Module Details

Module Code: COMP7038
Title: Non-linear Data Struct. & Alg.
Long Title: Non-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: 2 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 will be introduced to asymptotic cost analysis in order to assess the efficiency of algorithms and data structures when solving a computer science-related problem. The module will examine and assess dynamic programming and backtracking algorithms using nonlinear-based abstract data types.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Assess an algorithms computation complexity in terms of time and memory.
LO2 Compare and contrast the interfaces and internal representation of a number of nonlinear abstract data types.
LO3 Design and specify the operations of a nonlinear-based abstract data type in a declarative manner and implement them in a high-level programming language.
LO4 Assess the applicability of dynamic programming and backtracking algorithms to real-world problems.
LO5 Design and implement dynamic programming and backtracking 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).
12788 COMP7035 Linear Data Struct. & Alg.
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
Algorithm Computational Complexity.
Evaluation of algorithm efficiency: Time and memory factors. Formal asymptotic cost analysis: Big O notation. Best, worse and average cases.
Nonlinear-based Abstract Dataypes (ADTs).
Declarative semantics of nonlinear-based ADTs. ADT binary tree specification and implementation: Minimal set of operations. Extending the interface: Traversal, path properties and other supplementary operations. Comparable data type-based ADTs: Binary Search Trees (BST).
Graph-based ADTs.
Declarative semantics as a generalisation of tree-based ADTs. Graph main concepts (sub-graph, path, cycle, connection) and categories (directed, weighted).
Dynamic Programming Algorithms.
Tail recursion. Decreasing recursive design vs. increasing iterative design. Applications: Graphs, searching and resource allocation problems.
Backtracking Algorithms.
Satisfaction vs. Optimisation problems. Decision making: Explicit and implicit constraints. Exhaustive search: Alive, expansion and dead nodes. Prunning function. Applications: Puzzles, graphs and resource allocation 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 nonlinear data structures. Produce a report to justify the data structures being chosen in terms of the time and memory complexities for the algorithms implementing the operations.
Assessment Type Project % of Total Mark 25
Timing Week 11 Learning Outcomes 1,4,5
Assessment Description
Design, implement and document a dynamic programming or backtracking 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
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

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 & Directed Learning (Non-contact) 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 & Directed Learning (Non-contact) 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
  • Thomas H. Cormen et. al.. (2009), Introduction to Algorithms, 3rd. MIT Press, [ISBN: 9780262033848].
  • Robert Sedgewick and Kevin Wayne. (2011), Algorithms, 4th. Addison-Wesley, [ISBN: 9780321573513].
Supplementary Book Resources
  • Narashimha Karumanchi. (2016), Data Structures And Algorithms Made Easy, CareerMonk, [ISBN: 9788193245279].
  • 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 4 Mandatory
CR_KDNET_8 Bachelor of Science (Honours) in Computer Systems 8 Mandatory