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PX2016: COMPUTATIONAL METHODS IN PHYSICS (2022-2023)

Last modified: 31 Jul 2023 11:19


Course Overview

This course introduces computational methods in Physics. It consists of an introduction to programming, starting at basics such as variables, loops and conditional statements. This course is taught in Python, with an emphasis on modern programming concepts and data analysis skills.

Course Details

Study Type Undergraduate Level 2
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr M. Carmen Romano
  • Dr F. J. Perez-reche

What courses & programmes must have been taken before this course?

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

Are there a limited number of places available?

No

Course Description

This course will introduce computing skills including programming and numerical analysis methods. Students will develop programming and numerical analysis skills. Using Python, they will first learn the basics of programming, including loops, conditions and functions. Using this basis, they will then apply numerical algorithms to solve differential equations, eigenvalue problems and integration, and learn how to analyse and visualise data. An individual programming project concludes the course.


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

Design Project: Individual

Assessment Type Summative Weighting 60
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralApplyWork on developing your own mathematical programming project
ProceduralUnderstandBasic programming concepts: algorithms, loops, conditional statements, functions, etc
ProceduralUnderstandLinear algebra, random numbers and numerical integration
ProceduralUnderstandnumpy and matplotlib scientific libraries for data analysis and plotting

Class Test

Assessment Type Summative Weighting 40
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralUnderstandBasic programming concepts: algorithms, loops, conditional statements, functions, etc
ProceduralUnderstandLinear algebra, random numbers and numerical integration
ProceduralUnderstandnumpy and matplotlib scientific libraries for data analysis and plotting

Formative Assessment

There are no assessments for this course.

Resit Assessments

Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralUnderstandBasic programming concepts: algorithms, loops, conditional statements, functions, etc
ProceduralUnderstandnumpy and matplotlib scientific libraries for data analysis and plotting
ProceduralUnderstandLinear algebra, random numbers and numerical integration
ProceduralApplyWork on developing your own mathematical programming project

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