| Module Title |
Programming for Mathematics |
| Module Code |
CSC1168 (ITS: CA167A) |
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Faculty |
Engineering & Computing |
School |
Computing |
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NFQ level |
8 |
Credit Rating |
5 |
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Description
This module aims to give the students a foundation in programmatic problem solving and procedural programming in Python. While Introductory, the focus is on thorough understanding of the basic concepts. Students have weekly, automatically low stakes assessed programming exercises which provide immediate formative feedback, to develop competence and practical skills in the concepts being covered. These will culminate in a Python programming portfolio. This enables the students to bring themselves up to date, before moving on. Summative assessment includes two laboratory exams. A final summative exam at the end of the Semester which will assess concepts, constructs and practice of programming in Python
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Learning Outcomes
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 24 | Blended Delivery (Online/Campus) based lectures and demonstrations | | Tutorial | 24 | Small group tutorials to work through laboratory tasks | | Laboratory | 4 | Programming Lab Exams | | Laboratory | 24 | Drop In Programming Labs with tutor support | | Independent Study | 49 | Independent learning and programming portfolio development |
| Total Workload: 125 |
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| Section Breakdown | | CRN | 11938 | Part of Term | Semester 1 | | Coursework | 50% | Examination Weight | 50% | | Grade Scale | 40PASS | Pass Both Elements | N | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Brian Davis | Module Teacher | |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Laboratory Portfolio | Programming Portfolio in Python - Semester 1 Lab Assignments. Students should be able to design an algorithm and write a programme using Sequence, Selection and Iteration. they should also be familiar with reading and understanding simple Python code. | 5% | Every Week | | Loop Exam | Python Lab Exam 1: Students sit a lab exam after covering sequence, selection, iteration (including linear search) . Designed to assess their understanding and ability to implement these features. Occurs at Week 6 although this may vary due to public holidays and university closure. | 15% | Week 6 | | Loop Exam | Python Lab Exam 2: Students sit a lab exam after covering lists, functions, dictionaries, file processing. Designed to assess their understanding and ability to implement these features. Occurs towards Week 11 although this may vary due to public holidays and university closure. | 20% | Week 11 | | Formal Examination | End-of-Semester Final Examination - Computer Based Lab Exam | 60% | End-of-Semester |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories;
RC1: A resit is available for both* components of the module.
RC2: No resit is available for a 100% coursework module.
RC3: No resit is available for the coursework component where there is a coursework and summative examination element.
* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and Learning Activities
Python Programming Fundamentals
Writing, running and debugging Python programs. Basic data types including numeric types, booleans and strings, the operations on those types, their operators and their precedence. Python arrays and tuples and their operations. Selection and Iteration. Defining and calling functions in Python Text-oriented input/output from standard input and to standard output.
Problem Solving
Computational problem solving: translate programming problems from problem statement to functional solution/implementation. The use of Python arrays and tuples in algorithms and problem solving. Introductory algorithms, including linear search, binary search, insertion sort and selection sort. Iterative solutions to basic computational problems.
Resources
With the amount of material available on-line, it is unnecessary to recommend a particular textbook. Students will be provided with detailed notes and references to third-party materials where appropriate.
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Indicative Reading List
Books:
- Cay S. Horstmann,Rance D. Necaise: 2019, Python for Everyone, 3e, 1119572819
Articles:
None |
Other Resources
- 1: Website, Python Software Foundation, 2022, Python.org,
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