 | Use this page to view syllabus information, learning objectives, required materials, and technical requirements for the module.
As a result of College adapting your modules to combine face-to-face on campus and online teaching and learning support, the breakdown of notional learning hours set out under the heading “Technical Requirements” below may not necessarily reflect how each module will be delivered this year. Further details relating to this will be made available by your department and will be updated as part of the student timetable. |
| PH 2150 - Scientific Computing Skills |
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Associated Term:
2024/25 Academic Session
Learning Objectives: Basics of numerical calculation on computers: Types of computing. Numerical, symbolic, procedural, object orientated.
Arrays and Matrices: Arrays for storing data, manipulating arrays. Using arrays and matrices (Eigenvector, inversion).
Plotting and visualization: Scatter plots, Histograms, Multi-dimensional plots.
Data analysis: Mean/mode/median; Variance, RMS, kertosis, moments; Fitting (least squares); Regression.
Advanced data analysis: Fourier series, Fourier transform, Smoothing.
Programming: Control structures (if, for, while, return etc). Functions, program layout
Simulation: Evaluation of simple and complex expressions. Monte Carlo. Numerical integration.
Differential equations: Difference methods. Leap-frog method. Runge Kutta.
Monte Carlo methods: Metropolis algorithm. Integrals, integral estimation
Linear equations: Solutions, eigenvalues. Diagonalisation. Factorisation.
Project report: example illustrating physics from 2nd year core.
Learning Outcomes:
familiar and fluent with some of the high-level data analysis packages within Python
able to plot multidimensional data in different ways (histograms, parametric plots)
able to write computer programs that demonstrate the concepts and equations behind physics covered in the 1st and 2nd years
able make comparisons between different fits and approximations
Required Materials: Click here for the reading list system Technical Requirements: The total number of notional learning hours associated with course are 150. These will normally be broken down as follows: 10 hour(s) of Lecture(s) across 10 week(s) 7 hour(s) of Seminars across 7 week(s) 2 hours(s) of Tutorials across 2 week(s) 81 hour(s) of Laboratory classes across 12 week(s) 50 hours of Guided Independent Study Formative Assessment: Problem sets with solutions - Students are provided with written feedback on all problem sets with solutions presented in feedback class Summative Assessment: Progress/ Engagement (20%) 20 hours Portfolio (80%) 80 hours |
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