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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course NUMA01F valid from Autumn 2018

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General
Aim
  • The aim of the the course is to give an introduction to computational programming in Python for postgraduate students without previous programming knowledge.
    Python is a modern scripting language with strong ties to Scientific Computing.
Contents
  • Basic programming concepts, data structures, conditional statements, functions and classes.

    Problem-solving using a few basic numerical methods associated with mathematics and physics.

    The basic functions and data types of the Python programming language: arithmetic operations, arrays of vectors, matrices, graphics functions, lists, tuples, dictionaries, file management.

    Use of modules such as NumPy, SciPy and Matplotlib

    The representation of floating point numbers and their implications for arithmetic

    Syntax: [for], [if-else], [while], list comprehensions, generators

    Nested functions, self-defined functions and modules

    Classes and inheritance applied to mathematical objects

    Tests and profiling
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to explain and use basic programming concepts, data structures, conditional statements.

    be able do describe the structure of a Python program.
Competences and Skills
  • For a passing grade the doctoral student must
  • be able to convert algorithms into programming code

    be able to visualise, interpret and critically assess numerical results

    be able to report solutions to problems and numerical results in speech, writing and graphic form

    be able to use appropriate terminology in a logical and well-structured manner

    be able organise, implement and orally present a major programming project in carried out in group.
Judgement and Approach
  • For a passing grade the doctoral student must
  • be able to critically analyse the programs produced by fellow students and assess alternatives to his or her own programming solutions
Types of Instruction
  • Lectures
  • Exercises
  • Project
Examination Formats
  • Miscellaneous
  • Reports on programming exercises during the course, and a major programming project to be completed in groups.
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
Selection Criteria
  • Arrival time for application. At most five phd students.
Literature
  • Fuhrer, C., Solem, J. & Verdier, O.: Scientific Computing with Python 3 - Second Edition. 2016. ISBN 9781786463517.
Further Information
Course code
  • NUMA01F
Administrative Information
  • 2018-11-15
  • Professor Thomas Johansson

All Published Course Occasions for the Course Syllabus

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