UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: AST 520
2. Units:
See upper and lower division undergraduate course definitions.
3. College:
CEFNS
4. Academic Unit:
Physics and Astronomy
5. Student Learning Outcomes of the new course. (Resources & Examples for Developing Course Learning
Outcomes)





Students will understand the premise of astroinformatics and understand its importance in
the field of cutting edge astronomy research
Students will learn computational approaches to allow them to carry out astroinformatics
projects
Students will learn statistical approaches that are useful in evaluating astroinformatics
projects.
Students will learn how to define astroinformatics research projects
Students will carry out independent term projects that include an aspect of original
research that will allow them to refine their learned skills and approaches.
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).
This course is a required core class for the new PhD program in astronomy and planetary
science. It will provide students with the essential foundation for research in astroinformatics.
7. Effective BEGINNING of what term and year?
See effective dates calendar.
Fall 2015
8. Long course title: ASTROINFOMATICS: BIG DATA IN ASTRONOMY
(max 100 characters including spaces)
9. Short course title: ASTROINFOMATICS
(max. 30 characters including spaces)
10. Catalog course description (max. 60 words, excluding requisites):
This course provides training in the fundamentals of astroinformatics: applying “big data”
techniques to research topics in astronomy. Course material will include case studies of
astroinformatics projects that exist presently and that are coming in the future; tutorials in
computational approaches; exposure to relevant statistical approaches; and training in
Effective Fall 2012
creating informatics research topics. The course will conclude with a term project in which
students will apply the skills they have learned to existing data sets.
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.
Astronomy; Ph.D.
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
14. Grading option:
Letter grade
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
Bachelor’s degree in physics,
18. Prerequisites:
astronomy, computer science
If prerequisites, include the rationale for the prerequisites.
A bachelor’s degree in physics or astronomy provides the essential background for success
in the course.
19. Co requisites:
If co requisites, include the rationale for the co requisites.
20. Does this course include combined lecture and lab components?
Yes
No
If yes, include the units specific to each component in the course description above.
Drs. Trilling, Barlow, Koerner,
21. Names of the current faculty qualified to teach this course: Tegler
Effective Fall 2012
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
11/19/2014
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
EXTENDED CAMPUSES
Effective Fall 2012
Reviewed by Curriculum Process Associate
Date
Approvals:
Academic Unit Head
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
AST 520 – ASTROINFORMATICS
General Information
 CEFNS Department of Physics and Astronomy
 AST 520 (Astroinformatics)
 Semester: Fall/Spring 201X
 Meeting Time: MWF XX (3 Credit Hours)
 Location: Physical Sciences Bldg 19, rm 218
 Instructor(s): Dr. David Trilling, Dr. Nadine Barlow, Dr. David Koerner, Dr. Stephen Tegler
 Office address: Physical Sciences (bldg 19) XXX
 Office hours: XXX or by appointment
 Office Phone: 928-523-XXXX
Course Prerequisites:
Bachelor’s degree in physics, astronomy, computer science, or instructor’s permission
Course Description:
This course provides training in the fundamentals of astroinformatics: applying “big data” techniques
to research topics in astronomy. Course material will include case studies of astroinformatics projects
that exist presently and that are coming in the future; tutorials in computational approaches; exposure
to relevant statistical approaches; and training in creating informatics research topics. The course will
conclude with a term project in which students will apply the skills they have learned to existing data
sets.
Course Objectives and Student Learning Outcomes:
 Students will understand the premise of astroinformatics and understand its importance in the field
of cutting edge astronomy research
 Students will learn computational approaches to allow them to carry out astroinformatics projects
 Students will learn statistical approaches that are useful in evaluating astroinformatics projects.
 Students will learn how to define astroinformatics research projects
 Students will carry out independent term projects that include an aspect of original research that
will allow them to refine their learned skills and approaches.
Course structure/approach:
This course will combine lectures with tutorial sessions that allow for hands-on practice of the
techniques presented in the class. There will be homework assignments as well as an independent
term project that the students will present to the class and instructor at the end of the semester.
Textbook and required materials
Statistics, Data mining, and Machine Learning in Astronomy: A practical Python guide for the analysis
of survey data (Z. Ivezic et al.), Princeton University Press (also available as an eBook)
Recommended optional materials/references:
Learning Python, 5th edition (M. Lutz), O’Reilly Media
Effective Fall 2012
Python for Data Analysis (W. McKinney), O’Reilly Media
http://www.astroml.org/ -- Machine Learning and Data Mining for astronomy (web page and tutorial)
Course outline:
Week 1 – Introduction to Astroinformatics; case studies and examples
Week 2— Astronomy introduction: Why “big data”?
Week 3 – Computational introduction: Why “big data”?
Week 4 – Computer tutorials I: Python practice
Week 5 – Computer tutorials II: Databases
Week 6 – Computer tutorials III: Machine learning
Week 7 – Statistics I: Definitions and practice
Week 8 – Statistics II: Bayesian approaches, model fitting
Week 9 – Time series analysis
Week 10 – How to build and test a sensible astroinformatics project
Week 11 – Future data sets and creating research projects
Week 12 – Visualization of astroinformatics projects
Week 13 – The future of astroinformatics and how to prepare
Week 14, 15 – Presentations of student research projects
Assessment of Student Learning Outcomes:
 Three quantitative homework assignments will be assigned during the semester. These will
have an emphasis on practicing skills learned in the class.
 Each student will complete an independent research project that builds on the skills learned in
this class.
 Each student will give an oral presentation on the independent research project carried out for
this course.
Grading System- Letter Grade will be calculated as follows:
 Homework assignments 15% each
 Research project 45%
 Class participation (attendance, asking questions, work during tutorial sessions, etc.) 10%
Final grades will be determined by the following normalized percentages:
A = 90+, B = 80-89, C = 70-79, D = 60-69, F<60
Course policy:
 Attendance – class attendance is required.
 All work must be carried out individually.
Statement on plagiarism and cheating – plagiarism and/or homework copying will result in an
automatic F for that assignment. Repeated instances will result in a letter grade of “F” for the course.
See NAU policies at: http://www4.nau.edu/avpaa/UCCPolicy/plcystmt.html.
Effective Fall 2012