Introduction to Data Analysis

Related lectures (32)

Page 3 of 4

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Covers linear regression, correlation analysis, and logistic regression fundamentals, emphasizing the distinction between correlation and causation.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Explores variance, covariance, and practical applications in statistics and probability.

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Explores scientific misconduct, p-value optimization, and spotting issues with conclusions using real-world examples.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.