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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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DRPS : Course Catalogue : School of Geosciences : Earth Science

Undergraduate Course: Quantitative Methods in Earth Sciences (EASC09047)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 9 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryQuantitative and computational methods are widely used by industry and academic researchers in the Earth Sciences. This course will look at the many types of quantitative data that are encountered in Earth Sciences, how they can be interpreted and how they can be presented. Theory and practice will be combined in workshop classes which give an introduction to programming in Python, a widely-used open source language with powerful capabilities for scientific computing. Students will get to see the relevance of quantitative methods through the use of data from current research in the School. Numerical methods will be illustrated with examples of their use within research codes and professional software tools. The theory will aid understanding of quantitative concepts in other Honours courses such as Structural Geology, Chemical Geology and Hydrogeology. Numeracy and programming skills taught in this course are highly valued by employers in many sectors, including insurance, finance and exploration.
Course description 1. Programming concepts
Data types; reading and writing data; expressions; control constructs; and plotting.

2. Descriptive statistics
Data and errors; measures of central tendency and spread; measures of correlation; and probability distributions.

3. Analysis of data sequences
Examples of sequential data in earth sciences; trend detection; autocorrelation; semivariograms; and spectral analysis.

4. Analysis of spatial data
Examples of spatial data in earth sciences; interpolation; and contouring; directional data.

5. Analysis of multivariate data
Examples of multivariate data in earth sciences; multiple regression; cluster analysis; and principal component analysis.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed:
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesBasic calculus and linear algebra
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  60
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam: 0%, Course Work: 100 %, Practical Exam: 0%.
Mid-term Test (40%): An online set of multiple choice and short answer questions relating to material from Weeks 1 to 4 of the course.

The mid-term test will be conducted on Learn during the practical class in Week 5.

Data for the final assignment will be made available at the beginning of week 10 and the deadline for submission will be noon on the Friday of week 11.
Feedback Individual written feedback will be given through Learn on a formative exercise in week 3, the mid-term test in week 5 and the final assignment in week 11.

The practical classes provide opportunities for immediate verbal feedback on exercises from the lecturer and demonstrators. Demonstrations of solutions to common problems arising will be projected for the benefit of the whole class. A set of example solutions will be provided the week after each class.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Write computer programs to read, analyse and plot large datasets.
  2. Understand mathematical techniques for interpreting and relating sequential, spatial and multivariate data commonly used in earth sciences.
  3. Understand uncertainties in quantitative measurements and how they propagate into uncertainties in derived quantities.
Reading List
Davis, JC. Statistics and data analysis in geology. Wiley
McKillup, S, and Dyar, MD. Geostatistics explained: an introductory guide for earth scientists. Cambridge
Additional Information
Graduate Attributes and Skills Quantitative modelling of geological processes, analysis and visualization of large datasets, computer programming
Additional Class Delivery Information Lectures on Tuesdays at 12:10-13:00, Weeks 1 and 10 Practicals on Fridays at 13:10-16:00, Weeks 1-10

1. Programming concepts
Data types; reading and writing data; expressions; control constructs; and plotting.
2. Descriptive statistics
Data and errors; measures of central tendency and spread; measures of correlation; and probability distributions.
3. Analysis of data sequences
Examples of sequential data in earth sciences; trend detection; autocorrelation; semivariograms; and spectral analysis.
4. Analysis of spatial data
Examples of spatial data in earth sciences; interpolation; and contouring; directional data.
5. Analysis of multivariate data
Examples of multivariate data in earth sciences; multiple regression; cluster analysis; and principal component analysis.
KeywordsNot entered
Contacts
Course organiserDr Richard Essery
Tel:
Email: Richard.Essery@ed.ac.uk
Course secretaryMs Casey Hollway
Tel: (0131 6)50 8510
Email: Casey.Hollway@ed.ac.uk
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