Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Learn to design and run your own Monte Carlo simulations using Python!

Learn to analyze, plot, and model multivariate data.

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

Learn fundamental concepts of mathematical probability to prepare for a career in the growing field of information and data science.

Learn about probability distribution models, including normal distribution, and continuous random variables to prepare for a career in information and data science.