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  6. PX3017: RESEARCH AND COMPUTING SKILLS

PX3017: RESEARCH AND COMPUTING SKILLS (2016-2017)

Last modified: 28 Jun 2018 10:27


Course Overview

This course introduces mathematical and computational methods. The first part is 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. The second part teaches advanced mathematical methods using examples from Physics; for example multivariable calculus and Maxwell's equations, or ODE and partial differential equations in classical and quantum mechanics. There will be a one week career strategies module separating the two.




Course Details

Study Type Undergraduate Level 3
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus None. Sustained Study No
Co-ordinators
  • Dr Silke Henkes
  • Dr Francesco Ginelli

Qualification Prerequisites

None.

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

  • One of MA1002 Calculus a (4) (Passed) or MA1005 Calculus 1 (Passed) or MA1007 Introductory Mathematics 1 (Passed) or MA1508 Calculus II (Passed) or MA Natural Philosophy (Studied)
  • Any Undergraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

  • PX3011 Research Skills in Physics (Studied)
  • PX3015 Research and Computing Skills (Studied)

Are there a limited number of places available?

No

Course Description


'This course will introduce computing skills including programming and numerical analysis methods and key mathematical skills commensurate with those required by other honours courses such as Electricity and Magnetism. The course is divided into two halves. In the first half, 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 this part. In the second half students will revise and develop key mathematical skills including their ability to cope with vector calculus, differential and integral equations and linear algebra. Topics such as Stokes’s, Green’s and Gauss’s theorems will be explored and applications discussed.

Further Information & Notes

Priority entry to intending Physics honours students.

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

1st Attempt: 1st Attempt: In-course assessments (100%) made up of tutorial questions and one mid-term exam (50%) and computer programme assessments (50%)

Resit: Opportunity to resubmit any missed assessments.

Formative Assessment

Initial workshop on presentation skills formatively assessed; careers skills - feedback given, short "flash" assignments will be unassessed (eg. short blogs will receive comments). Formative assessment on continuing work within workshops, and guidance given.

Feedback

Feedback will be: provided by e-mail on formative assessments such as presentation skills given individually on careers skills provided as comments on blogs of "flash" assignments given informally throughout workshops and as required formally provided in writing on written (summative) assessments.

Course Learning Outcomes

None.
 

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