COMP1201 Algorithmics

Module Overview

This is a core module for computer science and software engineers. It teaches the basic data structures and algorithms which underpins modern software engineering. Without these algorithms most software would be hopelessly slow to the point of unusability. The course also teaches the principles behind the algorithms and data structures and the software engineering lessons which data structures and algorithms teach us.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Knowledge of common data structures and algorithms
Understanding of time complexity
Understanding of how to code data structures using object oriented methods

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Choose the most appropriate data structure for a particular problem
Understand the operation of a number of important computer algorithms using those structures
Understand how to evaluate an algorithm for efficiency
Choose an appropriate algorithmic strategy to solve a problem

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Be able to solve problems algorithmically

Syllabus

Introduction - Data Objects, Data Structures, Complex Data Structures Algorithm Analysis - Time Complexity Algorithm Design and Strategies - Brute Force, Depth-First, Breadth-First, DFID, Best-first, Greedy Divide and Conquer, - Dynamic Programming, Branch and Bound Simple Data Structures - List, Stack, Queue, Tree, Tree traversal Sorting - Selection Sort, Insertion Sort, MergeSort, QuickSort, Radix sort Searching - Sequential Search, Binary Search, Binary Tree Search Advanced Tree Structures - AVL Trees, Red-black Trees, B-trees Hash tables - Hash table size, Collision resolution, Separate chaining - Open addressing, Re-hashing Priority Queues (Heaps) - Simple implementations, Binary heaps, Heap sort Graphs - Adjacency Matrix and List, Connectivity, Breadth vs Depth first search - Shortest path algorithms - Minimum Spanning Tree, Prim's algorithm

Learning and Teaching

Type	Hours
Wider reading or practice	34
Tutorial	12
Revision	10
Lecture	36
Preparation for scheduled sessions	40
Follow-up work	18
Total study time	150

Resources & Reading list

Weiss MA (2006). Data Structures and Algorithm Analysis in Java.

Robert Sedgewick and Kevin Wayne (2011). Algorithms.

Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein (2009). Introduction to Algorithms.

Assessment

Formative

Assignment

Summative

Method	Percentage contribution
Final Assessment	100%

Repeat

Method	Percentage contribution
Set Task	100%

Referral

Method	Percentage contribution
Set Task	100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisite: COMP1202