Revised September 2010
NORTH CAROLINA STATE UNIVERSITY
GRADUATE COURSE ACTION FORM
NOTE: Click once on shaded fields to type data. To check boxes, right click at box, click “Properties”, and click “Checked” under Default Values.
DEPARTMENT/PROGRAM
Statistics
COURSE PREFIX/NUMBER
ST555
TYPE OF PROPOSAL
New Course
Drop Course
PREVIOUS PREFIX/NUMBER
COURSE TITLE
Statistical Computing I
ABBREVIATED TITLE
STAT COMPUTING I
SCHEDULING
Fall
Every Year
COURSE OFFERED
CREDIT HOURS
3
Spring
Alt. Year Odd
Course Revision
Dual-Level Course
Summer
Alt. Year Even
PREREQUISITE(S)
Other
BY DISTANCE EDUCATION ONLY
ON CAMPUS ONLY
BOTH ON CAMPUS AND BY DISTANCE EDUCATION
GRADING
ABCDF
Abbreviated Title
Credit Hours
S/U
CONTACT HOURS: Lecture/Seminar 3 Laboratory/Studio
REPEAT FOR CREDIT: YES
No X
INSTRUCTOR NAME: John F Monahan
TITLE: Professor
GRADUATE FACULTY STATUS: Associate
Full
ANTICIPATED ENROLLMENT
REVISION
Content
Prefix/Number
Title
Contact Hours
Grading Method
Research/Independent Study
Pre-Corequisites
Restrictive Statement
Description
Scheduling
On Campus:
Per semester 28
Multiple sections Yes
Distance Ed:
Anticipated Enrollment 15
No
Max. per Section 28
Maximum Enrollment 20
ST311 or ST305 or ST507 or ST511 or ST513
COREQUISITE(S)
PRE/COREQUISITE FOR
ST556
REQUIRED
CURRICULA/MINOR
PROPOSED EFFECTIVE DATE
Certificate in Applied Statistics, Certificate in Statistical Computing
8/13
APPROVED EFFECTIVE DATE __________________________
CATALOG DESCRIPTION IN CONCISE FORM MEANINGFUL TO STUDENT (INCLUDE RESTRICTIVE STATEMENT AT END OF DESCRIPTION; LIMIT TO TOTAL OF 80
WORDS): 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. Student must have regular access to computer for homework and class exercises. Credit for both
ST445 and ST555 is not allowed.
DOCUMENTATION REQUIRED
Please number all document pages
Course Justification
Proposed Revision(s) with
Justification
Enrollment for Last 5 Years
VERIFICATION/REQUEST BY: The course syllabus has been developed and is in
conformance with the requirements of the Provost’s website.
____________________________________________________________________________
Instructor or Preparer
Date
_____________________________________________________________________________
Department Head/Director of Graduate Programs
Date
Consultation with other Departments
ENDORSED BY:
Student Learning Outcomes
Evaluation Methods and Weighting
_____________________________________________________________________________
Chair, College Graduate Studies Committee
Date
Explanation of Differences for Duallevel Courses
_____________________________________________________________________________
College Dean(s)
Date
Resource Statement
APPROVED:
Topical Outline and Time Devoted
____________________________________________________________________________
Dean of the Graduate School
Date
INSTRUCTIONS
Provide the following information. If additional table rows are needed place cursor at location, select Table, Insert, Rows Above or
Rows Below. Please limit your submission to 4 pages using 10-point font.
I. Course Justification (Explain the need for the course and its place in the curriculum in terms of the educational needs and interests of the students for
whom the course is intended):
Computing skills have become increasingly important in our graduate curriculum in Statistics. At the same time, most incoming
students have meager computer skills. As we revise our Master’s degree curriculum, we see an increased need to train these
students to handle the scale and scope of the data facing modern statisticians (aka ‘Big Data’). We see ST555 as the course to
equip our students with basic data handling skills in SAS and R for our Master’s degree courses. Additionally, ST555 prepares
the foundation for ST556 (Statistical Computing II) where students are trained to handle complicated data sources and automated
statistical analysis. We also see these two courses as the core of our Statistical Computing Certificate program.
ST555 differs from the undergraduate ST445 in the inclusion of instruction in the R language.
II. Proposed Revisions with Justification (Briefly list the changes and the justification for each):
Revision
Justification
III. Enrollment for Last Five Years (Enter data – look up at R&R website for either existing course number or special topics number as applicable. If
not offered, indicate n/a. If previously offered as special topic, indicate designation after number enrolled [e.g. 17 - XX 592B]):
Academic Year
Fall
Spring
2012-2013
Summer
6-ST590-001
IV. Consultation with Other Departments (Consultation is needed whenever there is a possibility of content duplication or when establishment or
dropping would affect other programs . List all departments and individuals contacted, and a summary of any statements of objection, non-objection, or support.
Consultation should include Program Director or Department Head. A copy of the entire document/communication should also be sent to the Graduate School as a
separate document.)
Department
Computer Science
Contact Name
Douglas Reeves
Statement
“no objections or concerns”
V. Student Learning Outcomes. By the end of the course, the students will be able to:
1)
2)
3)
4)
5)
6)
Write SAS code to read simple data files and spreadsheets
Convert a single record to multiple SAS observations when needed
Merge and set SAS datasets together properly
Create a user-written format as appropriate
Do common vector statistical calculations in R
Write a function that creates a likelihood function which is then maximized in R
2
VI. Student Evaluation Methods (List types of evaluation [tests, exam, papers, homework, etc.] and % weighting normally anticipated) :
Evaluation Method
Quizzes – 3
Class Exercises
Homework
Weighting for Graduate Course (%)
Weighting for Undergraduate Version –
if Dual Level (%)
3x15% = 45%
15%
40%
VII. Explanation of Differences for Dual-Level Course (Explain differences in content, expectations, and outcomes for graduate level version of
dual-level course and indicate evaluation above):
VIII. Resource Statement (New courses only. Indicate the resource requirements of this course and the source(s) of those resources.)
This course will be taught by existing faculty. This course will normally be taught in the Statistics Instructional Computing Lab
(SAS 1107) for hands-on training of students. No additional resources will be required.
IX. Topical Outline of Course and Time Devoted to Each Topic (Definition should be adequate to allow understanding of the course content.
Indicate time measure used, e.g. weeks, 50 min. lectures, 75 min. lectures, etc.) :
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 1
5 multiple records to one obs, one record to multiple obs, arrays
6 reading spreadsheet files
7 set, proc append, sorting, Quiz 2
8 formats, permanent SAS datasets
9 merging datasets
10 using datasets from proc's, Quiz 3
11 Basics of R, vectors, seq and rep functions
12 vector calculations, constructing matrices, recycling
13 creating R functions, loops, conditionals
14 lists, data frames, basic optimization
15 reading data from files, R functions and environments
3