Module Details

Module Code: COMP7035
Title: Linear Data Struct. & Alg.
Long Title: Linear Data Struct. & Alg.
NFQ Level: Intermediate
Valid From: Semester 1 - 2017/18 ( September 2017 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 5 programme(s)
Module Description: Data structures and algorithms are fundamental elements in many computing applications. In computer programs data structures offer different techniques for storing data while algorithms provide the methods for manipulating this data. In this module the learner will be introduced to the application of algorithms and data structures to effectively tackle information representation and manipulation when solving a computer science-related problem.
The module will examine and assess divide & conquer and greedy algorithms using linear-based abstract data types.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Assess the role of an abstract data type in isolating the data usage from its internal representation.
LO2 Compare and contrast the interfaces and internal representation of a number of linear abstract data types.
LO3 Design and specify the operations of a linear-based abstract data type in a declarative manner and implement them in a high-level programming language.
LO4 Assess the applicability of divide and conquer algorithms and greedy algorithms to real-world problems.
LO5 Design and implement divide and conquer and greedy algorithms and compare their formulations and solutions.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Data Structures and Algorithms.
Computational model for solving a real-world problems. Data types and data structures. Representing information from a problem domain. Algorithm specification and implementation. Manipulation of the information to solve problems.
Abstract Data type (ADT).
Datatype: Values, operations and internal representation. Data type: Values, operations and internal representation. ADT: Separation of data usage from its internal representation. Creator, observer and mutator operations. Partial and total properties. Generics: Separation of data construction from its underlying elements.
Linear-based ADTs.
Brief declarative semantics of linear-based ADTs. ADT list. Specification: Minimal set of operations. Representation: Static (array-based) vs. dynamic (node-based) implementations. Extending the interface: Supplementary operations. Addition linear-baased ADTs: Stacks and queues.
Divide and Conquer Algorithms.
Direct solution vs. decomposition solutions. Recursion as a problem solving technique. Applications: Sorting and searching problems.
Greedy Algorithms.
Selected and discarded candidates. Selection, satisfiability and solution functions. Applications: Resource allocation and scheduling problems.
Module Content & Assessment
Assessment Breakdown%
Coursework50.00%
End of Module Formal Examination50.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 25
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
Define, specify and document the set of operations for a novel ADT. Implement the set of operations of the ADT using an internal representation based on static and dynamic linear data structures. Produce a report to justify the set of operations and the concrete data structures chosen for the internal representation.
Assessment Type Project % of Total Mark 25
Timing Week 11 Learning Outcomes 1,4,5
Assessment Description
Design, implement and document a divide and conquer or a greedy algorithm to tackle some real-life problems. Produce a report to justify the algorithm family being selected in terms of how effective it is to model the problem domain.
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 50
Timing End-of-Semester Learning Outcomes 1,2,3,4,5
Assessment Description
End of Semester Formal Examination.
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture deliverying theory underpinning learning outcomes. Every Week 2.00 2
Lab Contact Practical computer-based lab supporting learning outcomes. Every Week 2.00 2
Independent Learning Non Contact Independent Study. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture deliverying theory underpinning learning outcomes. Every Week 2.00 2
Lab Contact Practical computer-based lab supporting learning outcomes. Every Week 2.00 2
Independent Learning Non Contact Independent Study. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Narashimha Karumanchi. (2016), Data Structures And Algorithms Made Easy, CareerMonk, [ISBN: 9788193245279].
  • Richard Neapolitan. (2014), Foundations of Algorithms, 5th. Jones and Bartlett Publishers, [ISBN: 9781284049190].
Supplementary Book Resources
  • Thomas H. Cormen et. al.. (2009), Introduction to Algorithms, 3rd. MIT Press, [ISBN: 9780262033848].
  • Christopher Steiner. (2012), Automate This: How Algorithms Came to Rule Our World, Penguin, [ISBN: 9781591844921].
  • John V. Guttag. (2013), Introduction to Computation and Programming Using Python, MIT Press, [ISBN: 9780262525008].
  • Michael T. Goodrich et. al.. (2014), Data Structures and Algorithms in Java, Wiley Publishing, [ISBN: 9781118771334].
  • Michael T. Goodrich et. al.. (2013), Data Structures and Algorithms in Python, Wiley Publishing, [ISBN: 9781118290279].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KSDEV_8 Bachelor of Science (Honours) in Software Development 3 Mandatory
CR_KDNET_8 Bachelor of Science (Honours) in Computer Systems 3 Mandatory
CR_KWEBD_8 Bachelor of Science (Honours) in Web Development 3 Mandatory
CR_KCOMP_7 Bachelor of Science in Software Development 3 Mandatory
CR_KCOME_6 Higher Certificate in Science in Software Development 3 Mandatory