```UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: STA 570L
2. Units:
See upper and lower division undergraduate course definitions.
3. College:
CEFNS
1
Mathematics & Statistics
5. Student Learning Outcomes of the new course. (Resources & Examples for Developing Course Learning
Outcomes)
Upon completion of the course students will be able to:
1. Create vectors, data frames and lists.
2. Import data from external spreadsheets.
3. Apply common mathematical functions to data structures.
4. Use R to look up probabilities and quantiles associated with a wide variety of distributions.
5. Create graphs using the basic plotting commands in R. (Optionally using ggplot2)
6. Automate tasks using loops and decision statements.
7. Manipulate the results of an analysis function (e.g., lm) to obtain common residuals, coefficient
estimates, and confidence intervals.
8. Manipulate data frames to provide summary statistics using the apply, aggregate, and plyr commands.
6. Justification for new course, including how the course contributes to degree program outcomes,
or other university requirements / student learning outcomes. (Resources, Examples & Tools for Developing
Effective Program Student Learning Outcomes).
Our graduate statistical methods sequence, STA 570-571, taken by students in many disciplines,
makes consistent use of statistical software, especially R. Students enter the course with diverse
technological backgrounds, some with very little experience with programming or statistical packages,
while others are sufficiently skilled to easily pick up the essentials of R. Because there is no time in
STA 570 itself to bring everyone up to speed with R, we have recently been offering a successful
one-hour introduction to R under a university course line. It is now time to make this a regular course.
7. Effective BEGINNING of what term and year?
See effective dates calendar.
Fall 2015
8. Long course title: INTRODUCTION TO R
(max 100 characters including spaces)
9. Short course title: INTRODUCTION TO R
(max. 30 characters including spaces)
10. Catalog course description (max. 60 words, excluding requisites):
Effective Fall 2012
Provides an introduction to the software package R. Topics include the creation and
manipulation of data structures (vectors, data frames, and lists), using R as a suite of
statistical tables, graphing data, statistical model syntax, and simple programing constructs.
Prerequisite: Any 200-level or above STA course; OR Co Requisite: STA 570
11. Will this course be part of any plan (major, minor or certificate) or sub plan (emphasis)?
Yes
If yes, include the appropriate plan proposal.
No
12. Does this course duplicate content of existing courses?
Yes
No
If yes, list the courses with duplicate material. If the duplication is greater than 20%, explain why
NAU should establish this course.
13. Will this course impact any other academic unit’s enrollment or plan(s)?
Yes
No
If yes, describe the impact. If applicable, include evidence of notification to and/or response from
each impacted academic unit
Pass/Fail
Both
15. Co-convened with:
14a. UGC approval date*:
(For example: ESE 450 and ESE 550) See co-convening policy.
*Must be approved by UGC before UCC submission, and both course syllabi must be presented.
16. Cross-listed with:
(For example: ES 450 and DIS 450) See cross listing policy.
Please submit a single cross-listed syllabus that will be used for all cross-listed courses.
17. May course be repeated for additional units?
16a. If yes, maximum units allowed?
16b. If yes, may course be repeated for additional units in the same term?
Yes
No
Yes
No
Any 200-level or above STA course;
18. Prerequisites:
OR Co Requisite: STA 570
If prerequisites, include the rationale for the prerequisites.
The course will make consistent reference to statistical topics without explanation or development.
19. Co requisites:
STA 570
If co requisites, include the rationale for the co requisites.
See above; co-enrollment in STA 570 will serve as a satisfactory requisite for the course.
20. Does this course include combined lecture and lab components?
Yes
If yes, include the units specific to each component in the course description above.
Drs. Burch, St. Laurent,
21. Names of the current faculty qualified to teach this course: Sonderegger, Wang
Effective Fall 2012
No
22. Classes scheduled before the regular term begins and/or after the regular term ends may require
additional action. Review “see description” and “see impacts” for “Classes Starting/Ending
Outside Regular Term” under the heading “Forms”
http://nau.edu/Registrar/Faculty-Resources/Schedule-of-Classes-Maintenance/.
Do you anticipate this course will be scheduled outside the regular term?
Yes
No
23. Is this course being proposed for Liberal Studies designation?
If yes, include a Liberal Studies proposal and syllabus with this proposal.
Yes
No
24. Is this course being proposed for Diversity designation?
If yes, include a Diversity proposal and syllabus with this proposal.
Yes
Answer 22-23 for UCC/ECCC only:
No
FLAGSTAFF MOUNTAIN CAMPUS
Scott Galland
Reviewed by Curriculum Process Associate
01/07/2015
Date
Approvals:
Department Chair/Unit Head (if appropriate)
Date
Chair of college curriculum committee
Date
Dean of college
Date
For Committee use only:
UCC/UGC Approval
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
Effective Fall 2012
EXTENDED CAMPUSES
Reviewed by Curriculum Process Associate
Date
Approvals:
Date
Division Curriculum Committee (Yuma, Yavapai, or Personalized Learning)
Date
Division Administrator in Extended Campuses (Yuma, Yavapai, or Personalized
Learning)
Date
Faculty Chair of Extended Campuses Curriculum Committee (Yuma, Yavapai, or
Personalized Learning)
Date
Chief Academic Officer; Extended Campuses (or Designee)
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
Effective Fall 2012
Department of Mathematics & Statistics
PO Box 5717
Flagstaff, AZ 86011-5717
928-523-3481
928-523-5847 fax
www.math.nau.edu
COURSE SYLLABUS
STA 570L INTRODUCTION TO R
General Information
 Department of Mathematics and Statistics
 College of Engineering, Forestry, and Natural Sciences
 STA 570L – Introduction to R
 Offered in conjunction with STA 570, every semester beginning Fall 2015
 15 Clock hours, 1 credit hour
 Instructor: Dr. Derek Sonderegger
 Office Location: Adel Mathematics, Rm 173
 Office hours: M 9:30-10:30, T 2-3, W 1-2 Th 2-3 F 9:30-10:30
Course prerequisites: Any 200-level or above STA course; OR Co Requisite: STA 570.
Course description: Provides an introduction to the software package R. Topics include the creation
and manipulation of data structures (vectors, data frames, and lists), using R as a suite of statistical
tables, graphing data, statistical model syntax, and simple programing constructs.
Student Learning Expectations/Outcomes for this Course: Upon completion of the course, as student
will be able to:
1. Create vectors, data frames and lists.
2. Import data from external spreadsheets.
3. Apply common mathematical functions to data structures.
4. Use R to look up probabilities and quantiles associated with a wide variety of distributions.
5. Create graphs using the basic plotting commands in R. (Optionally using ggplot2)
6. Automate tasks using loops and decision statements.
7. Manipulate the results of an analysis function (e.g., lm) to obtain common residuals, coefficient
estimates, and confidence intervals.
8. Manipulate data frames to provide summary statistics using the apply, aggregate, and plyr
commands.
Course structure/approach: The course will consist of a weekly class period in which the daily topic
will be introduced along with a daily computer assignment. The bulk of the period will be devoted to
working the assignment using R. The students will be expected to read the appropriate section in the
class notes and/or book so that the classroom time is devoted to one-on-one student interactions.
Students will complete assignments outside the formal class session using R in our open computer
lab or via other available access methods (e.g., on a CEFNS server via a web interface).
Textbook and required materials: Possible texts to use for this course are:
Beginning R: The Statistical Programming Language by Mark Gardener
R Cookbook by Paul Teetor
Effective Fall 2012
R Graphics Cookbook by Winston Chang
Approximate course schedule by week:
1. R, RStudio, using R as a calculator
2. Creation and manipulation of vectors
3. Creating data frames and lists
4. Looking up probabilities, quantiles
5. Importing data from spreadsheets.
6. Basic plotting in R
7. Plotting - Using ggplot
8. Plotting - Using ggplot
9. Analysis – formulas, accessor functions for residuals, confidence intervals, etc.
10. Flow controls – decision statements, loops.
11. User defined functions
12. User defined functions
13. Using apply, aggregate, plyr to summarize data frames
14. Miscellaneous – Dates, spatial data, reading from data bases, calling C or Python
Assessment and Grading of Student Learning Outcomes:
Students will be assessed via successful completion of the weekly homework assignments.
Course policy:
Attendance policy – This policy may vary from instructor to instructor.
Make-up policy – This policy may vary from instructor to instructor.
University policies – Students are responsible for the following policies: Safe Environment, Students
with Disabilities, Institutional Review Board, Academic Integrity, and Academic Contact Hour. A copy
of these policies may be downloaded from the web site:
http://nau.edu/OCLDAA/_Forms/UCC/SyllabusPolicyStmts2-2014/.
Effective Fall 2012
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