Mathematical Programming

Please note

This document only provides information for the academic year selected and does not form part of the student contract

School:

School of Computing and Engineering

Credit Rating:

Level (including FHEQ):

F (FHEQ Level 4)

Graded or Non Graded:

Graded

Version Valid From:

2022-09-01

Module Leader:

Ciprian Coman

Version Number

2024.01

Learning Methods

Lecture

Guided Independent Study

Practical Classes and Demonstrations

Synopsis

The aim of this module is to introduce you to computer programming in general, and to Python programming specifically. You will develop an understanding of basic programming concepts and structures such as loops, conditional statements, iterations, functions, variables, scope etc. You will then go on to develop an understanding of object-oriented concepts such as data encapsulation, inheritance and polymorphism. By using Python as the programming language of choice, you will investigate mathematical problems taken from the material taught in lectures and will develop skills for the practical implementation of various types of algorithms discussed in class.

Learning Strategy

Contact time typically consists of lectures and tutorial sessions. Lectures will introduce the theoretical foundations of programming and related mathematical concepts, and will include example applications drawn from topics associated with various branches of mathematics (e.g., calculus, geometry, discrete mathematics, etc). Students will be taught how to apply their skills… For more content click the Read More button below.

Contact time typically consists of lectures and tutorial sessions. Lectures will introduce the theoretical foundations of programming and related mathematical concepts, and will include example applications drawn from topics associated with various branches of mathematics (e.g., calculus, geometry, discrete mathematics, etc). Students will be taught how to apply their skills in programming to translate various mathematical solution strategies into appropriate software solutions which they will develop in the tutorials to explore and support application of the theoretical concepts.

Outline Syllabus

Programming topics:Introduction to computers and programming; algorithms; flowcharts; pseudocode.Overview of Python.Data types; variables; named constants.Data, decision and repetition structures.Functions; parameter passing; scope (local vs. global).Working with files; writing and reading data.Guidelines for computer program design; code refactoring.Introduction to user-defined data types (classes).Aspects of inheritance and polymorphism.Recursion.Exception handling in Python.Plotting mathematical… For more content click the Read More button below.

Programming topics:
Introduction to computers and programming; algorithms; flowcharts; pseudocode.
Overview of Python.
Data types; variables; named constants.
Data, decision and repetition structures.
Functions; parameter passing; scope (local vs. global).
Working with files; writing and reading data.
Guidelines for computer program design; code refactoring.
Introduction to user-defined data types (classes).
Aspects of inheritance and polymorphism.
Recursion.
Exception handling in Python.
Plotting mathematical functions in 2D and 3D (with Matplotlib).
Symbolic Toolbox.

Algorithms/Mathematical content:
Floating-point numbers and their computer representation; review of binary and decimal number systems; relative and absolute errors in scientific computing.
Introduction to algorithm complexity; big O and little o notations; related concepts.
Searching and sorting algorithms.
Recurrence relations & mathematical induction.
Iterative methods for the solutions of algebraic equations in one unknown and/or for differential equations.

Learning Outcomes

On successful completion of this module students will

Demonstrate an understanding of procedural programming using Python.

Demonstrate understanding of fundamental concepts in object-oriented programming using Python.

Analyze mathematical problems and design and develop algorithms using mathematical programming tools.

Implement, test, and debug computer programs in Python using a mixture of built-in and user-defined functions (or classes).

Formative Assessment

Assessment 1: Other

Description:Opportunities for formative assessment will be provided through the practical sessions. Work will be set and feedback will be provided on the students' efforts. Model answers will be provided to certain exercises for comparison and evaluation. Students will be able to gain informal feedback on work in progress by taking it to the practical for their tutor to review. Students will also have the opportunity to self-assess their work to gain a greater understanding of the assessment process.

Individual or Group:Individual

Summative Assessment

Assessment 1: Portfolio

Description:This portfolio will consist of several programming exercises designed to test the learner’s understanding of procedural programming. This portfolio will be developed as work carried out in class and through directed independent study. Some of the exercises will simply check knowledge of the Python syntax, while others will test the ability to translate basic mathematical algorithms into computer code.

Assessment Weight:50

Assessment Length:20 Hours (Typical Effort)

Module Learning Outcomes:MLO1, MLO3

Individual or Group:Individual

Final Assessment Component:No

Marked Anonymously:No

Eligible for Tutor Reassessment:Yes

Assessment 2: Portfolio

Description:This portfolio will be mostly related to object-oriented programming (OOP), and it serves a twofold purpose. First, this coursework is meant to check the learner’s understanding of the syntax for creating classes (identifying suitable instance attributes and defining the appropriate methods for handling them), implementing inheritance and operator overloading, etc. Second, the coursework is also used for evaluating the learner’s ability to design useful OOP code in Python, which can be applied to solving simple mathematical problems.

Assessment Weight:50

Assessment Length:20 Hours (Typical Effort)

Module Learning Outcomes:MLO2, MLO4

Individual or Group:Individual

Final Assessment Component:Yes

Marked Anonymously:No

Eligible for Tutor Reassessment:Yes

Assessment Criteria

Both portfolios will be assessed based on the learner’s understanding of and ability to apply programming concepts and techniques in problem solving, as well as their ability to use various mathematical libraries in Python.

Course and Module Catalogue