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Credit Risk Modeling in R

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5

(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

This course will teach you how to model credit risks using logistic regression and decision trees in R.

The role of banks is crucial in assessing credit risk for company and personal loans. The probability of a debtor defaulting is a key component in determining credit risk. Although you will learn many other models in this course, the only two that will be used for credit scoring are logistic regression and decision trees. These models will be discussed in the context of how banks evaluate them.

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Highlights

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Rating & Reviews

Top 30 Percentile

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Pedagogy

Top 30 Percentile

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Pedagogy

Acquire all major R Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in R Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

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Rating & Reviews

This highly acclaimed course is among the top-rated in R Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in R Programming.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate R for Finance

What You Will Learn

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

In this chapter, you will learn how to apply logistic regression models on credit data in R

Course Instructors

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Lore Dirick

Director of Data Science Education 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 workin...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%

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