Probability for Data Science

Week 1: Fundamentals of Probability

 • Module 1: Introduction to Probability

 ◦ Basic Probability Concepts

 ◦ Probability Rules and Theorems

 ◦ Conditional Probability and Bayes’ Theorem

 • Module 2: Probability Distributions

 ◦ Discrete Distributions (Bernoulli, Binomial, Poisson)

 ◦ Continuous Distributions (Uniform, Normal, Exponential)

 ◦ Properties and Applications of Distributions

Week 2: Advanced Probability Concepts

 • Module 3: Joint, Marginal, and Conditional Distributions

 ◦ Joint Probability Distributions

 ◦ Marginal Probability Distributions

 ◦ Conditional Distributions and Independence

 • Module 4: Applications in Data Science

 ◦ Central Limit Theorem

 ◦ Law of Large Numbers

 ◦ Probability in Machine Learning (e.g., Naive Bayes, Markov Chains)