Statistics for Data Science: Basics and Modelling

Related lectures (31)

Page 3 of 4

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Covers the normal distribution, including its properties and calculations.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Explores conditional density, expectations, and independence of random variables with practical examples.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores moment-generating functions, linear combinations, and normality of random variables.