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Mathematical Software - course description

General information
Course name Mathematical Software
Course ID 11.9-WK-CSEEP-MS-S22
Faculty Faculty of Exact and Natural Sciences
Field of study computer science and econometrics
Education profile academic
Level of studies First-cycle studies leading to Bachelor's degree
Beginning semester winter term 2022/2023
Course information
Semester 6
ECTS credits to win 2
Course type optional
Teaching language english
Author of syllabus
  • dr Tomasz Małolepszy
Classes forms
The class form Hours per semester (full-time) Hours per week (full-time) Hours per semester (part-time) Hours per week (part-time) Form of assignment
Laboratory 30 2 - - Credit with grade

Aim of the course

The familiarization of the students with the capabilities of the mathematical software supporting the work of mathematicians and engineers (like SciPy).

Prerequisites

Computer Programming 1.

Scope

  1. Introduction and first steps. (2 hours)
  2. Vectors and matrices. (3 hours)
  3. Strings and character data. (4 hours)
  4. Special types of arrays. (4 hours)
  5. Elements of the programming. (4 hours)
  6. Test. (2 hours)
  7. Two- and three-dimensional graphics. (4 hours)
  8. Symbolic computation. (5 hours)
  9. Test. (2 hours)

Teaching methods

To illustrate the capabilities of the mathematical software, during laboratory classes students will write computer programs solving some mathematical problems. In addition, in order for students to become more skilled at using given mathematical software, lists of assignments will be provided.

Learning outcomes and methods of theirs verification

Outcome description Outcome symbols Methods of verification The class form

Assignment conditions

Learning outcomes will be verified through two tests consisted of exercises of different degree of difficulty. A grade, determined by the sum of points from these two tests, is a basis of assessment.

Recommended reading

1. Mark Lutz, David Ascher, Learning Python, 5th Edition, O'Reilly Media, Inc., 2013.

Further reading

1. Robert Johansson, Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, 2nd edition, Apress, 2018.

Notes


Modified by dr Tomasz Małolepszy (last modification: 29-12-2023 21:12)