Introduction to Statistics: Foundations of Data Analysis

This module provides an overview of the course and a review of the main tools used in descriptive statistics to visualize information.

This module covers the main concepts for sampling and designing experiments, including pitfalls and how to evaluate the effectiveness of such experiments.

This module covers the definition of probability and essential rules of probability for solving simple and complex challenges, including examples of how probability rules are used in real-life situations.

This module covers the empirical rule and normal approximation for data, the binomial distribution, and the basics of random variables.

This module covers the Law of Large Numbers, the Central Limit Theorem, and differentiating between different types of histograms in statistical analysis.

This module covers regression techniques, including inference, regression, and regression diagnostics.

This module teaches how to construct and interpret confidence intervals in standard situations.

This module covers the logic behind testing and how to perform appropriate statistical tests for different samples and situations, including common misunderstandings and pitfalls in testing.

This module focuses on the Monte Carlo method and the Bootstrap method, covering their theoretical principles and applications in different contexts.

This module covers three important statistical analyses for categorical data: Chi-Square Goodness of Fit test, Chi-Square test of Homogeneity, and Chi-Square test of Independence.

This module covers the basics of ANOVA and how F-tests work on one-way ANOVA examples.

This module covers issues in big data era: data snooping, multiple testing fallacy, challenges in data reproducibility and applicability, and prevention methods.