Probability & Statistics
Apply probability theory and statistical reasoning to data problems.
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After this skill you can…
- Calculate conditional probability and Bayes' theorem
- Interpret confidence intervals and p-values
- Apply common distributions (Gaussian, Bernoulli, Poisson)
Watch (10 videos)
Detecting Power Laws in Real-world Data | w/ Python Code
→ Apply statistical concepts to real-world data
Introduction to Probability: Student's T Distribution
→ Calculate the variance of the Student's T Distribution→ Determine the degrees of freedom for a given distribution→ Identify the differences between the Student's T Distribution and the normal distribution
Probability: Types of Distributions
→ Apply probability distributions to real-world problems→ Analyze data using statistical methods→ Understand AI safety concepts
Learn Probability & Statistics with Interactive Simulations
→ Calculate conditional probability→ Understand random variables and their applications
R Tutorial: Flipping coins in R
→ Understand probability distributions→ Model real-world events with probability
R Tutorial: Monty Hall
→ Analyze game theory problems→ Understand probability distributions
5 Probability Distributions you should know as a Data Scientist
→ Apply probability distributions to data science problems→ Understand the assumption of normality→ Use Monte Carlo simulations in practice
The Main Ideas behind Probability Distributions
→ Understand probability distributions→ Approximate probabilities using curves
Probability Theory Made Easy: From Basics to Advanced (with Memory Song!)
→ Calculate probability using additive and multiplicative laws→ Apply probability theory to real-world problems
Probability Part 2: Updating Your Beliefs with Bayes: Crash Course Statistics #14
→ Apply Bayes' theorem to update beliefs→ Analyze data using Bayesian statistics
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