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Online course materials for MATH20622

Programming with Python


Unit code: MATH20622
Credit Rating: 10
Unit level: Level 2
Teaching period(s): Semester 2
Offered by School of Mathematics
Available as a free choice unit?: N

Requisites

Prerequisite

Additional Requirements

Students should have taken (MATH10101 OR MATH10111)

Aims

To introduce students to the \algorithmic way of thinking" and to the basic programming concepts, laying strong foundations for computer-aided problem solving applicable in their later studies, as well as for some programming-related problems they may encounter in the future.

Overview

The course will focus on teaching basic programming mechanisms in Python and on writing programs based on basic mathematical algorithms, as well as creating algorithms from various problems' descriptions.

Most of the Python-specifc constructs will be avoided, to make students familiar with the concepts that they can easily apply to other programming environments they may encounter.

No prior computer programming knowledge is assumed.

Learning outcomes

On successful completion of this course, students will be able to translate existing algorithms to Python. Students will also be capable of presenting and solving mathematical problems in a form understandable by a computer, with the practical work done in Python, but applicable to any other similar language.

Assessment Further Information

Four in-class mini-tests in selected weeks of the semester and one big coursework programming project at the end.

Syllabus

  1. Introduction. Programs as computers' \cookbooks". Compilers vs. pseudo-compilers. Python versions (no details, just the awareness that there are two which are somewhat incompatible) and IDEs (for Linux, Mac, Windows). Simple program (writing, running, testing in the chosen IDE (probably Anaconda)).
  2. Basic input and output, variables, basic types in Python (numbers, strings; mention types in typed languages), value assignments, simple expressions (standard arithmetic operations, real and integer division, division remainder,. . . ). Boolean expressions, true and false. Comments and their importance.
  3. Branching (if, if...elif...else). The concept of \doing nothing" (pass).
  4. Loops (for and range, while). Nesting loops. Flow control (continue, break).
  5. Recap of the above: simple algorithms (Euclid's algorithm, \toying" with numbers' digits, divisibility, etc).
  6. Subroutines (de ning, arguments, return values, documenting), reusing previously presented algorithms.
  7. Lists (including multidimensional ones): concept, basic usage and operations (adding items, traversing, removing items), passing them to and from subroutines. More complex operations (sorting and searching).
  8. Concept of complexity (when is one algorithm \better" than some other). Examples of simple algorithms highly improved by reduction of complexity.
  9. Modules (loading them, introduction to some of the common ones). Brief introduction to the Python package ecosystem (NumPy, SciPy, SymPy, etc.).
  10. Basics of testing and debugging. File I/O.

Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.

Study hours

  • Lectures - 16 hours
  • Practical classes & workshops - 16 hours
  • Independent study hours - 68 hours

Teaching staff

Stefan Guettel - Unit coordinator

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