Testing statistical hypotheses are an integral part of Data Science. In my new course, I used real-world data sets to test parametric and nonparametric hypotheses using Python 3.
The course has several strengths that should not be ignored.
- It is hands-on, uses real-world data and focuses on testing statistical hypotheses using Python 3.
- It is taught by an Adjunct Professor of Statistics who taught statistics for twelve years
- it is extensive and cover all aspects of testing statistical hypotheses using Python
- It uses Jupyter notebook and mark-downs to clearly document the codes and make them professional
- The course uses latex to write the statistical hypotheses to help users understand what is being tested
In this course, you will learn how to test various statistical hypotheses using Python 3. The course covers the most relevant tests about the population parameters for one, two and many samples. In addition, the course covers ANOVA (Analysis of Variance) and many nonparametric tests. This course is hands-on with real-world datasets to help the students understand how to carry on the various tests.