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

Learn to build pipelines that stand the test of time.

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

Learn how to work with streaming data using serverless technologies on AWS.

Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Sheets.

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Learn to solve increasingly complex problems using simulations to generate and analyze data.

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

Sharpen your knowledge in machine learning, and prepare for any potential question you might get in a machine learning interview in Python.

Learn to streamline your machine learning workflows with tidymodels.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn to analyze, plot, and model multivariate data.

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.