Learn to develop a set of principles for your data science and software development projects.

Learn to visualize multivariate datasets using lattice graphics.

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

Publicly release data sets with a differential privacy guarantee.

Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.

Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.

Learn defensive programming in R to make your code more robust.

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

Predict employee turnover and design retention strategies.

Learn to automate many common file system tasks and be able to manage and communicate with processes.

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.

Learn to detect fraud with analytics in R.

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.