Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.

This course is part of the Data Science Foundations: Statistical Inference Specialization

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## About this Course

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

## What you will learn

Explain why probability is important to statistics and data science.

See the relationship between conditional and independent events in a statistical experiment.

Calculate the expectation and variance of several random variables and develop some intuition.

## Skills you will gain

- Probability
- central limit theorem
- continuous random variables
- Bayes' Theorem
- discrete random variables

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

## Offered by

## Start working towards your degree

## Syllabus - What you will learn from this course

**2 hours to complete**

### Start Here!

**2 hours to complete**

**7 hours to complete**

### Descriptive Statistics and the Axioms of Probability

**7 hours to complete**

**7 hours to complete**

### Conditional Probability

**7 hours to complete**

**8 hours to complete**

### Discrete Random Variables

**8 hours to complete**

**9 hours to complete**

### Continuous Random Variables

**9 hours to complete**

## Reviews

- 5 stars75.25%
- 4 stars12.37%
- 3 stars2.06%
- 2 stars2.06%
- 1 star8.24%

### TOP REVIEWS FROM PROBABILITY THEORY: FOUNDATION FOR DATA SCIENCE

Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.

This is a great course on probability. Although I felt like it was too easy and should include more PDFs (such as Beta and Gamma) and random variable transformations.

The instructor is very good, more examples need to be added, there are mistakes in the evaluation

This is an excellent course to review foundational probability concepts. The instructor speaks clearly and goes through examples thoroughly for each concept.

## About the Data Science Foundations: Statistical Inference Specialization

## Frequently Asked Questions

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