Correlations, Association & Hypothesis Testing (with Python)

Published 2022-05-08
Platform Udemy
Rating 5.00
Number of Reviews 2
Number of Students 4
Price $59.99
Instructors
Viani D.B.
Subjects

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Data Science Prerequisite: Correlations, Associations and Hypothesis Testing (with applications using Python)

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,

The course is divided into three main sections.

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.

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