# Module 06-22754 (2013)

## Level 1/C

 Dan Ghica Semester 1 10 credits John Bullinaria Semester 2 10 credits
Co-ordinator: Dan Ghica
Reviewer: Martin Escardo

The Module Description is a strict subset of this Syllabus Page.

### Outline

The module will introduce the fundamental concepts of Computer Science, such as the representation of data in computer memory, programming constructs, data models and data structures and the analysis of algorithms. The ideas will be presented abstractly, although examples will be given in the language used in the parallel programming workshop modules.

### Aims

The aims of this module are to:

• introduce the fundamental concepts of Computer Science
• support and underpin the programming modules
• introduce advanced abstract data types and standard operations on the same and demonstrate their various representations based on arrays and pointers
• discuss the advantages and disadvantages of the different representations of data types
• introduce the main algorithms for fundamental tasks such as sorting and searching and examine their complexity
• introduce basic concepts of numerical calculations

### Learning Outcomes

On successful completion of this module, the student should be able to:

1 demonstrate understanding of the principles of algorithmic programming
2 demonstrate understanding of abstract models of data and computation
3 make informed choices between alternative ways of implementation, justifying choices on grounds such as time and space complexity or considerations of numerical accuracy
4 explain and apply data structures such as binary trees, heap-trees, graphs and tables, together with their internal representations and relevant algorithms
5 select, with justification, appropriate data structures to ensure efficient implementation of an algorithm (e.g. searching, insertion, deletion or sorting)
6 explain the differences between basic complexity classes of algorithms (constant, linear, quadratic, logarithmic, exponential)
7 argue that algorithms are correct, and derive their time complexity
8 select, with justification, appropriate algorithms for basic tasks such as searching, including reference to the algorithm's complexity class

### Teaching methods

2 hrs lecture, 1 hr exercise class per week

Contact Hours: 68

### Assessment

Sessional: 3 hr examination (80%), continuous assessment (20%).

Supplementary (where allowed): By examination only.

### Detailed Syllabus

1. Introduction to Semester 1
2. Basics of functional programming
3. Recursion
4. Induction
5. Complexity
6. Lists
7. Numeric data types
8. Trees
9. Queues
10. Combinators
11. Functional programming in Java
12. Introduction to Semester 2
13. Basic data types
• Stacks, Queues, Sets
14. Efficiency and Complexity
• Measuring efficiency
• Complexity classes and Big-O notation
15. Trees
• Representations and primitive operators
• Binary Trees
• Binary Search Trees, B-Trees
• Heap Trees, Priority Queues
16. Sorting Algorithms
• General principles and strategies
• Insertion Sort, Selection Sort, Bubblesort
• Tree Sort, Heapsort
• Quicksort, Merge Sort
• Non-Comparison Sorts
17. Storing Data
• Storing in Trees
• Hash Tables
• Handling Collisions
18. Graphs
• Representations
• Traversals and Planarity
• Shortest Paths (Dijkstra's and Floyds' Algorithms)
• Minimal Spanning Trees (Prim's and Kruskal's Algorithms)
• Travelling Salesmen and Vehicle Routing Problems