Hypothesis testing is one of the most important concepts in statistics, especially in inferential statistics. The basis of the statistical hypothesis test and different terminologies (p-value, level of significance, type 1 and type 2 errors)will be explained elaborately. Students will be capable to infer a population mean, proportion, differences between means or proportions, and the relationships between variables and many others. The students will come to know the process of formulating and conducting the hypothesis test step by step. They will gain an insight view of different types of a statistical hypothesis tests. First of all, students will get basic ideas about normal distribution, which is the basis of all the statistical tests and the most widely used distribution too. Along with the normal distribution, they get knowledge about an empirical rule. They will be able to distinguish between the t-test and z-test. This course also includes the test for qualitative data, which is the chi-square test. The course will lay the foundation for the advanced level of a statistical hypothesis test. It will be very helpful to understand and infer the different models and algorithms in data science and machine learning. Specially, those who are interested to advance their careers in data science and machine learning should complete the course