Title:  Linear Data Struct. & Alg. 
Long Title:  Linear Data Struct. & Alg. 
Field of Study: 
Computer Science

Valid From: 
Semester 1  2017/18 ( September 2017 ) 
Module Coordinator: 
Sean McSweeney 
Module Author: 
Ignacio Castineiras 
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 sciencerelated problem.
The module will examine and assess divide & conquer and greedy algorithms using linearbased abstract data types.

Learning Outcomes 
On successful completion of this module the learner will be able to: 
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 linearbased abstract data type in a declarative manner and implement them in a highlevel programming language. 
LO4 
Assess the applicability of divide and conquer algorithms and greedy algorithms to realworld problems. 
LO5 
Design and implement divide and conquer and greedy algorithms and compare their formulations and solutions. 
Prerequisite learning 
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 CIT 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 
Corequisite Modules

No Corequisite 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 
Module Content & Assessment
Indicative Content 
Data Structures and Algorithms.
Computational model for solving a realworld 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.

Linearbased ADTs.
Brief declarative semantics of linearbased ADTs.
ADT list. Specification: Minimal set of operations. Representation: Static (arraybased) vs. dynamic (nodebased) implementations.
Extending the interface: Supplementary operations.
Addition linearbaased 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.

Assessment Breakdown  % 
Course Work  50.00% 
End of Module Formal Examination  50.00% 
Course Work 
Assessment Type 
Assessment Description 
Outcome addressed 
% of total 
Assessment Date 
Project 
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. 
1,2,3 
25.0 
Week 6 
Project 
Design, implement and document a divide and conquer or a greedy algorithm to tackle some reallife problems. Produce a report to justify the algorithm family being selected in terms of how effective it is to model the problem domain. 
1,4,5 
25.0 
Week 11 
End of Module Formal Examination 
Assessment Type 
Assessment Description 
Outcome addressed 
% of total 
Assessment Date 
Formal Exam 
End of Semester Formal Examination. 
1,2,3,4,5 
50.0 
EndofSemester 
Reassessment Requirement 
Coursework Only
This module is reassessed solely on the basis of resubmitted coursework. There is no repeat written examination.

The institute reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time 
Workload Type 
Workload Description 
Hours 
Frequency 
Average Weekly Learner Workload 
Lecture 
Lecture deliverying theory underpinning learning outcomes. 
2.0 
Every Week 
2.00 
Lab 
Practical computerbased lab supporting learning outcomes. 
2.0 
Every Week 
2.00 
Independent Learning 
Independent Study. 
3.0 
Every Week 
3.00 
Total Hours 
7.00 
Total Weekly Learner Workload 
7.00 
Total Weekly Contact Hours 
4.00 
Workload: Part Time 
Workload Type 
Workload Description 
Hours 
Frequency 
Average Weekly Learner Workload 
Lecture 
Lecture deliverying theory underpinning learning outcomes. 
2.0 
Every Week 
2.00 
Lab 
Practical computerbased lab supporting learning outcomes. 
2.0 
Every Week 
2.00 
Independent Learning 
Independent Study. 
3.0 
Every Week 
3.00 
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 Ed., Jones and Bartlett Publishers [ISBN: 9781284049190]
 Supplementary Book Resources 

 Thomas H. Cormen et. al. 2009, Introduction to Algorithms, 3rd Ed., 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 

 Website: Learn to think as a Computer Scientist
 Website: Data Structure Visualizations
 Website: CodinGame  Practice coding with fun
programming challenges
 Website: Python documentation
 Website: Java documentation

Module Delivered in
