Statistical Inference and Hypothesis Testing in Data Science Applications

Module 1: Start Here!

This introductory module sets the stage for the course, providing an overview of what students can expect and the foundational concepts they will explore throughout the program.

Module 2: Fundamental Concepts of Hypothesis Testing

This module delves into the core principles of hypothesis testing, covering topics such as types of hypotheses, test statistics, significance, and practical implementation of hypothesis testing through lab exercises and visualizations.

Module 3: Composite Tests, Power Functions, and P-Values

Building upon the foundational concepts, this module explores composite tests, power functions, and the interpretation of p-values, providing in-depth knowledge of the statistical tools essential for hypothesis testing in data science applications.

Module 4: t-Tests and Two-Sample Tests

Students will gain a comprehensive understanding of t-tests, two-sample tests, and their practical application in comparing population means, further enhancing their ability to make informed decisions using hypothesis testing methods.

Module 5: Beyond Normality

Exploring beyond normality assumptions, this module covers properties of the exponential distribution, best tests, likelihood ratio tests, and chi-squared tests, expanding students' knowledge of hypothesis testing in diverse scenarios.

Module 6: Likelihood Ratio Tests and Chi-Squared Tests

Delving into likelihood ratio tests and chi-squared tests, this module provides a deeper understanding of these advanced statistical tools, equipping students with the skills to apply them in real-world data science applications.