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DRPS : Course Catalogue : School of Geosciences : Earth Science

Undergraduate Course: Research Methods in Physical Geography (RMPG) (EASC09053)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course is made of two components related to research methods. In semester one, lectures and practicals will provide training in the use of computer programming to analyse and visualise data using physical geography examples. In parallel, a series of workshops throughout the year will develop students' ability in generation and testing of scientific hypotheses. The course will culminate with the production of a research proposal in which students will describe the scientific motivation for their dissertation project, the main goal of the project and the methods they will use to achieve this goal.
Course description Syllabus

Programming and Data Analysis: Semester 1:
Week 1: No Practical/Demonstrator unavailable (do not meet)
Week 2 (Friday): Basic Programming
Week 3 (Friday): Data Visualisation
Week 4: No Practical/Demonstrator unavailable (do not meet)
Week 5 (Friday): Probability and sampling
Week 6 (Friday): Correlation and Regression
Week 7 (Friday): Online Programming Test
Week 8 (Friday): Time Series analysis
Week 9 (Friday): Spatial data analysis
Week 10 (Friday): Numerical solution of differential equations
Week 11 (Day TBC): Overview / Questions about coding project

Workshops for Research Design*: all year long. The milestones are:
Sem1 Wk2 (Friday): Introduction to research design workshops
Sem 1 Wk9: discussion of preliminary ideas in small groups
Sem 1 Wk11: 4th Year GPG dissertation conference
Sem 2 ILW: Mock peer-review interview based on assessment of students' proposals

*Note: specific dates and times for the workshops will be arranged via e-mail, possibly via a ┐doodle-poll┐.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 39, Supervised Practical/Workshop/Studio Hours 39, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 118 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %

Assessment breakdown:
Online programming test: 15%
Assignment- programming and data analysis: 35%
Dissertation Proposal: 50%

Assessment deadlines:
In-practical online test: Semester 1, Week 7
Programming assignment: Semester 1, Week 11
Dissertation Proposal: Semester 2, Week 8

For further information on deadlines please refer to the learn page.

Feedback The students will have the opportunity to engage with staff and demonstrators during the laboratory practicals. Students are encouraged to experiment with different software programming approaches and to ask the staff and demonstrator for guidance.

The workshops will have a number of staff on hand who will also provide verbal feedback on project ideas, presentation skills, proposal writing and other issues that come up. A mark, along with written feedback will be provided on the dissertation proposal.

The course staff are available for contact by email regarding questions about course and assessment material (for detailed questions, scheduled meetings may be more appropriate)
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Acquire skills in computer programming
  2. Develop an understanding of a range of data processing/analysis techniques and the ability to determine suitable data analysis approaches to test hypotheses
  3. Define a research question or a scientific hypothesis to test
  4. Search for literature and gather information on the topic
  5. Define a rigorous strategy for data collection and analysis
Reading List
Statistics and Data Analysis in Geology (Davis, JC. Wiley)

Geostatistics explained: an introductory guide for earth scientists (McKillup, S and Dyar, MD. Cambridge)

Walliman, N (2004) "Your undergraduate dissertation". London: Sage

Kneale, P (2011) "Study Skills for Geography, Earth and Environmental Science Students". Hodder Education.
Additional Information
Graduate Attributes and Skills Not entered
Additional Class Delivery Information The lab practical portion of the course will take place over 8 weeks in the Fall term. The practicals will cover the programming and data analysis component of the course. In these practicals, the staff member will give a brief overview of the current practical and its learning outcomes, and the students will then carry out the practical exercises in a hands-on fashion with a staff member and demonstrator standing by.
KeywordsProgramming,Data Analysis,Research Design,Dissertation
Course organiserDr Daniel Goldberg
Tel: (0131 6)50 2561
Course secretaryMs Ashley Stein
Tel: (0131 6)50 8510
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