MY559 Half Unit
Special Topics in Quantitative Analysis: Applied Statistical Computing
This information is for the 2014/15 session.
Teacher responsible
Dr Benjamin Lauderdale COL.8.10.
Availability
The course is available to all research students.
Pre-requisites
The course will assume knowledge of linear and logistic regression models, to the level covered in MY452.
Course content
The aim of the course is to introduce students to advanced analytic methods frequently used in leading-edge social research. The content of the course will change from year to year. In the 2014/2015 session, this course will cover computer programming for social science research and some advanced data analysis methods that require significant computation. The main topics covered are basic programming and data structures, web scraping and regular expressions, nonparametric density estimation and regression, additive models, the lasso, cross-validation, the bootstrap, and permutation/randomization inference. Lectures, class exercises and homework will be based on the use of the R statistical software package, but will assume no background knowledge of that language.
Teaching
20 hours of lectures and 10 hours of computer workshops in the LT.
Formative coursework
Exercises from the computer classes can be submitted for marking.
Indicative reading
Matloff, N. 'The Art of R Programming'
Shalizi, CR. 'Advanced Data Analysis from an Elementary Point of View'
Assessment
Coursework (100%, 4000 words).
Key facts
Department: Methodology
Total students 2013/14: 5
Average class size 2013/14: 4
Lecture capture used 2013/14: No
Value: Half Unit
