Correlations, Association & Hypothesis Testing (with Python)

Exploration and assessment of the strength of associations between variables/features is critical in statistical analysis and machine learning. All of the applications in the course are written in Python. There is some overlap between this course and the instructor's other course, "Correlations, Associations, and Hypothesis Testing (with R)." This course, on the other hand, will benefit both junior analysts and more experienced data scientists. If you are an aspiring/junior data analyst/scientist, this course will assist you in laying the proper foundation early in your career. If you're a seasoned data scientist, this course will help you revisit and eventually improve your understanding of assessing associations between variables/features.

There are three major sections to the course. Each section examines a variety of statistical metrics in relation to variable associations and then constructs statistical hypothesis tests to assess the strength of these relationships. Throughout the course, you'll see how to use Python to implement the methods discussed in class and perform various hypothesis tests on real-world datasets. You'll also learn and master the art of interpreting results in a larger context.

A quiz is also included at the end of each section. These quizzes are designed to assist you in consolidating the main concepts covered in the course. You will have a clear and coherent understanding of covariances, correlations, t-test, Chi-squared test, ANOVA, F-test, and much more by the end of the course. You'll learn when and how to use these tests, as well as how to ensure that the underlying assumptions are met.

Who this course is for:

• Anyone interested in data analysis.
  
• All levels

Requirements

• There is no requirement for prior experience. You will begin with the fundamentals.

Course ratings: 5.0/5

Enroll here: https://www.udemy.com/course/with-python-correlations-association-hypothesis-testing/