| LGD and EAD models: distribution of recovery rates and credit conversion factors | ||
| 11. | LGD model (29 minutes) | LGD model: preparing the inputs |
| LGD model: testing the model | ||
| LGD model: testing the model | ||
| LGD model: estimating the accuracy of the model | ||
| LGD model: saving the model | ||
| LGD model: stage 2 – linear regression | ||
| LGD model: stage 2 – linear regression with comments | ||
| LGD model: stage 2 – linear regression evaluation | ||
| LGD model: stage 2 – linear regression evaluation | ||
| LGD model: combining stage 1 and stage 2 | ||
| LGD model: combining stage 1 and stage 2 | ||
| Homework: building an updated LGD model | ||
| 12. | EAD model (11 minutes) | EAD model estimation and interpretation |
| EAD model estimation and interpretation | ||
| EAD model validation | ||
| EAD model validation | ||
| Homework: building an updated EAD model | ||
| 13. | Calculating expected loss (17 minutes) | Calculating expected loss |
| Calculating expected loss | ||
| Homework: calculate expected loss on more recent data | ||
| Completing 100% |
Resources Required
- No previous knowledge is necessary
- Both Python and Anaconda must be installed
Featured Review
Ridwan Adi Pratama (4/5) : This was a good course for introductory to credit risk modeling in pyhton. Need to include the forward looking factors for PD, EAD and LGD models and it would be a perfect course.
Pros
- Rajshekhar Paul (5/5) : This is the best course on credit risk modelling i have come across.
- Daniel Amponsah (4/5) : This is a an awesome lecture by all standards and kudos to the instructor for a wonderful work.
- Gursewak Singh Sidhu (5/5) : A wonderful course to study basics of PD, LGD and EAD modeling.
- Sunil Raperia (4/5) : The best course I have ever found for Credit Risk modelling, all the things are explained step by step.
Cons
- Wei Ji (1/5) : very slow response the instructor does not explain the coding well.
- Tyrone Byrne (1/5) : Secondly, the lecturer (time restrictions?) does a terrible job of explaining step by step why we’re doing what we’re doing, this becomes especially apparent in the coding sections.
- Shideh Rafigh (2/5) : The videos are created in a way that it assumes that the learner has a decent level of programming by Python while in the description of the program it says assumes no knowledge of Python! very disappointing.
- Anonymized User (2/5) : Sloppy materials with a lot of mistakes, concepts are not explained very well and the explanations of the Python code is very poor
About the Author
The instructor of this course is 365 Careers who is a Creating opportunities for Data Science and Finance students. With 4.6 Instructor Rating and 661,468 Reviews on Udemy, 365 Careers offers 91 Courses and has taught 2,296,959 Students so far.
- On Udemy, 365 Careers is the top-selling provider of classes in business, finance, and data science
- In 210 different countries, more than 2,000,000 students have completed the company’s courses
- People who have finished 365 Careers trainings now work at renowned companies like Apple, PayPal, and Citigroup
- On Udemy right now, 365 concentrates on the following subjects: 1) Finance – Financial modelling in Excel, valuation, capital budgeting, financial statement analysis (FSA), investment banking (IB), leveraged buyout (LBO), corporate budgeting, using Python for finance, Tesla value case study, CFA, ACCA, and CPA 2) Data science – Credit Risk Modeling and Credit Analytics, Data Literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy, Statistics, Mathematics, Probability, SQL, Python Programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the Integration of SQL and Tableau, the Integration of SQL, Python, Tableau, and Power BI Entrepreneurship: Business Strategy, Human Management, Marketing, Decision-Making, Negotiation, and Persuasion, as well as Tesla’s Business Strategy and Marketing 4) Office efficiency, including Microsoft Word, Excel, PowerPoint, and Outlook 5) Enterprise Blockchain Every one of our classes is: Pre-scripted, practical, laser-focused, interesting, and tried in real-world situations By selecting 365 Careers, you can be certain that you will learn from seasoned professionals who are passionate about sharing their knowledge and can help you advance from a beginner to a master in the shortest amount of time
- The classes offered by 365 Careers are the ideal place to start if you want to work as a financial analyst, data scientist, business analyst, data analyst, business intelligence analyst, business executive, finance manager, FP&A analyst, investment banker, or entrepreneur
Comparison Table
| Parameters | Credit Risk Modeling in Python 2023 | Python Data Science with Pandas: Master 12 Advanced Projects | Quantitative Finance & Algorithmic Trading in Python |
|---|---|---|---|
| Offers | INR 499 ( | INR 455 ( | INR 455 ( |
| Duration | 7 hours | 15.5 hours | 15 hours |
| Rating | 4.7/5 | 4.6/5 | 4.6/5 |
| Student Enrollments | 21,955 | 9,550 | 12,408 |
| Instructors | 365 Careers | Alexander Hagmann | Holczer Balazs |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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