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Unit information: Numerical Methods and Programming in 2016/17

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Unit name Numerical Methods and Programming
Unit code EASC20041
Credit points 10
Level of study I/5
Teaching block(s) Teaching Block 4 (weeks 1-24)
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


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 fit models to data.
  • An ability to program in Matlab.
  • An ability to plot data and model output using Matlab

Teaching details

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

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

Assessment Details

Formative feedback will be given on practical exercises during weekly help sessions.

Summative assessment will comprise coursework (60%) and a 2 hour examination (40%).

  • The coursework component will consist of two exercises, based on a computer programme, to be submitted near the end of each teaching block. Clear assessment instructions including any limits on length will be given for each component prior to submission. Each piece of coursework is worth 30%.
  • The exam will be a mixture of multiple choice, numerical, and short answer questions.

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
  • Press et al. "Numerical Recipes in Fortran 77". 3rd edition.