Undergraduate Course: Computational Modelling for Geosciences (EASC09035)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 9 (Year 3 Undergraduate) |
Credits | 10 |
| Home subject area | Earth Science |
Other subject area | Geosciences |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Computational methods and modelling are widely used in Geosciences to interpret data and understand parts of the Earth System. Many scientists use interpreted languages with integrated plotting tools which allow them to be very productive. Students will learn and use Python and some additional libraries which is an interpreted language with plotting and data manipulation tools. They will also learn some basic Linux skills.
The latter 6-and-a-half weeks of the course teach numerical methods. These methods would use the Python taught in the first part of the course and be applied to simple Geoscience modelling problems. The numerical methods part of the course has three aims.
1) Develop student's knowledge of numerical methods.
2) Give the students an environment in which to develop their software skills.
3) Give students an appreciation of computational modelling. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 1, Available to all students (SV1)
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Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Class Delivery Information |
3 hour(s) per week for 10 week(s)
Tutorial week 11 |
| Course Start Date |
15/09/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 15,
Seminar/Tutorial Hours 1,
Supervised Practical/Workshop/Studio Hours 15,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
65 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | Computational Modelling for Geosciences | 1:30 | |
Learning Outcomes
On completion of this course, the student will be able to:
1. A comprehensive and integrated overview of numerical methods used in Geosciences so that students gain the following.
1. An ability to use interpreted language (Python and Numpy & matplotlib libraries) to apply numerical methods to problems in Geosciences.
2. 2. An ability to use interpreted language (Python and extensions) to visualise Geoscience data.
3. 3. An understanding of basic numerical methods:
a) linear-algebra;
b) methods for solving one dimensional ordinary differential equations; and
c) an introduction to methods for solving two dimensional differential equations.
4. 4. A basic understanding of numerical stability, accuracy, convergence and computational complexity in numerical methods
5. 5. A knowledge of how to apply the techniques of computational modelling to simple Geoscience modelling problems. |
Assessment Information
Written Exam: 50%, Course Work: 50 %, Practical Exam: 0%.
The written exam focuses on the numerical methods part of the course and students are expected to answer two out of three questions in 90 minutes.
The assessed practical is a modelling exercise where students will be asked to model a relatively simple geophysical system and analyse the results of the simulations. Students will be asked to hand in a report and the report will be assessed on the quality of the report (writing, figures and interpretation) and on the software written. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Week 1: half session on introduction to Linux and outline of aims of course.
Weeks 1-4: three sessions on Python, Numpy and Matplotlib. Sessions in week 1 and week 4 are half sessions.
Weeks 4-6: two half sessions on numerical methods for Linear Algebra. The session in week 4 a half session.
Weeks 7-9: three sessions on numerical methods for solving ordinary differential equations. |
| Transferable skills |
Not entered |
| Reading list |
Numerical Methods
Otto and Denier: An Introduction to Programming and Numerical Methods in Matlab
Python
Downey: Think Python ¿ an introduction to Python and some software engineering ideas.
A Hands-On Introduction to Using Python in the Atmospheric and Oceanic Sciences. (This is more of an advanced text rather than a text to learn Python from as it assumes some existing programming knowledge). |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Comp_Mod |
Contacts
| Course organiser | Prof Simon Tett
Tel:
Email: Simon.Tett@ed.ac.uk |
Course secretary | Mr Ken O'Neill
Tel: (0131 6)50 8510
Email: koneill3@exseed.ed.ac.uk |
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