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所在平台: Udemy |
课程主页: https://www.udemy.com/course/with-python-correlations-association-hypothesis-testing/
课程评论：没有评论
**课程名称：** Correlations, Association & Hypothesis Testing (with Python) **课程概述：** 本课程旨在深入探讨变量/特征之间的关联性，并学习如何评估其强度，这对统计分析和机器学习至关重要。所有课程内容均通过Python实现。 **课程亮点：** * **面向对象广泛：** 无论您是初级分析师还是经验丰富的数据科学家，本课程都能为您提供价值。对于渴望成为数据分析师/科学家的新手，它将帮助您打下坚实的基础；对于资深数据科学家，它将帮助您巩固和提升对变量关联性评估的理解。 * **内容体系化：** 课程分为三个主要部分： 1. **数值变量关联性评估与量化：** 学习如何衡量和量化两个数值变量之间的关系。 2. **分类变量关联性评估：** 探索如何分析两个分类变量之间的关联。 3. **数值与分类变量关联性评估：** 掌握如何评估一个数值变量和一个分类变量之间的关联。 * **实用性强：** 每个部分不仅会介绍相关的统计指标，还会构建统计假设检验来衡量关联的强度。课程包含大量实践环节，您将学习如何使用Python实现这些方法，并在真实数据集上进行各种假设检验。 * **注重解读：** 除了技术实现，课程还将指导您如何将统计结果置于更广阔的背景下进行解读。 * **巩固学习：** 每个部分结束后都设有测验，帮助您巩固所学概念。 * **掌握核心概念：** 课程结束后，您将透彻理解协方差、相关性、t检验、卡方检验、方差分析（ANOVA）、F检验等关键统计概念，并知道何时使用它们，以及如何确保这些方法的假设条件得到满足。 **学习目标：** * 能够评估和量化变量之间的关联强度。 * 掌握常见的假设检验方法，如t检验、卡方检验、ANOVA、F检验等。 * 能够使用Python在真实数据集中实现这些统计方法。 * 能够正确解读统计检验的结果，并将其应用于实际问题。 * 理解不同统计方法的使用场景和假设条件。
Exploring and assessing the strength of associations between variables/features plays a fundamental role in statistical analysis and machine learning.All the applications in the course are implemented in Python. There are overlaps between this course and my other course "Correlations, Associations and Hypothesis Testing (with R)".I decided to create this course after leading many data science projects and coming across many data scientists struggling with the fundamentals of association between variables/features and hypothesis testing.This course will be beneficial to junior analysts as well as to more experienced data scientists. In particular,If you are an aspiring/junior data analyst/scientist, this course will contribute towards building the right foundation at an early stage of your career.If you are an experienced data scientist, this course will help you to re-visit and eventually improve your understanding of the assessment of associations between variables/features.The course is divided into three main sections.The first section looks at the assessment and quantification of associations between numerical variables.The second section focusses on the assessment of associations between categorical variables.The third section covers the assessment of associations between numerical and categorical variables.Each section discusses a number of statistical metrics in relation to associations between variables and then build statistical hypothesis tests to measure the strengths of these associations.There are practical sessions throughout the course, where you will see how to implement the methods discussed in the course (using Python) and to perform various hypothesis testing using real world datasets. Your will also learn and master how to interpret results in a broader context.In addition, quiz is added at the end of each section. The objective of these quizzes is to help you to consolidate the main concepts covered in the course.By the end of the course, you will have a clear and coherent understanding of covariances, correlations, t-test, Chi-squared test, ANOVA, F-test, and much more. In particular, you will know when to use these tests and how to ensure that the underlying assumptions are satisfied.
