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via Udemy |
Go to Course: https://www.udemy.com/course/credit-risk-modeling-in-python/
The "Credit Risk Modeling in Python" course on Coursera is an excellent choice for anyone interested in building a career in data science, especially within the banking and financial industry. This course stands out because it offers a comprehensive, real-world approach to credit risk modeling using Python, focusing on practical skills that are highly demanded in the job market. One of the key strengths of this course is its expert instructor, who holds a PhD from the Norwegian Business School and has taught at prestigious universities like HEC and the University of Texas. This assures you of high-quality instruction rooted in both academic rigor and practical experience. The course is designed to be accessible for beginners, beginning with fundamental concepts, data pre-processing, and theoretical foundations before moving into hands-on modeling. The curriculum covers essential techniques such as Weight of Evidence, Information Value, Logistic Regression, and model evaluation metrics like AUC, Gini Coefficient, and Kolmogorov-Smirnov statistic. A notable feature is that you'll learn to develop models compliant with Basel II and Basel III regulations, making your skills directly applicable to real-world banking scenarios. What truly sets this course apart is its focus on real-world data. Instead of working with synthetic datasets, you'll analyze an actual dataset, giving you a realistic understanding of credit risk modeling. The course also guides you through creating a scorecard from scratch, providing a complete picture of the process involved in credit risk assessment. In addition to video lectures, you'll receive diverse learning resources such as notebooks, quizzes, slides, and homework assignments, along with access to an interactive Q&A with the instructor. This multifaceted approach ensures that you thoroughly understand each component of the modeling pipeline. Overall, I highly recommend the "Credit Risk Modeling in Python" course on Coursera for aspiring data scientists and professionals aiming to specialize in financial modeling. It offers a balanced combination of theory, practical skills, and real-world application, making it an ideal investment in your career development. Whether you're just starting out or looking to deepen your expertise, this course provides valuable insights and skills that can differentiate you in the competitive job market.
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 - 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!