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Unit information: Numerical Methods and Programming in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Numerical Methods and Programming
Unit code EASC20041
Credit points 10
Level of study I/5
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Nick Teanby
Open unit status Not open

EASC10007 Computing for Earth Scientists



School/department School of Earth Sciences
Faculty Faculty of Science

Description including Unit Aims

In this unit students learn how to extract information from numerical data using rigorous numerical and mathematical methods, which will be implemented using the Matlab programming and plotting software. This software is a powerful tool that is used extensively throughout academia and industry.

The skills learnt will be very useful for project and practical work during all degree programmes and for work in diverse careers. The programming skills acquired during this unit build upon and extend those acquired in Computing for Earth Scientists (EASC10007).

Intended Learning Outcomes

Students will develop:

  • An understanding of the importance of measurement errors when analysing data.
  • An understanding of wide-ranging numerical data analysis techniques.
  • An ability to program in Matlab.
  • An ability to analyse data and model output using Matlab

Teaching Information

Learning will be entirely through independent practical exercises, delivered via Blackboard.

Help and feedback on progress will be provided during timetabled weekly help sessions.

Assessment Information

100% coursework comprising analysis of a scientific dataset and creation of a multi-panel figure incorporating elements of the course ILOs. The figure(s) produced from the data analysis will be combined into a single page PDF for assessment. The Matlab code used to generate the figures will also be submitted for plagiarism checking.

Reading and References


  • Trauth. "Matlab recipes for Earth Scientists". 3rd edition, Springer

Recommended (Matlab-based):

Further reading / Reference texts (methods only):

  • Bevington and Robinson. "Data reduction and error analysis for the physical sciences". 3rd edition. McGraw-Hill