|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/credit-risk-modeling-in-python/
课程评论：没有评论
课程名称：Python中的信用风险建模 课程概览： 欢迎来到Python中的信用风险建模课程！这是唯一一门在线课程，教授银行如何利用Python中的数据科学建模来提升绩效并遵守监管要求。如果您对数据科学职业感兴趣，那么这是一门非常适合您的课程。以下是课程的几个亮点： - 讲师为行业专家，拥有挪威商学院的博士学位，并曾在世界知名高校（如HEC、德克萨斯大学和挪威商学院）授课。 - 课程适合初学者，内容从理论和数据预处理开始，逐步解决完整的实际案例。 - 所有内容都是最新的，涵盖了当今银行业Python模型发展的相关课程。 - 这是一门独特的课程，提供关于信用风险的全面知识，使用先进技术建模期望损失方程的三个方面：违约概率（PD）、损失给付率（LGD）和暴露于违约（EAD），并从零开始构建评分卡。 - 课程展示如何创建符合巴塞尔II与巴塞尔III规范的模型，这是其他课程很少涉及的内容。 - 使用的不是虚拟数据，而是实际的真实世界案例。 - 通过这门课程，您将能够丰富您的数据科学投资组合，展示市场上高度需求的技能。 - 最重要的是，您将亲眼目睹如何在真实世界中解决数据科学任务。 本课程深入涵盖了多种重要的数据科学技术，包括： - 证据权重（Weight of Evidence） - 信息价值（Information Value） - 精细分类（Fine Classing） - 粗略分类（Coarse Classing） - 线性回归（Linear Regression） - 逻辑回归（Logistic Regression） - 曲线下面积（Area Under the Curve） - 接收者操作特征曲线（Receiver Operating Characteristic Curve） - 基尼系数（Gini Coefficient） - Kolmogorov-Smirnov检验 - 人口稳定性评估（Assessing Population Stability） - 模型维护（Maintaining a Model） 除了视频课程，您还将获得多种有价值的学习资源： - 讲座 - 笔记本文件 - 作业 - 问题测验 - 幻灯片 - 下载资源 - Q&A环节，您可以与课程讲师进行联系。 今天注册这门课程将是您迈向数据科学职业的绝佳一步。请务必充分利用这个令人惊叹的机会！期待在课程中见到您！
Hi! Welcome to Credit Risk Modeling in Python. This is the only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you, if you are interested in a data science career. Here's why:· The instructor is a proven expert, holding a PhD from the Norwegian Business school and having taught in world renowned universities such as HEC, the University of Texas, and the Norwegian Business school).· The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you· Everything we cover is up-to-date and relevant in today's development of Python models for the banking industry· This is the only online course that provides a complete picture of credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation - PD, LGD, and EAD) including creating a scorecard from scratch· Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon· We are not going to work with fake data. The dataset used in this course is an actual real-world example· You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace· What is most important - you get to see first-hand how a data science task is solved in the real-worldMost data science courses cover several frameworks but skip the pre-processing and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine.We don't do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ‘'friendliest'' format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness.Throughout the course, we will cover several important data science techniques.- Weight of evidence- Information value- Fine classing- Coarse classing- Linear regression- Logistic regression- Area Under the Curve- Receiver Operating Characteristic Curve- Gini Coefficient- Kolmogorov-Smirnov- Assessing Population Stability- Maintaining a modelAlong with the video lessons you will receive several valuable resources that will help you learn as much as possible:· Lectures· Notebook files· Homework· Quiz questions· Slides· Downloads· Access to Q & A where you could reach out and contact the course tutor.Signing up for the course today could be a great step towards your career in data science. Make sure that you take full advantage of this amazing opportunity!See you on the inside!
