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DSCI 500B Essential Probability Theory for Data Science (Kuter)

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01: What is Probability
1.01: Sample Spaces and Events
1.02: Probability Measures
1.03: Equally Likely Outcomes and Counting Techniques (Combinatorics)
02: Conditional Probability
2.01: Conditional Probability and Bayes' Rule
2.02: Independent Events
03: Discrete Random Variables
3.01: Introduction to Random Variables
3.02: Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs) for Discrete Random Variables
3.03: Bernoulli and Binomial Distributions
3.04: Expected Value of Discrete Random Variables
3.05: Variance of Discrete Random Variables
04: Continuous Random Variables
4.01: Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) for Continuous Random Variables
4.02: Expected Value and Variance of Continuous Random Variables
4.03: Uniform Distributions
4.04: Normal Distributions
05: Multivariate Random Variables
5.01: Joint Distributions of Discrete Random Variables
5.02: Joint Distributions of Continuous Random Variables
06: The Sample Mean and Central Limit Theorem
6.01: Functions of Normal Random Variables
6.02: Sample Mean
07: The Sample Variance and Other Distributions
7.01: Chi-Squared Distributions
7.2: Sample Variance