2020/21 Undergraduate Module Catalogue

SOEE2250 Numerical Methods and Statistics

10 creditsClass Size: 38

Module manager: Dr Stephen Stackhouse
Email: S.Stackhouse@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2020/21

Pre-requisites

SOEE1160

Comp & Prog in Geosciences

This module is not approved as a discovery module

Module summary

Numerical methods and statistics play a fundamental role in all aspects of geophysical research. This module teaches students the basic techniques needed for careful analysis of experimental data and solving numerical problems.The students will be introduced to the mathematical methods in the lectures and translate these into Python programs in the practical sessions.

Objectives

The objective of the first part of the module is to introduce students to the most common numerical methods, both theory and implementation in Python. The objective of the second part is for students to learn how to handle and report data with uncertainties in an appropriate manner.

Learning outcomes
On completion of this module students should be able to:

1. Design and implement computer programs to solve numerical problems that would be impossible or time-consuming to complete by hand, and understand their limitations.

2. Derive expressions for simple numerical methods.

3. Solve mathematical problems via recall or use of the appropriate numerical method for finding the roots or optima of functions, solving linear systems of equations, interpolating values, performing numerical integration and differentiation, and solving initial-value and boundary-value problems.

4. State the advantage and disadvantages of different numerical methods and, where appropriate, conditions required for convergence.

5. Handle and report data with uncertainties in an appropriate manner.

Syllabus

Numerical methods
1. Errors in Numerical Methods
2. Finding Roots
3. One-Dimensional Optimisation
4. Linear Systems - Direct Methods
5. Linear Systems - Iterative Methods
6. Interpolation
7. Numerical Integration
8. Numerical Differentiation
9. Initial-Value Problems
10. Boundary-Value Problems.
Statistics
1. Error Representation
2. Error Propagation
3. Statistical Analysis
4. Normal Distribution
5. Least-Squares Fitting.

Teaching methods

Delivery type	Number	Length hours	Student hours
Tutorials	20	1.00	20.00
Lecture	15	1.00	15.00
Practical	10	2.00	20.00
Private study hours			45.00
Total Contact hours			55.00
Total hours (100hr per 10 credits)			100.00

Private study

Students will be expected to spend time reviewing course material, completing the problems sets and practicals, and practising computer programming.

Opportunities for Formative Feedback

Problems sets will be given out in the lectures and reviewed in subsequent lectures. Students will receive oral and written feedback on their computer programs.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course Assessment	Computer programming assessment	40.00
Total percentage (Assessment Coursework)		40.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Unseen exam	1 hr 30 mins	60.00
Total percentage (Assessment Exams)		60.00

Students who fail the module will be required to resit any failed component.

Reading list

The reading list is available from the Library website

Last updated: 10/08/2020 08:46:33

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