Three-Toed Sloth

Course Announcement: 36-350, Statistical Computing

Since the semester begins on Monday, I might as well admit to myself that I am, in fact, teaching a new class:

36-350, Statistical Computing

Instructors: Cosma Shalizi and Vincent Vu

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.

Pre-requisites: This is an introduction to programming for statistics students. Prior exposure to statistical thinking, to data analysis, and to basic probability concepts is essential, as is some prior acquaintance with statistical software. Previous programming experience is not assumed, but familiarity with the computing system is. Formally, the pre-requisites are "Computing at Carnegie Mellon" (or consent of instructor), plus one of either 36-202 or 36-208, with 36-225 as either a pre-requisite (preferable) or co-requisite (if need be).

Further details, subject to change, at the class website. Teaching materials will definitely be posted there, and may be posted here.

(For tedious reasons, this class has the same number as the data-mining class I've taught previously; that course is now numbered 36-462, and will be taught in the spring by somebody else, while I'll be returning to 36-402, advanced data analysis.)

Corrupting the Young; Enigmas of Chance; Introduction to Statistical Computing