Data Academy Subscription > Data with R > Probability Distributions
Course Description
In this course, you will study the use of probability distributions as a form of unsupervised learning. You will learn the basic terminology of probability distributions; explore kernel density estimation; and examine the KDE. In this course, you will study the use of probability distributions as a form of unsupervised learning. You will learn the basic terminology of probability distributions; explore kernel density estimation; and examine the Kolmogorov-Smirnov Test. You will generate different distributions in R, estimate probability distribution functions for new datasets in R, and compare the closeness between two different samples of same or different distributions.
Requirements
  • This course assumes prior programming knowledge in R and a basic knowledge of mathematical concepts, including exponents, square roots, means, and medians.
Instructor:
Bert Gollnick
Bert Gollnick has a Diploma in Aerospace Engineering and has pursued MSc in Economics. He is also a Data Scientist and has 10 years of experience in R. He is also an online trainer for Data Science and Machine Learning.