Credit Risk Modeling in R

Business and Economics

Short Description

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Long Description

This course offers a practical approach to understanding credit risk modeling using real-life credit data. The ability to accurately assess the likelihood of default is crucial for banks when evaluating credit risk for both personal and company loans. In addition to introducing other models, this course focuses on two commonly used models in credit scoring: logistic regression and decision trees. Participants will gain hands-on experience in utilizing these models within the credit scoring context and will also learn about the evaluation methods employed by banks.

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Course Details

Duration: 4 hours
Format: Short Course
Price: USD39.00
Course Link

DataCamp

DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.

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