Module: Computer Programming II

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Computer Programming II
Module Code	CA117
School	School of Computing
Module Co-ordinator	Semester 1: Darragh O'Brien Semester 2: Darragh O'Brien Autumn: Darragh O'Brien
Module Teachers	Stephen Blott Darragh O'Brien
NFQ level	8	Credit Rating	10
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

None

Description

This module introduces the student to more advanced programming concepts including object-oriented programming through designing and employing classes; it enhances familiarity with built-in libraries and data structures; it equips students with techniques for designing and analyzing elementary algorithms. It further develops students’ programming and problem-solving abilities and fosters good programming practices. Students are expected to attend lectures, participate in tutorials, carry out practical exercises in a programming laboratory, and engage in extensive self-study and hands-on programming practice.

Learning Outcomes

1. Select and appropriately apply the basic programming language constructs and built-in data types (e.g. strings, integers, floats, booleans) required to write a program.
2. Write programs that make effective use of fundamental built-in data structures and their associated methods e.g. lists, tuples, dictionaries and sets.
3. Write programs that read and write text files.
4. Employ exception handling to produce code robust to runtime errors.
5. Define classes that make effective use of instance methods/attributes and class methods/attributes, and make appropriate use of those classes in writing programs of small to moderate size.
6. Design and implement simple algorithms for searching, sorting, and text handling, and assess their time complexity.
7. Implement both iterative and recursive solutions to particular problems (e.g. searching and sorting).
8. Proceed from informal descriptions of problems to design and implementation of programming solutions.
9. Comply with good software engineering principles when writing programs, such as readability, maintainability, information hiding, and levels of abstraction.
10. Implement and apply in problem-solving some fundamental abstract data types and data structures (e.g. stack, queue, tree, graph) including associated operations.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	48	Course lectures
Laboratory	48	Lab work - supervised learning
Laboratory	36	Completion of programming exercises
Independent Study	118	Review of lecture material, background reading and independent practice
Total Workload: 250

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

Built-in types
Strings, integers, floats, lists, dictionaries, sets, tuples.

Files
Reading, writing and processing text files.

Exception handling
Using exceptions to write code robust to runtime errors.

Algorithms
Recursion. Binary and linear search. Elementary sorting. Elementary complexity theory. Introduction to Big-O notation. Introduction to regular expressions. Introduction to graph search.

Design
Modelling with objects. Information hiding and object composition. Case studies in designing with classes. Problem solving techniques, problem decomposition and modular design.

Object-oriented programming
Designing classes and objects. Instance methods and attributes. Class methods and attributes. Operator overloading. Inheritance. Composition. Encapsulation.

Abstract data types and data structures
Stack, queue, list, tree, graph and associated operations.

Assessment Breakdown
Continuous Assessment	50%	Examination Weight	50%

Type	Description	% of total	Assessment Date
Course Work Breakdown
In Class Test	Lab exam 1	20%	As required
In Class Test	Lab exam 2	25%	As required
Laboratory Portfolio	Completion of lab exercises	5%	As required

Reassessment Requirement Type
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
This module is category 1

Indicative Reading List

Gaddis, Tony: 2015, Starting out with Python, 9780133582734
Smallshire, Robert & Bingham, Austin.: 2017, The Python Apprentice, 1788293185
Punch, William F.: 2014, The Practice of Computing Using Python, 9780132805575
Lambert, Kenneth: 2014, Fundamentals of Python: Data Structures, 9781285752013

Other Resources

59417, Website, 0, Think Python: How to Think Like a Computer Scientist, http://www.greenteapress.com/thinkpython/html/index.html, 59418, Website, 0, The Python Standard Library, https://docs.python.org/3/library/index.html, 59419, Website, 0, Online Python Programming, https://www.jdoodle.com/python-programming-online, 59420, Website, 0, A Byte of Python, https://python.swaroopch.com/, 59421, Website, 0, Learn Python The Hard Way, https://learnpythonthehardway.org/book/,

Programme or List of Programmes

COMSCI	BSc in Computer Science
DS	BSc in Data Science

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