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Module Specifications

Archived Version 2022 - 2023

Module Title Introduction to Programming
Module Code CA598
School School of Computing

Online Module Resources

Module Co-ordinator Ray WalsheOffice NumberL2.50
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

The module aims to give the students a foundation in procedural programming. While Introductory, the focus is on thorough understanding of the basic concepts. The course progresses in three levels of difficulty. At Level 1, students have weekly, automatically low stakes assessed programming exercises which provide immediate formative feedback, to develop competence in the concepts being covered. These will culminate in a programming portfolio. This enables the students to bring themselves up to date, before moving on. Summative assessment includes one laboratory exam (Level 2) and one group project (Level 3). Laboratory exams are open book; projects are implemented in small groups. The group assignment also includes a short structured reflective report on by each group member which will be assessed individually. The report involves a critically reflective piece on the programming concepts learned in the group project. Introductory Topics: introduction to the Computer; introduction to Python Programming; Introduction to the Python Problem Solving Techniques: Problem Analysis and Problem Solving; Control Structures - sequence, selection, and iteration Introduction to the basic features of Python : Integer and floating-point variables; Boolean variables; Arithmetic calculations; Operator precedence; Mathematical functions; Control structures in Python if and if/else; while loops; for loops; Lists - Declaration, initialisation and data storage for lists. Manipulating Lists. Strings: Modularity: Use of Python Functions ; passing data between functions. Implement basic programming idioms, such as linear search of an array/list

Learning Outcomes

1. Analyse a problem and write its solution in structured English
2. Read and modify Python programming code
3. Use variables and lists to store data in Python programmes
4. Write programmes using the programming structures Sequence, Selection and Iteration
5. Use Python programmes to break a programme into modules
6. Implement basic programming idioms, such as linear search of an array.

Workload Full-time hours per semester
Type Hours Description
Tutorial12Exercises from notes and previewing laboratory exercises are worked through in small groups.
Laboratory20Practical exercises are attempted by students in the laboratory. Stage 2 assessment involves a laboratory-based exam on these exercises.
Lecture24Full class notes are provided in booklet form in advance of lectures and material is divided by lecture and topics covered. Course content, including supplementary material on key topics and a short interactive tutorial are available online
Independent Study71This comprises time for reading, reviewing given and other exercises, group interaction on project, project time and write-up and revision
Total Workload: 127

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

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.

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 (as well as extracts from) third-party materials where appropriate.

Assessment Breakdown
Continuous Assessment50% Examination Weight50%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Indicative Reading List

  • Cay S. Horstmann, Rance D. Necaise: 2018, Python For Everyone, 3rd, John Wiley & Sons, Limited,, 1119572819
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