Course Credit Risk Modeling in R

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

DescriptionChaptersExercisesInstructor

This online course about Credit Risk Modeling in R covers a key part of what a future data analyst would require.

Enroll now in this Credit Risk Modeling in R course, and don’t miss the opportunity of learning with the best, as Lore Dirick is. With 52 enriching exercises, 16 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Chapter 1: Introduction and data preprocessing
This chapter begins with a general introduction to credit risk models. We’ll explore a real-life data set, then preprocess the data set such that it’s in the appropriate format before applying the credit risk models.
Chapter 2: Logistic regression
Logistic regression is still a widely used method in credit risk modeling. In this chapter, you will learn how to apply logistic regression models on credit data in R.
Chapter 3: Decision trees
Classification trees are another popular method in the world of credit risk modeling. In this chapter, you will learn how to build classification trees using credit data in R.
Chapter 4: Evaluating a credit risk model
In this chapter, you’ll learn how you can evaluate and compare the results obtained through several credit risk models.
Chapter 5:
Credit Risk Modeling in R. Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Lore Dirick

Manager of Data Science Curriculum at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is now the Manager of Data Science Curriculum at Flatiron School, a coding bootcamp in NYC.

Collaborators

#R #Python #MachineLearning #BigData #DataAnalysis