Hypothesis Testing : Essential statistics for Data Science

Hypothesis testing is one of the most essential topics in any statistical study and machine learning algorithmsP-value, alpha and point estimates find their foot prints in most of the statistical and ML studies.Hence it is important to understand hypothesis testing in as much detail as possible . The course includes five lecturesStarting the first lecture with the concepts that help you understand Hypothesis testing and what Null & Alternative hypothesis are. How to frame Null & Alternative hypothesis. Explain with a few real time examplesLecture 2 introduces you to the possible errors namely type I and Type II errors that occurs as a result of the hypothesis testing. Explains how to choose Type I error and balance between Type & II errorsThe third lecture is the main lecture and an elaborate one. Helps you understand how to apply the concepts and carry out the hypothesis testing applying all the essential statistical concepts. Explains all the statistical steps and their sequence involved in carrying out the hypothesis test until the conclusion is arrivedThe fourth lecture is dedicated for illustration of the hypothesis testing using real time examplesThe fifth and the final lecture explains you all about the power of the hypothesis test. The roll of type II error and it probability beta on the power of the testAll the above steps Comprehensively cover all the details in great level of granularity that at the end of the course I am sure you  will have a complete and a comprehensive understanding on Hypothesis testing and how to carry them out#hypothesistesting #nullhypothesis #alternativehypothesis #pointestimate #pvalue #zstatistic #tstatistic #alpha #type1error #typeierror #beta #type2error #typeiierror #statistics #datascience