Course: Python for Applied Science

» List of faculties » FM » NTI
Course title Python for Applied Science
Course code NTI/PAV
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory, Compulsory-optional
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Lecturer(s)
  • Březina Jan, doc. Mgr. Ph.D.
Course content
Lectures: 1) IPython - basic calculation, combining with text and equations 2) Python - basic syntax, data types 3) matplotlib - 2D graphs 4) Sympy - symbolic calculation, Numpy - multi-dimensional arrays, vectorization 5) SciPy - overview, linear algebra, interpolation, quadrature, ODE 6) SciPy - algebraic equations, optimization, statistics 7) Parallel programming, threads 8) Parallel programming, MPI 9) Paraview (basics, example of embeded Python) 10) MayaVi (3D vizualiztion) Tutorials: 1) Python, IPython - work with basic data types, files 2) Introduction of project topics, modules 3) Matplotlib 4) SymPy, NumPy 5) SciPy 6) Metacentrum/Hydra - get access, job execution 7) Parallel programming, threads. 8) Distributed computing, MPI, PBS 9) Presentation of selected works: Matplotlib 10) Presentation of selected works: SymPy, SciPy

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing), Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments), Laboratory work
  • Class attendance - 40 hours per semester
  • Preparation for laboratory testing; outcome analysis - 26 hours per semester
  • Preparation for formative assessments - 18 hours per semester
  • Preparation for credit - 25 hours per semester
  • Home preparation for classes - 20 hours per semester
  • Presentation preparation (report) - 20 hours per semester
Learning outcomes
The course provides an introduction into the Python language emphesizing its usage in the scientific computing as a glue tool and scripting language for the specialized numerical software. The closing part of the course is an introduction to the paralell programing and high-performance computing.
After the course the student should be able to program its own specialized scripts, apply basic paralellization techniques, and executing jobs on paralellel computers.
Prerequisites
Basic knowledge of any programming language.

Assessment methods and criteria
Oral presentation of self-study

The graded credit is granted according to the activity at the tutorials and the quality of the seminal work.
Recommended literature
  • Jupyter and the future of IPython ? IPython [online]. [cit. 2016-01-08]. Dostupné z: http://ipython.org/.
  • ParaView/Python Scripting - KitwarePublic [online]. [cit. 2016-01-08]. Dostupné z: http://www.paraview.org/Wiki/ParaView/Python_Scripting.
  • Ponořme se do Pythonu 3 [online]. [cit. 2016-01-08]. Dostupné z: http://diveintopython3.py.cz/index.html.
  • Cyrille Rossant. IPython Interactive Computing and Visualization Cookbook.
  • J. Elkner, A. B. Downey, Ch. Meyers. Učíme se programovat v jazyce Python 3 [online]. 2008, 2015 [cit. 2016-01-08]. Dostupné z: http://howto.py.cz/.
  • Mark Pilgrim. Ponořme se do Pythonu 3 [online]. [cit. 2016-01-08]. Dostupné z: http://diveintopython3.py.cz/index.html.
  • Švec. Seriál Létající cirkus [online]. Root.cz, 2015 [cit. 2016-01-08]. Dostupné z: http://www.root.cz/serialy/letajici-cirkus/.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Applied Sciences in Engineering (2016) Category: Special and interdisciplinary fields 2 Recommended year of study:2, Recommended semester: Summer