This course provides a basic introduction to Bayesian statistics in R.

Professor, Bowling Green State University

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Assistant Professor at Smith College

This course was designed to get you up to speed with the most important and powerful methodologies in statistics.

DeWitt Wallace Professor of Mathematics, Statistics, and Computer Science at Macalester College

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Assistant Professor of Statistics at Reed College

Learn the language of data, study types, sampling strategies, and experimental design.

Associate Professor at Duke University

Learn the core techniques necessary to extract meaningful insights from time series data.

Assistant Professor at Cornell University

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Professor of Statistics at the University of Pittsburgh

Learn the practice of drawing conclusions about a population from a sample of data, a process known as statistical inference.

Professor at Pomona College

In this follow-up course, you will expand your stat modeling skills from part 1 and dive into more advanced concepts.

Learn how to visualize time series visualization in R, then practice with a stock-picking case study.

Quantitative Trader and creator of the R Trader blog (www.thertrader.com)

Learn how to make predictions about the future using time series forecasting in R.

Professor of Statistics at Monash University

Learn to visualize multivariate datasets using lattice plotting.

Professor at the Indian Statistical Institute, a member of R-Core, and the creator of lattice.