Model Selection & Boosting | Machine Learning Tutorial | Data Science Tutorial | Edureka Rewind
Taha GÖÇER
About this course
This course offers an in-depth exploration of statistical hypothesis testing, covering key concepts such as null and alternate hypotheses, critical value and P value methods, and the implications of statistical testing in data science. Through real-world examples and case studies, learners will gain clarity on how to apply hypothesis testing to validate assumptions and make informed decisions based on data analysis.
What you should already know
A basic understanding of statistics and familiarity with Python programming is recommended before taking the course.
What you will learn
By the end of this course, learners can expect to proficiently perform hypothesis testing, analyze statistical data, and apply learned concepts to real-world data science challenges.