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Basel IRB Credit Risk Modeling using SAS: PD, LGD, EAD
1 month ago
git.ir
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How Much Will We Lose: Chapter 6
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1. Python > tutto il resto. Devi saper usare pandas per pulire dataset sporchi, calcolare VaR, fare regressioni, simulazioni MonteCarlo e mini stress test. Un notebook GitHub con un progetto di rischio vale oro nel CV. 2. Excel avanzato come complemento. Pivot, funzioni nidificate e un mini-modello di Expected Loss: serve, ma senza Python oggi non entri. 3. SQL per lavorare su dati reali. I team di rischio vivono di dataset enormi: join, filtri, query pulite sono un must. 4. Conoscenza chiara de
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Grande distribution : Lidl, un modèle différent 🎙️ Thiago Almeida, directeur exécutif des achats de Lidl France, a expliqué devant la commission d'enquête #MargesGrandeDistribution (Anne-Catherine Loisier / Antoinette Guhl) pourquoi Lidl fonctionne sans centrale d'achat européenne.
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