Graduate Certificate in Credit Scoring Data Science
-- ViewingNowThe Graduate Certificate in Credit Scoring & Data Science is a comprehensive course designed to meet the growing industry demand for skilled professionals in credit scoring and data analysis. This program equips learners with the essential skills required to analyze complex credit data and make informed, data-driven decisions.
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โข Fundamentals of Credit Scoring: An introduction to credit scoring, including the history, purpose, and key concepts. This unit will cover credit scoring models, credit reporting agencies, and the role of credit scoring in lending decisions.
โข Data Analysis for Credit Scoring: An examination of the techniques and methods used to analyze credit data, including descriptive statistics, data visualization, and data preprocessing. This unit will cover data cleaning, feature engineering, and data exploration.
โข Machine Learning for Credit Scoring: An exploration of the machine learning algorithms used in credit scoring, including logistic regression, decision trees, and random forests. This unit will cover the advantages and disadvantages of each algorithm and how to select the right algorithm for a given problem.
โข Evaluating Credit Scoring Models: A review of the techniques used to evaluate credit scoring models, including performance metrics, cross-validation, and model selection. This unit will cover the importance of model evaluation and how to interpret the results.
โข Fair Lending and Compliance: An examination of the legal and ethical considerations in credit scoring, including fair lending laws and regulations. This unit will cover the importance of compliance and how to ensure that credit scoring models are fair and unbiased.
โข Credit Scoring in Practice: A review of the practical considerations in implementing credit scoring models, including data privacy, model deployment, and monitoring. This unit will cover the challenges and best practices in implementing credit scoring models in a real-world setting.
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