From the webpage:
Description
Computational data analysis is an essential part of modern statistics. Competent statisticians must not just be able to run existing programs, but to understand the principles on which they work. They must also be able to read, modify and write code, so that they can assemble the computational tools needed to solve their data-analysis problems, rather than distorting problems to fit tools provided by others. This class is an introduction to programming, targeted at statistics majors with minimal programming knowledge, which will give them the skills to grasp how statistical software works, tweak it to suit their needs, recombine existing pieces of code, and when needed create their own programs.
Students will learn the core of ideas of programming — functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction — through writing code to assist in numerical and graphical statistical analyses. Students will in particular learn how to write maintainable code, and to test code for correctness. They will then learn how to set up stochastic simulations, how to parallelize data analyses, how to employ numerical optimization algorithms and diagnose their limitations, and how to work with and filter large data sets. Since code is also an important form of communication among scientists, students will learn how to comment and organize code.
The class will be taught in the R language.
Carnegie Mellon
Statistics this Fall at Carnegie Mellon. Lectures, Homework, Labs.
Whether you simply missed statistics or it has been a while, this looks very good.