ST 555
Statistical Computing I
Summer 2013

Syllabus

This course will cover the mechanics of converting data from whatever form it may arrive and preparing it for processing by statistical software.  The first goal of this course will be the mastery of Base SAS programming, especially the DATA step.  The second goal of this course is an introduction to R programming.  This class will regularly meet in SICL, and the computing facilities will permit regular hands-on activities.  

The primary material for the course are the code files of demonstrations available at the course website and archived by week.  

For SAS, some suggested reference books are:

SAS Institute, SAS Certification Prep Guide (2nd ed), 2009

Frank C. DiIorio, SAS Applications Programming, Duxbury Press, 1991.

SAS Institute, Step-By-Step Programming with Base SAS Software, 2001.

Jay A. Jaffe, Mastering the SAS System, Thompson, 1996.

Lora Delwiche & Susan Slaughter, The Little SAS Book, SAS Institute 
(The third edition of this book is available online at    
  http://www2.lib.ncsu.edu/catalog/record/NCSU1858431 )
 
For R, a suggested reference is

Norman Matloff, The Art of R Programming, No Starch Press, 2011.

The prerequisite for this course is an introductory course in statistical methods.  Further training in Statistics will help in providing a perspective on why we may want to prepare the data in a particular fashion.

Homework and class assignments will be assigned from time to time and will count for about one half of the final grade.  There will likely be 2 quizzes during the semester, each counting for about 15% of the final grade.  The quizzes will be in-class and closed book.  The final will likely count about 20% of the final grade.

Auditors must earn a respectable D to gain recording of this course.



Typical Semester schedule -- Summer is 5 weeks


  Week    Topics 
   1     Basics of SAS: data step and procs, SAS datasets 
   2     Reading data: list, column, format; reading from files 
   3     data step language elements & structure, PDV, basic procs 
   4     loops, SAS functions, Quiz 
   5     multiple records to one obs, one record to multiple obs, arrays 
   6     reading spreadsheet files 
   7     set, append, sorting, Quiz 
   8     formats, permanent SAS datasets 
   9     merging datasets 
  10     using datasets from proc's 
  11     Basics of R, reading data & code 
  12     matrix/vector calculation, loops, forcing, recycling 
  13     R functions 
  14     basic matrix functions, apply 
  15     optimization methods