Data Science
26
Probability
Home
1
Introduction
2
Schedule
R Programming
3
Installation
4
Introduction to R
5
Getting Started
6
IDEs
7
R packages
8
Basic operations
9
Data Types & Vectors
10
Data Structures
11
Indexing
12
Factors
13
Base Data I/O
14
Read and Write Excel Data
15
Vectorized Operations
16
Control Flow
17
Summarizing Data
18
Aggregate
19
Writing Functions
20
Function Scoping
21
The Apply Family
22
Base Graphics
Data Science
23
Terminal
24
Version Control with Git and GitHub
25
Demo Dataset
26
Probability
27
Statistics
28
Hypothesis Testing
29
Data Visualization
30
Introduction to Machine Learning
31
Clustering
32
Decomposition and Dimensionality Reduction
33
Supervised Learning
34
AI Assistants for Data Science
35
References
Table of contents
26.1
Learning objectives
26.2
Events and probabilities
26.3
Joint and conditional probability
26.4
Independence and Bayes’ rule
26.5
Random variables and distributions
26.6
Expectation and variance
26.7
Worked example
26.8
Hands-on practice
26.9
Checkpoint
26.10
Further study
Data Science
26
Probability
26
Probability
26.1
Learning objectives
Express uncertainty using events and probabilities.
Distinguish marginal, joint, and conditional probabilities.
Interpret random variables and probability distributions.
26.2
Events and probabilities
26.3
Joint and conditional probability
26.4
Independence and Bayes’ rule
26.5
Random variables and distributions
26.6
Expectation and variance
26.7
Worked example
26.8
Hands-on practice
26.9
Checkpoint
26.10
Further study
25
Demo Dataset
27
Statistics