Course Highlights
  • 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.
Curriculum

5 Topics
Earn Academic Credit for your Work!
Course Support
Course Resources and Reading
Introduce Yourself
Introduction to Jupyter Notebooks and R

8 Topics
Intro to Probability
Axioms of Probability
Counting: Permutations and Combinations
Intro to Probability
Introducing the formula sheet for this course
Homework: Descriptive Statistics and the Axioms of Probability
Homework: Axioms of Probability
Guided Exploratory Ungraded Lab

6 Topics
Conditional Probability and Bayes Theorem
Independent Events
Conditional Probability and Bayes Theorem
Homework: Conditional Probability
Homework: Bayes Theorem
Guided Exploratory Ungraded Lab

8 Topics
Discrete Random Variables
Bernoulli and Geometric Random Variables
Expectation and Variance
Binomial and Negative Binomial Random Variables
Discrete Random Variables
Homework: Discrete Random Variables
Homework:  Calculations with Discrete Random Variables
Guided Exploratory Ungraded Lab

9 Topics
Continuous Random Variables
The Gaussian (normal) Random Variable Part 1
The Normal Random Variable Part 2
The Poisson and Exponential Random Variables
Continuous random variables
Normal Random Variable
Homework: Continuous Random Variables
Homework: Continuous Random Variables and Normal Random Variables
Guided Exploratory Ungraded Lab

6 Topics
Covariance and Correlation
More on Expectation and Variance
Jointly Distributed Random Variables
Covariance and Correlation
Homework: Joint Distributions and Covariance
Homework: Calculations of Covariance and Correlation in Various Examples

6 Topics
Introduction to the Central Limit Theorem
Central Limit Theorem Examples
Central Limit Theorem
Homework: Central Limit Theorem
Homework: Working with Normal Random Variables and the CLT
Guided Exploratory Ungraded Lab

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Probability Theory: Foundation for Data Science

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