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Summary
Unclaimed ProfileThis course focuses specifically on moving from credit scoring theory to a fully validated, production-ready scorecard using Python, covering data preparation, WOE/IV encoding, logistic regression modeling, probability-to-score conversion, cutoff optimization, and a full validation suite (ROC, KS, Gini, PSI, CSI). By the end, students have a complete capstone scorecard with monitoring frameworks and management reports, along with the full Python codebase, sample datasets, and templates used in the course.
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Program Overview
Learning format
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Price
Price may change · updated within 1–2 weeks
Course language
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
Best For
- Those wanting production-ready, not theoretical, training
- Credit risk managers wanting technical depth
- Data scientists in fintech or banking
- Risk analysts wanting Python scorecard skills
Not For
- Those outside finance/credit risk fields
- People wanting a fully self-paced format
- Those wanting independently verified instructor credentials
- Complete beginners to Python programming
What You'll Learn
- Monitoring scorecard performance and detecting model drift
- Determining optimal cutoffs using ROC and KS analysis
- Converting probabilities into scorecard point systems
- Building and validating logistic regression scorecards
- Applying WOE/IV encoding and variable selection techniques
- Preparing credit data, handling missing values and outliers
About the creator
Nitin Kumar
View full profileCredit Scorecard Development · Not publicly disclosed
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Nitin Kumar is a credit risk modeling specialist with a stated 15+ years of experience building scorecards for major banks and financial institutions. Holding an MBA from Rotterdam School of Management and an M.Tech in Computer Science from NIT Karnataka, he runs Digital Dojo and teaches Master Credit Scorecard Development with Python, a hands-on Maven course on building production-ready credit scorecards.
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