COURSE CODE : COSC 11014 TITLE : PRE …science.kln.ac.lk/web/syllabus/stat2015.pdf1 COURSE CODE : COSC 11014 TITLE : Theoretical Foundations of Computer Science PRE-REQUISITES : G.C.E

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COURSE CODE : COSC 11014

TITLE : Theoretical Foundations of Computer Science

PRE-REQUISITES : G.C.E (A/L)

CO-REQUISITES : None

LEARNING OUTCOMES:

At the end of this course module the student should be able to demonstrate a good understanding

of fundamental theoretical aspects of Computer Science.

COURSE CONTENT:

Main components of a Computer: I/O devices, CPU, Memory; Organization of a Computer,

Secondary Storage devices and types of Secondary Storage; Classification of Computers;

Generations of Computers; Software: Systems Software and Application Software; Operating

Systems, functions and types of operating systems; Utility Programs, Translators (compilers,

interpreters, assemblers); Application Software Packages; Application Programs: Algorithms,

Computer programs, Computer programming Languages; Generations of programming

languages; Number Systems: Decimal, Binary, Octal and Hexadecimal; Conversions between

number systems; Use of number systems; Binary addition and subtraction; Representation of

Characters: ASCII, EBCDIC, Unicode; Representation of Numbers: Whole numbers and

Floating-point numbers, Sign-magnitude form, Two’s complement form, Scientific form,

Standard form; Introduction to Computer Networks: LAN, MAN, WAN; Types of Computer

networks: Bus, Star, Ring and Tree topologies; Advantages and disadvantages of computer

networks; Networking and Internetworking devices and their functions; Need for standard

protocols; Data transmission; Introduction to the Internet; Services available on the Internet:

World Wide Web (WWW) and E-mail; Web browsers and Search engines; Effective use of e-

mail; Information Systems: Types of Information Systems, Systems Development Life Cycle

(SDLC); Social, Ethical, Legal and Economic impacts of the use of computers; Computer crime.

METHOD OF TEACHING AND LEARNING: A combination of lectures, tutorials and

assignments.

ASSESSMENT: End of semester examination and assignments.

RECOMMENDED READING:

1. Peter Norton, Introduction to Computers (2006, 6th Edition), Tata McGraw-Hill

Publishing Company Limited, India.

2. June Jamrich Parsons and Dan Oja, New Perspective on Computer Concepts (2003, 6th

Edition), Course Technology a division of Thomson learning Inc.

3. B. Ram, Computer Fundamentals : Architecture and Organization (2005, 3rd Edition),

New Age Publications, India.

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TITLE : Introduction to Programming and Program Design

PRE-REQUISITES :COSC 11014


LEARNING OUTCOMES:

At the end of this course module the student should be able to demonstrate a good understanding

of structured computer programming concepts, algorithm designing and writing structured

programs using a procedural language.

COURSE CONTENT: Introduction to Programming: A brief history and types of programming languages.

Program Design: Introduction to software development, Algorithms, Algorithm specifications

(flow charts, N-S diagrams and pseudo code), Modular programming concepts, Elegance in

program design, Implementing an algorithm using a programming language, Program testing and

program documentation.

The C programming language: Data types, Constants, Identifiers, Variables, Expressions and

assignment, Input and output, Control structures, Functions, Storage classes, Pointers, Scope of

arguments, Structured data types (arrays, structures, unions), Programmer defined data types,

Recursion, File processing, Multi-file programming, Bit manipulation and enumerations, C

preprocessor and advanced features of C language.

Practical Sessions: Programming using C.


practical sessions.

ASSESSMENT: End of semester examination , practical examination and assignments.


1. Gottfried, B.S. Schaum's Outline of Theory and Problems of Programming in C , (2001,

2nd Edition), McGraw Hill Professional Publishing.

2. Kelly, A. and Pohl, I. A Book on C: Programming in C, (1999, 4th Edition), Addison

Wesley Longman Inc.

3. Rajaraman, V. , Computer Programming in C (2004, 6th Edition), Prentice Hall.

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TITLE : Data Structures and Algorithms

PRE-REQUISTIES : COSC 12025


LEARNING OUTCOMES:

At the end of this course module the student should be able to demonstrate a good knowledge on

abstract data types in structured programming, implement and analyze commonly used searching

and sorting algorithms.

COURSE CONTENT:

Introduction to Data structures: A general introduction to data structures, The need for data

structures in programming, Data types, Data structures and abstract data types, Homogeneous

and heterogeneous structures, Static and dynamic structures;

Structures to be considered: Stacks, Queues, Deques, Lists, Trees and Graphs.

Searching Technique: Linear Search and Binary Search;

Sorting Techniques: Internal and External Sorting, Exchange (Bubble) Sort, Insertion Sort,

Selection Sort, Shell Sort, Quick Sort, Heap Sort, Merge Sort and Radix Sort;

Hashing Techniques;

Analysis of Algorithms: Analysis of searching and sorting algorithms, Efficiency of various

sorting algorithms and the need for external sorting.


practical sessions.

ASSESSMENT: End of semester examination, practical examination and assignments.


1. Kelley, AI & Pohl, Ira, A Book on C, (l998, 4th Edition) Addison-Wesley.

2. Kruse R.L., Leung B. P. and Tondo C. L., Data Structures and Program Design (2001),

Pearson Education (Singapore) Pte. Ltd.

3. Gottfried, B., Programming with C. (l999, 2nd Edition), Tata McGraw-Hill Company Ltd.

4. Rajaraman, V., Computer Programming in C, (1994), Prentice-Hall of India (Pvt) Ltd.

5. Langsam Y., Augenstein M. J. and Tenenbaum A. M., Data Structures using C and C++

(2002, 2nd Edition), Pearson Education (Singapore) Pte. Ltd.

4


TITLE : Database Management Systems

PRE-REQUISITIES : COSC 21015


LEARNING OUTCOMES:

At the end of this course module the student will be able to demonstrate the theoretical

knowledge of basic concepts of Database Management Systems and practical skills on

applications of them.

COURSE CONTENT:

Introduction to database systems: Database system concepts and architecture, Three tire

architecture and mapping; Data Modeling: Entity-Relationship (ER) model and Enhanced Entity-

Relationship (EER) model; Relational model: Introduction to the relational model, Relational

constraints, Normalization approach for relational database design (first, second, third and BCNF

normal forms), Advantages and disadvantages of the normalization approach; Logical database

design: ER to relational mapping and EER to relational mapping, Data Manipulation: Relational

algebra and relational calculus, Data Manipulation using SQL, Query Processing and

Optimization; Security and integrity in databases; Physical database design: Introduction to

transaction management in databases, Storing data and primary file organization, File

organization and indexes (primary, secondary, clustering and multilevel indexes), Dynamic

index structure B+ trees; Web based Database Design and Implementation: XML Data, XML

Data Model, XPATH Language, XQuery Language, XML and Relational Databases, Normal

Form for XML Data, Web based Database Applications


practical sessions.

ASSESMENT: End of semester examination, practical examination and assignments.


1. Ramez Elmasri and Shamkant B. Navathe, Fundamentals of Database Systems (2006,5th

Edition),Addison-Wesley Longman Publishing Co., Inc.

2. Fred R. McFadden, Jeffrey A. Hoffer, and Mary B. Prescott, Modern Database

Management (2006, 8th Edition), Pearson Education Series.

3. Ramakrishnan G, Database Management Systems, (2002, 3rd edition), McGraw-Hill.

4. Gary W. Hansen and James V. Hansen, Database Management and Design (2nd Edition,

2002), Prentice Hall.

5. Michael Brundage, XQuery: The XML Query Language (2004), Addison Wesley

Longman Publishing Co., Inc.

http://www.amazon.com/Fundamentals-Database-Systems-Ramez-Elmasri/dp/0321369572/ref=pd_bbs_sr_3?ie=UTF8&s=books&qid=1195720708&sr=1-3

http://www.amazon.com/Fundamentals-Database-Systems-Ramez-Elmasri/dp/0321369572/ref=pd_bbs_sr_3?ie=UTF8&s=books&qid=1195720708&sr=1-3

http://www.amazon.com/Modern-Database-Management-Fred-McFadden/dp/0805360549/ref=sr_1_1?ie=UTF8&s=books&qid=1195721063&sr=1-1

http://www.amazon.com/Modern-Database-Management-Fred-McFadden/dp/0805360549/ref=sr_1_1?ie=UTF8&s=books&qid=1195721063&sr=1-1

http://www.amazon.com/exec/obidos/search-handle-url/002-6750595-9091251?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Gary%20W.%20Hansen

http://www.amazon.com/exec/obidos/search-handle-url/002-6750595-9091251?%5Fencoding=UTF8&search-type=ss&index=books&field-author=James%20V.%20Hansen

http://www.qbrundage.com/michaelb

http://www.amazon.com/Nutshell-Third-Elliotte-Rusty-Harold/dp/0596007647/ref=pd_sim_b_title_2




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TITLE : Object Oriented Programming

PRE-REQUISITES : COSC 12025


LEARNING OUTCOMES:

On successful completion of this course module the student will be able to demonstrate an

understanding of the theory of object–oriented systems, be able to demonstrate how an object-

oriented programming language upholds object-oriented concepts and effectively use an industry

relevant object-oriented programming language.

COURSE CONTENT: Background and motivation of Object Oriented Methods, Concepts of Object-Oriented project

management issues, Principles and features of an industry standard Object-Oriented

Programming language (e.g.: Java/C++). Specific topics include Class and Object models,

Object declaration and creation, Instantiable classes, Visibility modifiers, arrays of objects, Self-

referencing pointers, Re-use of code, Static methods, Arithmetic Expressions, Variables, Scope,

Event-Driven Input and Output, File Objects and Looping Statements, Primitive and reference

types, Strings, Use of String buffer, Passing objects as parameters, Exceptions and Additional

Control Structures, Inheritance, Polymorphism, Encapsulation, Selection, collaboration,

hierarchical classification, using super, creating multilevel hierarchy, method overriding, using

abstract classes, use of final and other relations among classes and objects as well as both static

and dynamic system models and implementations.

Practical Sessions: Programming using Java/C++


practical sessions.


RECOMMENDED READING: 1. Booch, Rumbaugh and Jacobson., The Unified Modeling Language User Guide,(1996)

Pearson Education.

2. Eriksson and Penker., UML Toolkit, (1998), John Wiley & Sons.

3. Wu, T. An Introduction to Object-Oriented Programming with Jav,. (2001, 2nd Edition),

McGraw Hill.

4. Gamma et at. , Design Patterns: Elements of Reusable Object-Oriented Software, (1995)

Addison-Wesley, Reading, MA.

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TITLE : Data Communication and Networks



LEARNING OUTCOMES:

At the end of this unit the student should be able to examine the use of computer networks and to

identify the forces behind their development, and be able to demonstrate a good understanding of

the design and implementation of various network topologies, architectures, protocols and

algorithms.

COURSE CONTENT:

Introduction: Data Communication, Network structures, Types of networks, The Internet,

Protocols and standards, Layers of the OSI model;

The physical layer: Transmission media (guided and unguided), analog and digital transmission,

Transmission impairment, Encoding techniques, Modulation techniques and Modems,

Multiplexing, Circuit switching; Telephone Networks and DSL technology;

The medium access sub-layer: Ethernet (CSMA/CD), token bus, token ring and FDDI;

The data link layer: Framing, error detection and correction, error control and flow control and

data link protocols;

The network layer: Addressing, Routing algorithms, Internetworking and network layer

protocols;

The transport layer: Transport layer protocols (UDP and TCP) and connection management;

The session layer: Token management and synchronization.

The presentation layer: Data compression, data security and encryption.

The application layer: Client-Server model, Application level protocols for File transfer,

Electronic mail, Network management, Hypertext transfer and World Wide Web;

Advanced telecommunication services and developments: ISDN, Frame Relay Networks, ATM

Networks, packet switching and X.25 Networks;

LAN, MAN , WAN and Networking software;

Networking and Internetworking Devices.


assignments.

ASSESSMENT: End of course examination, assignments and/or practical examination.


1. Forouzan B. A., Data Communications and Networking, (2004, 4nd Edition), McGraw

Hill.

2. Forouzan B. A., Local Area Networks, (2002, 1st Edition), Tata McGraw Hill Edition.

3. Stallings W., Data and Computer Communications, (2002, 6th Edition), Pearson

Education Inc.

4. Tanenbaum A.S., Computer Networks, (1996, 3rd Edition), Prentice-Hall International.

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TITLE : Web and Internet Technologies



LEARNING OUTCOMES:

At the end of this course module the student will be able to demonstrate the theoretical

knowledge of basic concepts of Web and Internet Technologies and practical skills in their usage

in critical system development.

COURSE CONTENT:

Overview of the Internet: 7 Layer Architecture, Addressing, Routing, Control protocols,

Application Layer Services etc; Web technologies: Overview of HTTP and Web Servers,

HTML, CSS, Client side scripting, Server side scripting, three tier application development;

Internet technologies: Overview of Distributed Systems, Theory and Practice of Internet

Application Development, Hyper Text Transfer Protocol (HTTP) Programming, Web Servers,

Browsers, Downloading Utilities, Proxy Servers, Caching, User Tracking, Site Maintenance,

Search Engines, Web Crawlers, Programming using Email Protocols, Application Service

Providers (ASPs), Internet Service Providers (ISPs), Firewalls, FTP, Telnet, Tracert, Wireless

Web Access, eXtended Markup Language (XML), VoiceXML, ebXML & UBL; Web Services:

Overview of Web Services, Web Services Frameworks and their Performance, Simple Object

Access Protocol (SOAP), Web Services Description Language (WSDL), Web Services Security,

Wireless Web Services; e-Commerce: overview of e-Commerce, e-Business strategies and

models, Business and Marketing tools for e-Business, e-Marketing and e-Ethics, e-Business

laws

METHOD OF TEACHING AND LEARNING: A combination of lectures, practicals and

tutorials.

ASSESMENT: End of semester examination, practical examination and assignments.


1. Jeffrey C. Jackson, Web Technologies: A Computer Science Perspective (2006), Prentice

Hall.

2. Alison Cawsey and Rick Dewar, Internet Technology and E-Commerce, Palgrave

Macmillan.

3. Anders Mller and Michael I. Schwartzbach, An Introduction to XML and Web

Technologies (2006), Addison-Wesley Longman Publishing Co.

4. Paul J. Deitel, Tem R. Nieto, and Deitel, The Complete Internet and World Wide Web

Programming Training Course (2nd Edition), Prentice Hall.

5. Simon St. Laurent, Edd Dumbill and Joe Johnston, Programming Web Services with

XML-RPC, O'Reilly.

6. Sebesta and Robert W, Programming the World Wide Web (4th Edition, 2007), Addison

Wesley.

7. Chris Bates , Web Programming: Building Internet Applications (2003), John Wiley &

Sons, Inc

http://www.amazon.co.uk/Web-Technologies-Computer-Science-Perspective/dp/0131856030/ref=sr_1_10?ie=UTF8&s=books&qid=1196418444&sr=1-10

http://www.google.com/pagead/ads?client=ca-print-pearson-prentice_hall_college&format=googleprint&url=null&num=0&channel=BTB-ca-print-pearson-prentice_hall_college+BTB-ISBN:0131856030&alternate_ad_url=http://vig.prenhall.com/catalog/academic/product/0,1144,0131856030,00.html


http://www.amazon.co.uk/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books-uk&field-author=Alison%20Cawsey

http://www.amazon.co.uk/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books-uk&field-author=Rick%20Dewar

http://www.amazon.com/exec/obidos/search-handle-url/002-6750595-9091251?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Anders%20Mller

http://www.amazon.com/exec/obidos/search-handle-url/002-6750595-9091251?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Michael%20I.%20Schwartzbach

http://www.amazon.com/Complete-Internet-Programming-Training-Course/dp/0130895504/ref=sr_1_12?ie=UTF8&s=books&qid=1196931548&sr=1-12

http://www.amazon.com/Complete-Internet-Programming-Training-Course/dp/0130895504/ref=sr_1_12?ie=UTF8&s=books&qid=1196931548&sr=1-12

http://www.amazon.com/Programming-Services-XML-RPC-OReilly-Internet/dp/0596001193/ref=sr_1_10?ie=UTF8&s=books&qid=1196931548&sr=1-10

http://www.amazon.com/Programming-Services-XML-RPC-OReilly-Internet/dp/0596001193/ref=sr_1_10?ie=UTF8&s=books&qid=1196931548&sr=1-10

http://books.google.lk/url?id=l40nvlrjWL0C&pg=PP1&q=http://www.oreilly.com&linkid=1&usg=AFQjCNGHaF93Kx_3hdNxXi_4yVkSsASBzw

http://www.alibris.com/booksearch.detail?invid=9282576263&browse=1&subject=Internet+programming&qwork=5408060&qsort=&page=1

http://www.amazon.com/Web-Programming-Building-Internet-Applications/dp/0470017759/ref=pd_bbs_sr_2?ie=UTF8&s=books&qid=1196931548&sr=1-2

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8. Harvey M. Deitel, Paul J. Deitel and Andrew B. Goldberg, Internet & World Wide Web

How to Program (3rd Edition), Prentice-Hall, Inc.

9. Matthew W. Guah and Wendy Currie, Internet Strategy: The Road to Web Services

Solutions, Idea Group Inc (IGI)

10. David Wood and Mark Stone, Programming Internet Email, O'Reilly.

11. Jean Andrews, i-Net+ Guide to Internet Technologies, Course Technology.


TITLE : Visual Programming



LEARNING OUTCOMES:

On successful completion of this course module the student will be able to demonstrate a good

understanding of visual computer programming and algorithm designing. They will be able to

demonstrate the ability to apply conventional structured programming techniques to the code

written for event driven procedures.

COURSE CONTENT: Introduction to Visual Programming: A brief history and types of programming languages, use of

an Integrated Development Environment, basic language facilities; events, errors and exceptions;

Facilities for building GUI interfaces: Form design, Uses of forms, Controls and control

properties, Design of forms;

Event driven programming: Introduction to basic control objects, Branching, Control loops,

Procedures and functions, Interacting with the user, stream-based file I/O, Arrays, Jet Database

Engine, Database connectivity, Connecting through ODBC, Introduction to Threads, Debugging

and Testing;

Querying the Database: Query by example, Query by form, Use of SQL commands;

Reports: Development of a variety of reports including tabular, group totals, sub totals and other

standard reports.

Practical Sessions: Programming using VB.NET

METHOD OF TEACHING AND LEARNING: A combination of lectures, tutorials,

practical sessions and a group project.

ASSESSMENT: End of course examination, practical examination and assignments.


1. Dale, N.B., McMillan M.,Visual Basic .NET: A Laboratory Course, (2002), John and

Bartlet Publishers.

2. Grundgeiger, D., Programming Visual Basic .NET, (2002), O'reilly Publications.

http://www.amazon.com/Internet-World-Wide-Web-Program/dp/0131450913/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1196931548&sr=1-1

http://www.amazon.com/Internet-World-Wide-Web-Program/dp/0131450913/ref=pd_bbs_sr_1?ie=UTF8&s=books&qid=1196931548&sr=1-1

http://www.amazon.co.uk/Internet-Strategy-Road-Services-Solutions/dp/1591407648/ref=sr_1_14?ie=UTF8&s=books&qid=1196418794&sr=1-14

http://www.amazon.co.uk/Internet-Strategy-Road-Services-Solutions/dp/1591407648/ref=sr_1_14?ie=UTF8&s=books&qid=1196418794&sr=1-14

http://books.google.lk/url?id=JZCztS1x_AUC&pg=PP1&q=http://www.idea-group.com&linkid=1&usg=AFQjCNFQnyRpwFIK7WCDleYoOyCmDW1jnw

http://www.amazon.com/Programming-Internet-Email-David-Wood/dp/1565924797/ref=sr_1_4?ie=UTF8&s=books&qid=1196931548&sr=1-4

http://books.google.lk/url?id=l40nvlrjWL0C&pg=PP1&q=http://www.oreilly.com&linkid=1&usg=AFQjCNGHaF93Kx_3hdNxXi_4yVkSsASBzw

http://www.amazon.com/exec/obidos/search-handle-url/002-6750595-9091251?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Jean%20Andrews

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3. Vick, P., The Visual Basic .Net Programming Language, (2004), Addison-Wesley

Professional.

4. Blair, B., Crossland, J., Reynolds, M., Willis, T. (2002, 2nd Edition) Beginning VB.NET,

Wrox Press Ltd.. Birmingham.

5. Deitel, H.M., Deitel, P.J., Nieto,T.R. (2002, 2nd Edition), Visual Basic.Net How to

Program , Prentice Hall London.

6. Evjen, B., Hollis, B., Lotka, R., McCarthy, T., Pinnock, J., Ramachandran, R., Sheldon,

B., Professional VB.NET , (2004), Wiley Publishing, Inc. Indianapolis

7. Liberty, J., Programming Visual Basic.NET, (2003), O'Reilly and Associates Inc.

Sebastopol.

8. Schneider, D., An Introduction to Programming using Visual Baisc.NET, (2003), Pearson

Education Ltd. London.

1

COURSE CODE : STCS 44014

TITLE : Systems Analysis and Design



LEARNING OUTCOMES :

At the end of this course a student should be able to demonstrate an understanding of the key

principles and techniques of systems analysis and design, and the ways in which they are employed in

the development of information systems to meet the needs of organizations. Particular emphasis will

be given to the study of the role that analysts are expected to play in systems development.

COURSE CONTENT :

An Introduction to the Systems Process, The Systems Development Life Cycle, Systems Analysis and

Modeling, The Philosophy and Techniques of Systems Design, Prototyping, Modeling Tools for the

Systems Analyst, Systems Implementation and Management, Quality Assurance and Testing,

Maintaining and Managing the Systems Process, Issues in Information Systems Project Management.

METHOD OF TEACHING AND LEARNING: A combination of lectures and tutorials.

ASSESMENT: End of course examination, assignments and/or seminars.


1. Whitten et al, Systems Analysis and Design Methods, (2002), McGraw-Hill.

2. Perry Edwards, Systems Analysis and Design, (1993), McGraw-Hill.

3. Kenne!h E. Kendall and Julie E. Kendall, Systems Analysis and Design, (1998, 4th Edition),

Prentice Hall.

2


TITLE : Object Oriented Analysis and Design



LEARNING OUTCOMES :

After successful completion of the course unit, a student will be able to design software in an

object-oriented environment and be able to use UML as a notation to support this design.

COURSE CONTENT: The course covers life-cycle and tasks for OO software development, up to, the actual writing of

code;

Object Concepts: Objects ,Classes, Inheritance, Software Development Methodology, Using

UML, CRC Cards, Requirements Capture, Business Perspective, Developer Perspective,

Analysis: Introduction, Static Analysis, Dynamic Analysis;

System Design: Introduction, Networked System Topologies, Choosing Technologies,

Partitioning Software, Subsystem Design, Designing the Business Logic, Persistence using a

Relational Database, Finalizing the User Interfaces, Designing the Business Services, Thread

Safety;

Code Specification: Object-Oriented Specification, Design by Contract, Informal Specification

in Java, UML Notation will be used throughout the course unit.

Practical Sessions: Programming using Java/C++

METHOD OF TEACHING AND LEARNING:

A combination of lectures, tutorial and practical sessions



1. Booch, Rumbaugh and Jacobson., The Unified Modeling Language User Guide,(1996),

Pearson Education.

2. Eriksson and Penker., UML Toolkit, (1998), John Wiley & Sons.

3. Wu, T., An Introduction to Object-Oriented Programming with Java. (2001, 2nd Edition),

McGraw Hill.

4. Gamma et al. , Design Patterns: Elements of Reusable Object-Oriented Software, (1995),


3


TITLE : Computer Architecture and Design



LEARNING OUTCOMES :

At the end of this course the student should be able to demonstrate an understanding of the important

aspects of the hardware of a conventional computer and the architectural features of microprocessors

provided to support efficient computation.

COURSE CONTENT: Introduction to Computer Organization, Architecture and Design Concepts;

Basic Principles: Boolean Algebra, Combinational and Sequential circuits, logic design techniques,

Karnaugh map, Latches, Flip-flops, Half-adder, Full-adder, counters and Shift registers;

Classification of Processor architectures: SISD, SIMD and MIMD;

Instruction sets: Sequential execution of the instructions with respect to a single Processor, Machine

instruction characteristics, Instruction set design, Addressing modes and formats, Instruction

Pipelining;

Input and Output: I/O modules, System bus, I/O channels and processors;

CPU Structure: Processor and register organization, Design of ALU, Arithmetic, Shift and logic

control systems, RISC machine and parallel architectures.

Memory Management: Memory hierarchy, Virtual memory and Cache memory, Performance

comparison.

Programmed data transfer: Interrupts and interrupt handling systems.

Assembly language programming

METHOD OF TEACHING AND LEARNING: A combination of lectures and tutorials.

ASSESSMENT: End of course examination and assignments.


1. Stallings W., Computer Organization and Architecture, (2002, 5th Edition), Prentice Hall

International Inc.

2. Ram B., Computer Fundamentals, (1995), Wiley Eastern Limited.

3. Tananbaum A. S., Structrued Computer Organization, (1990, 3rd Edition), Prentice Hall

International Inc. .

4. Morris Mano M., Computer System Architecture, (3rd Edition), Prentice-Hall International Inc.

4


TITLE : Advanced Database Systems with Applications



LEARNING OUTCOMES :

At the end of this course module a student will be able to demonstrate the theoretical knowledge

on advanced database management system design principles and techniques, and practical skills

on applications of them.

COURSE CONTENT:

Relational database design; More on SQL; Physical data organization and Indexing: Basics,

ISAM, B-tree, more trees, hashing, textual, distributed/P2P indexing; Query processing: scan,

sort, hash, index-based, systems view; Buffer management; Query optimization: Rewrite, cost

estimation, algorithms; Online query processing and optimization, downsizing the DBMS;

Transaction Processing Concepts; Concurrency Control Techniques; Practical Database Design

and Tuning; Database Recovery Techniques; Object-Oriented Databases; Deductive databases;

Emerging Database Technologies and Applications; Distributed Databases and Client-Server

Architecture; XML, DTD, XPath, XQuery, XSLT, XML storage, XML indexing, streaming

XML; Database Security and Authorization; Enhanced Data Models for Advanced Applications;

Recent advances in database systems such as Spatial and Active Databases; Introduction to Data

warehousing and data mining.


A combination of lectures, tutorials, assignments and group projects.


RECOMMENDED READING

1. Ullman, J. D. and Widom, J., Database Systems: The Complete Book, (2002), Hector

Garcia-Molina, Prentice Hall.

2. Stonebraker M., and Hellerstein, J. M., Readings in Database Systems, (1998, 3rd

Edition), Morgan Kaufman.

3. Elmasri, R. and Navathe, S. B., Fundamentals of Database Systems (2006, 5th Edition),

Addison-Wesley Longman Publishing Co., Inc.

4. McFadden, F. R., Hoffer, J. A. and Prescott, M. B., Modern Database Management

(2006, 8th Edition), Pearson Education Series.

5. Ramakrishnan, G., Database Management Systems, (2002, 3rd edition), McGraw-Hill.

6. Hansen, G. W. and Hansen, J. V., Database Management and Design, (2002, 2nd

Edition), Prentice Hall.

7. Brundage, M., XQuery: The XML Query Language, (2004), Addison Wesley Longman

Publishing Co., Inc.

5


TITLE : Logic Programming



LEARNING OUTCOME:

At the end of this course the student should be able to demonstrate an understanding of important

aspects in an Intelligent Knowledge Based System, and demonstrate skills in writing programs

using PROLOG.

COURSE CONTENT:

Introduction: Introduction to Artificial Intelligence (AI), Major areas of AI, Description of

Intelligent Knowledge Based Systems (IKBS);

Knowledge and Reasoning: Knowledge representation and types of reasoning;

Declarative/Procedural Programming Languages;

Logic Programming: Introduction to Logic Programming using PROLOG.

Basic Concepts: Facts, Rules and Queries, Data Objects, Goal Matching, Unification and

Backtracking, Recursion in Prolog, List manipulation in Prolog;

Search strategies and Search trees;

Cut and Negation: The Use of the Cut, Negation as Failure, Problems with Cut and Negation,

Implementing IF-THEN-ELSE condition;

Input and Output: Input and Output in Prolog, Communicating with files, Processing files of

terms.

Advanced features in Prolog: Difference Lists, Binary Trees in Prolog;

Some applications of Prolog: Searching and Sorting Techniques, Problem solving, Theorem

proving, Database applications, etc.;

Logic and Prolog: Deductive reasoning in Propositional and Predicate Logic, Resolution in

Propositional and Predicate Logic, First-order Predicate logic, Prolog and First-order Predicate

logic, Writing Logical Statements, Conversion of Logical Statements into Normal Form,

Unification, Unification algorithm, Resolution principle, Connection Graph proof procedures.

METHOD OF TEACHING AND LEARNING: A combination of lectures, assignments and practical classes.

ASSESSMENT: End of course examination, assignments and practical examination.


1. Bratko, Ivan, Prolog Programming for Artificial Intelligence, (1988, 2nd Edition),

Addison-Wesley.

2. Sterling, L & Shapiro, E., The Art of Prolog, (1986), The MIT Press.

3. Kaushik Saroj, Logic and Prolog Programming (2007, 1st Edition), New Age

International Publishers, India.

4. Russel, S. and Norvig, P., Artificial Intelligence – A Modern Approach (2005, 2nd

Edition), Prentice-Hall of India Private Limited.

5. Shinghal, R., Formal Concepts in Artificial Intelligence, (1992, 1st Edition), Chapman &

Hall.

6

6. Luger, G. F.& Stubblefield, W. A., Artificial intelligence, (1993, 2nd Edition) The

Benjamin/ Cummings Publishing Company, Inc.

7. Mc Donald C, Yazdani M., Prolog Programming, (1990, 1st Edition), Blackwell

Scientific Publications.


TITLE : Machine Learning

PRE-REQUISITES : All COSC core course units followed in the General Degree.


LEARNING OUTCOME:

At the end of this course module a student will be able to demonstrate the ability to solve

problems using learning algorithms, neural networks and genetic algorithms.

COURSE CONTENT:

Learning: Learning Agent, Concept of learning, Nature inspired and other types of learning,

Decision tree learning;

Artificial Neural Networks: Basic Models, Hopfield Model, Design and implementation,

Perceptrons, Multilayer Networks and Backpropagation, Counterpropagation and recurrent

networks;

Statistical Training: Evaluating hypotheses, Bayesian learning, Bayesian belief networks,

Optimal Classifiers, Computational learning theory, Vapnic-Chervonenkis Dimension, Mistake

bound analysis, Sample complexity analysis, Instance-based learning;

Genetic Algorithms: Schemata, Design and implementation, Genetic operations, Hypothis Space

search, Genetic Programming, Models of Evolution and Learning;

Inductive and Analytical Learning; Reinforcement Learning; Classifier Systems;

Selected applications in data mining, automated knowledge acquisition, pattern recognition,

program synthesis, text and language processing, internet-based information systems, human-

computer interaction, semantic web, bioinformatics and computational biology.

METHOD OF TEACHING AND LEARNING: A combination of lectures and assignments.



1. Mitchell, T. M., Machine Learning, (1997), McGraw-Hill International Editions.

2. Bishop, C., Pattern Recognition and Machine Learning, (2006), Springer-Verlag.

3. Hertz, J., Krogh, A. and Palmer, R. G., Introduction to The Theory of Neural

Computation, (1991), Addison-Wesley Publishing Company.

4. Goldberg, D. E., Genetic Algorithms, (1989), Addison-Wesley Publishing Company.

5. Rao, M. A. and Srinivas, J., Neural Networks, (2004), Narosha Publishing House Pvt.

Ltd.

7

6. Mehothra, K., Mohan, C.K. and Ranka, S, Artificial Neural Networks,(1997), Oxford

University Press.

7. Looney, C.G., Pattern Recognition Using Neural Networks, (2000), MIT Press.

COURSE CODE : COSC44074

TITLE : Theoretical Aspects of Computer Graphics



LEARNING OUTCOMES:

At the end of this course module a student should be able to demonstrate a good understanding

of concepts behind computer graphics and be able to apply the techniques and algorithms for

modeling and rendering 2D and 3D graphical objects.

COURSE CONTENT: Hardware and software components of graphics systems. Fundamental techniques in graphics

Output and filled data primitives. Graphic systems, Scan conversions, 2D and 3D geometric

transformations. Two dimensional viewing: viewing pipeline, clipping, and windowing. Three

dimensional viewing: viewing pipeline, viewing parameters, projections, viewing

transformations, clipping, visible surface detection. Basic rendering, Introduction to illumination

models and surface rendering, Geometric modelling and representations, Computer animation,

Visualization.

Practical Sessions: Programming using OpenGL

METHOD OF TEACHING AND LEARNING: A combination of lectures, assignments and practical classes.

ASSESSMENT: End of course examination, assignments and practical examination.


1. Hearn, D. and Baker, M.P., Computer Graphics with OpenGL, (2004, 3rd Edition),

Prentice Hall.

2. Giambruno M., 3D Graphics&Animation, (2002), Prentice Hall.

3. Hearn, D. and Baker, M.P., Computer Graphics C Version, (1997, 2nd Edition), Prentice

Hall.

4. Foley, J. D., van Dam, A, Feiner, S. K. and Hughes, J. F., Computer Graphics, (2002, 2nd

Edition), Pearson Education.

5. Xiang, Z. and Plastock, R., Theory and Problems of Computer Graphics, (2005, 2nd

Edition), Tata McGraw-Hill Publishing Company Limited New Delhi.

8


TITLE : Data Security



LEARNING OUTCOMES:

At the end of this course module a student should be able to understand the need for good

security management and should be able to identify the security attacks associated with computer

networks and how to provide security against such attacks.

COURSE CONTENT: An introduction to cryptography and security mechanisms: Security Services, mechanisms and

Attacks; Conventional cryptography: Symmetric Ciphers, Block Ciphers; Modular Arithmetic;

Public-key cryptography: Key management, Message Authentication and Hash functions, Digital

Signatures and Authentication Protocols;

Network security: Protection of data transferred over commercial information networks, Generic

security technologies relevant to networks, user identification techniques, authentication

protocols and key distribution mechanisms, Electronic mail security, IP Security, Web Security;

Computer Security: Defining security requirements (security policy). Intruders, Malicious

Software, Firewalls, Security models, The main security features and mechanisms in operating

systems, Security-related issues of computer architecture.

Security of Middleware: Software protection, Standards and security evaluation criteria,

Security-related standards, Guidelines for managing IT security.




1. Stallings, W., Cryptography and Network Security, (2004, 3rd Edition), Prentice-Hall of

India Private Limited.

2. Pfleeger, C. P. and Pfleeger, S. L., Security in Computing, (2005, 3rd Edition), Pearson

Education.

3. Bishop, M., Introduction to Computer Security, (2005), Addison-Wesley, Pearson

Education, Inc.

4. Carroll, J M, Computer Security, (1996), Butterworth-Heinemann.

5. Leiss ,E.L., Principles of Data Security, (1962), Plenum Press.

9


TITLE : Software Engineering



LEARNING OUTCOMES:

At the end of this course module the student should be able to understand the principles behind

software engineering processes and the strengths and weaknesses of various software processes,

produce simple analyses, designs, implementations and test suites for small systems

COURSE CONTENT: Software Process: Software products, Software processes, Software life-cycle and process

models, process assessment models, Team work in the software process, Professional

responsibility;

Software requirements and specifications: Requirements elicitation, requirements analysis

modeling techniques, functional and nonfunctional requirements, prototyping, basic concepts of

formal specification techniques;

Software Design: Fundamental design concepts and principles, design patterns, software

architecture, structured design, object-oriented analysis and design, design for reuse; comparison

of design strategies, User interface design;

Software Reliability: Software reliability metrics, Programming for Reliability, Software Reuse.

Verification and Validation: Validation planning, testing fundamentals, including test plan

creation and test case generation, black-box and white-box testing techniques, unit integration,

validation, and system testing, object oriented testing, inspections;

Software evolution: Software maintenance, characteristics of maintainable software,

Configuration management, Software Reengineering, Legacy systems, Safety critical systems,

Security critical systems, Software reuse.




1. Sommerville, I., Software Engineering, (8th Edition)

2. Pressman, R. S., and Ince, D., Software Engineering: A Practitioner's Approach, (1992,

5th Edition), McGraw Hill.

3. Ghezzi, C., Jazayeri, M., and Mandrioli, D., Fundamentals of Software Engineering,

(2002), Prentice Hall.

4. Goodbole, N. S., Software Quality Assurance, (2004), Narosha Publishing House Pvt.

Ltd.

10


TITLE : Software Project Management

PRE-REQUISITES : COSC 44014 and COSC 44094


LEARNING OUTCOMES: At the end of this course module a student should be able to

explain how a project can be broken down into stages and what each stage contributes to the

project, be able to select appropriate techniques for use in the stages of a project, be able to

justify the appropriateness of these techniques, and apply them to practical situations, be able to

explain the limitations of the project approach in developing information/software systems.

COURSE CONTENT: Software Project Management Techniques and Concepts: The profile of a project, understanding

and decomposing the task, Estimation and risk, Scheduling and resourcing, Monitoring and

control, Quality assurance, Maintenance, Cost benefit analysis, Software project documentation

issues, The project team and the project manager, Alternative approaches and emerging issues,

Human Resource Management, Professional, legal and ethical issues, Computer crime,

Computer law in Sri Lanka.

METHOD OF TEACHING AND LEARNING: A combination of lectures and assignments

ASSESSMENT: End of course examination and assignments


1. Yeates, D. and Cadle, J., Project Management for Information Systems, (1996, 2nd

Edition) , Pitman Publishing.

2. Pressman, R. S., Software Engineering, (1997, 4th Edition), McGraw-Hill.

3. Kemerer, C. F., Software Project Management: Readings and Cases, (1997), IRWIN

Publishers.

4. Hughes, B. , Cotterell, M., Software Project Management, (2002), McGraw-Hill.

11


TITLE : Multimedia Systems Development



LEARNING OUTCOMES:

At the end of this course module a student should be able to understand the characteristics of

multimedia systems and how to address issues, be aware of the differences among multimedia

authoring systems, be familiar with the software development process as practiced in a

multimedia development environment.

COURSE CONTENT: Introduction to Multimedia: Multimedia and Hyper media, Components of multimedia,

Multimedia authoring and tools;

Graphics and Image Representation: Text, Images, Audio and Video representation. Image data

types, Colour lookup tables, File formats, Graphics animation files;

Colour in Image and Video: Colour science, Colour models in images, Colour models in video;

Concepts in Video: Types of video signals, Analog video, Digital video;

Digital Audio: Digitization of sounds, Quantization and Transmission of audio, Musical

Instrument Digital Interface (MIDI);

Multimedia Data Compression: Lossless compression algorithms, Lossy compression

algorithms, Text and Image compression, Image compression standards, Video compression

techniques, Audio compression techniques, MPEG audio and video compression;

Multimedia Communication: Computer and Multimedia networks, Multimedia network

communications and applications, Wireless networks, Multimedia over wireless networks,

Entertainment networks;

Practical applications using a suitable multimedia authoring tool.




1. Li, Z., and Drew, M. S., Fundamentals of Multimedia, (2005), Pearson Education.

2. Halsall, F., Multimedia Communications, (2001), Addison-Wesley.

3. Andleigh, P. K., and Thakrar, K., Multimedia Systems Design, (1998),Prentice-Hall.

4. Zendler, A., Haggenmüller, R. and Schwärtzel, H., Multimedia Development Systems:

With Methods for Modeling, (1998), Tectum Verlag DE.

12


TITLE : Wireless Communication and Networks



LEARNING OUTCOMES:

At the end of this course module a student should be able to demonstrate a good understanding

of principles, issues and design of wireless networks.

COURSE CONTENT: Introduction to wireless communication technology, Protocols and TCP/IP Suite, Antennas and

propagation of signals, Signal encoding and decoding, Spread spectrum, Multiple access

methods, Coding and error control, Satellite communications, Cellular wireless networks,

Cordless systems and wireless local loop, Wireless LAN technology and Standards, Bluetooth,

GPRS and 3G wireless systems.




1. Stallings, W., Wireless Communications and Networking, (2004), Prentice-Hall of India

Private limited, New Delhi.

2 Vijay Kumar and Garg, V.K., Wireless communication systems, (2007), Elsevier

Science & Technology.

13


TITLE : Theory of Computation



LEARNING OUTCOMES:

At the end of this course module a student should be able to understand the advanced concepts of

the theory of computation.

COURSE CONTENT: Regular Languages and Expressions: Finite Automata, Deterministic and Nondeterministic Finite

Automata, Kleene's Theorem, Criteria for Regularity, Regular expressions, Pumping lemma,

Closure properties of Regular languages;

Context-Free Languages: Context free grammars, Derivation Trees and Ambiguity, Simplified

and Normal Forms, Chomsky Normal Form, Gireibach Normal Form;

Pushdown Automata: Pushdown Automata (PDA) and Context-Free Languages (CFLs) and

Nondeterministic CFLs;

Turing Machines: P, NP and NP complete Problems, Reusability, Undecidability, Recursively

Enumerable Languages; Computability and Complexity Theory.




1. Natarajan, A.M., Tamilarasi, A., and balasubramani, P., Theory of Computation, (2007),

New Age International Bublishers.

2. Xavier, S. P. E., Theory of Automata, Formal Languages and Computation, (2007), New

Age International Bublishers.

3. Martin ,J.C., Introduction to Languages and Theory of Computation, (1996), McGraw

Hill.

4. Sipser, M., Introduction to the Theory of Computation, PWS Publishing Co.

5. Hopcroft, J. E., Motwani, R. and Ullman, J.D., Introduction to Automata Theory,

Languages and Computation, (2nd Edition, 2002), Pearson Education.

14


TITLE : Compiler Theory

PRE-REQUISITES : All COSC core course units.


LEARNING OUTCOMES:

At the end of this course module a student should be able to understand the advanced concepts

on compiler construction, operation and its applications.

COURSE CONTENT: Motivation for studying compilers; desirable qualities of a compiler; tracing the compilation of

an example expression; lexical analysis and DFAs; tokens and lexemes; syntax analysis and

CFGs and PDAs; semantic analysis, symbol tables, Tracing the compilation of an example

expression (continued); translation and instruction selection; the compiler as a frontend/backend

pair; intermediate languages; the frontend composed of a scanner and parser; role of scanner

and parser, parse tree nodes, Implementing parse tree behaviour, The backend composed of

instruction selection, register allocation, and instruction scheduling; optional optimization phase

between the frontend and backend; difficult language features; introduction to lexical analysis;

scanner generators; specifying patterns for scanners; formal languages; regular expressions and

regular languages; operations; More regular expression examples; scanner generation theory;

definition of finite automata; DFA vs. NFA; transition diagrams; examples; table

implementation of DFA; relationship of generators and their advantages; acceptance in an NFA;

equivalence of RE, DFA, & NFA, Role of the parser; formal vs. informal descriptions of

programming languages; context free grammars and their advantages; derivations and parse

trees; leftmost & rightmost derivations; different derivations producing difference precedence

of operators; enforcing precedence; ambiguity, rearranging grammar to eliminate ambiguity;

dangling else solutions; common grammar constructs; parsing and role of parser; desirable

properties of a single-scan parser, Introduction to LL(1) and LR(1); top-down vs. bottom-up

parsers; predictive parsing & recursive descent parsing; problems with left recursion;

eliminating left recursion, Introduction to semantic analysis; name binding and type checking;

name binding and scoping rules in the Tiger language; separate name spaces for types and

variables; predefined names; Symbol tables and associated operations (insert, lookup, delete);

hash tables with chaining; keeping track of scopes; hash table with inter-twining linked list,

Type checking constructs: function call, array and record access, if-then-else, while & for loops,

and declarations of types, variables, arrays, records, and functions; Activation records/frames

and their format; code for calling linkage; different storage locations for variables; accessing

lexically nested variables; de Bruijn notation, garbage collection; reference counting; mark-n-

sweep, copying collection; generational copying collection; introduction to compiler

optimization; types of optimization: execution speed, code size, power consumption, etc.; safety

of compiler transformations, Register allocation; historical perspective; the graph coloring

problem; NP Completeness of problems; liveness of variables and the interference graph;

graph simplification algorithm; spilling; enhancements: coalescing, optimistic coloring, and

node merging.



15


1. Holmes, J., Building Your Own Compiler with C++, (1995), Prentice Hall.

2. Barrett, W.A., Compiler Construction: Theory and Practice, (1986), Science Research

Associates.

3. Aho, A. V. and Ullman, J. D., Principles of Compiler Design, (2002), Narosha Publishing

House Pvt. Ltd.


TITLE : Digital Image Processing



LEARNING OUTCOMES:

At the end of this course module a student should have an understanding about analytical tools

and methods which are currently used in digital image processing as applied to image

information for human viewing.

COURSE CONTENT: Image Sensing and Digital Image Representation, Image Analysis, Human Visual Perception;

Image Enhancement: Smoothing and Image restoration; System models: Gray scale models,

histogram models; Image Transforms: Discrete transforms, Fourier transforms, Discrete cosine

transforms, Walsh-hadamard, Haar, PCT, Filtering, Wavelet transformation, Wavelets and

Multiresolution Processing; Image Compression; Pseudo colour; Morphological Image

Processing.




1. Gonzalez, R. C. and Woods, R. E. , Digital Image Processing, (2002), Prentice Hall.

2. Pratt, W. K. , Digital Image Processing : PIKS Scientific Inside, (2007), John Wiley &

Sons.

3. Fu, K. S. and Young, T. Y. , Handbook of Pattern Recognition and Image Processing,

(1986), Academic Press.

16


TITLE : Data mining and warehousing

PRE-REQUISITES : COSC 22025 and COSC 44045


LEARNING OUTCOMES:

At the end of this course module the student should be able to understand advanced aspects of

data warehousing and data mining, encompassing the principles, research results and commercial

application of the technologies.

COURSE CONTENT: Review of database technology underpinning data warehousing and data mining;

Data warehouse logical design: Star schemas, Fact tables, Dimensions, Snowflake schemas,

Dimension hierarchies, Data marts.

Data warehouse physical design: Partitioning, Parallelism, Compression, Indexes, Materialized

views;

Data warehouse construction: Data extraction, Transformation, Loading and refreshing, Data

warehouse support in Oracle, Warehouse metadata.

From data warehousing to data mining: OLAP architectures, OLAP operations, SQL extensions

for OLAP.

Data mining approaches and applications: Data mining technologies and implementations,

Techniques for mining large databases, Data mining support in SQL Server, Oracle, Clementine,

Data mining standards.




1. Jarke, M, and et al., Fundamentals of Data Warehouses, (2003, 2nd Edition), Springer.

2. Han, J. and Kamber, M., Data Mining Concepts and Techniques, (2001), Morgan

Kaufmann.

3. Seidman, C., Data Mining with Microsoft SQL Server 2000 Technical Reference,

Microsoft Press.

4. Nagabhushana, S., Data Warehousing OLAP and Data Mining, (2007), New Age

International (P) Limited Publishers.

17


TITLE : E-Business Technologies



LEARNING OUTCOMES:

At the end of this course module the student should be able to understand advanced aspects of e-

commerce and steps involved in launching an online business.

COURSE CONTENT: Foundations of Information Systems in Business, Components of e-Business, Competing with

Information Technology, Comparison of e-Marketing with traditional marketing , Managing

Data Resources, Functional and Cross-Functional e-Business Systems, Enterprise e-Business

Systems in Practice, Electronic Commerce Systems, Decision Support in e-Business, Developing

e-Business Strategies, Developing E-Business Solutions, Electronic Payment Systems,

Evaluation of EPSs, Comparison of EPS with traditional payment systems, Electronics Funds

Transfer, Payment Cards, Electronic cash, electronic checks and electronic wallets, Tools and

Technologies for secure EPS, Security Management of e-Business, Legal and Ethical Issues in e-

Business.




1. Napier, H. A., E-Business Technologies, (2002), Thomson Course Technology.

2. Bidgoli, H., Electronic Commerce: Principles and Practice, (2002), Academic Press,

New York.

3. Deitel, H. M., Deitel, P. J. and Steinbuhler, K., e-Business and e-Commerce for

Managers, (2001), Prentice-Hall, New Jersey.

4. Elsenpeter, R. C. and Velte, T. J., E-Business: A Beginner’s Guide, Osborne, McGraw-

Hill.

5. Amor, D., Electronic Business Revolution, (2000), Prentice Hall, NJ.

18

COURSE CODE : COSC 44184.

TITLE : Natural Language Processing



LEARNING OUTCOMES:

At the end of this course module the student will be able to demonstrate an understanding of the

theory and practices involved in building computer systems for human language processing.

COURSE CONTENT:

Introduction and Overview, Regular Expressions, String Edit Distance and Alignment, Context

Free Grammars, Non-probabilistic Parsing, Information Theory, Language modeling and Naive

Bayes, Part of Speech Tagging and Hidden Markov Models, Viterbi Algorithm for Finding Most

Likely HMM Path, Probabilistic Context Free Grammars, Parsing with PCFGs, Maximum

Entropy Classifiers, Maximum Entropy Markov Models & Conditional Random Fields, Lexical

Semantics, Machine Translation, Unsupervised Language Discovery, Topic Models and

Language in Social Networks, Pragmatics, Information Extraction & Reference Resolution.





1. Jurafsky, D. and Martin, J. H., Speech and Language Processing, (2004), Pearson

Education.

2. Lutz and Ascher, Learning Python, O'Reilly.

3. Manning and Schutze, Statistical Natural Language Processing, MIT Press.

19


TITLE : Special Topics in Computer Science



LEARNING OUTCOMES:

At the end of this course module, the student will be knowledgeable on emerging technologies in

Computer Science.

COURSE CONTENT:

This course is designed with the aim of introducing emerging technologies in Computer Science.

METHOD OF TEACHING AND LEARNING: A combination of lectures, assignments, presentations and practical sessions as applicable.

ASSESSMENT:

End of course examination, assignments and/or practical examination as applicable.


Required reading material will be recommended by the lecturer(s) depending on the topic at the

beginning of the course module.


TITLE : Research Project



LEARNING OUTCOMES:

At the end of this project the student will be able to demonstrate an understanding in planning

and carrying out a substantial research based project in the field of Computer Science and also be

able to express themselves both in verbal and written forms.

COURSE CONTENT:

A study and/or an implementation of a computer system related to major sub disciplines of

Computer Science curriculum under an assigned supervisor.

METHOD OF TEACHING AND LEARNING: A combination of self-study, Seminars, Presentations and a dissertation.

ASSESSMENT: Dissertation, Seminars and Oral presentations.

20


Reading list and material relevant for each selected topic will be provided at the beginning of the

academic year by the supervisor.


TITLE : Industrial Training



LEARNING OUTCOMES:

At the end of this industrial training, the student will be able to work in an industrialized

environment and to analyze, design and implement a real world problem assigned by the

industry.

COURSE CONTENT:

Major aspects to be covered are the main phases of software and/or hardware systems

development, generic skills needed to work in an industrialized environment and understanding

of expectations of an organization.

METHOD OF TEACHING AND LEARNING: Training under the supervision and guidance of a suitable trainer in an IT related industry.

ASSESSMENT: Evaluation of the progress report submitted by the trainer and the technical report submitted by

the student.


Reading and reference material recommended by the relevant industry.

Course Structure and Syllabi of Computer Studies

B.Sc. (General) Degree Programme – Computer Studies

Level Semester Code Title Type Credits

1

I COSC 11014 Theoretical Foundations of Computer

Science

C 4

II COST 12115 Introduction to Programming C 5

2

I COST 21123 Database Management Systems C 3

II COST 22133 Structured Systems Analysis and Design C 3

COST 22144 Web Technology and e-Commerce Applications C 4

3

I COST 31153 Visual Programming C 3

COST 31163 Management Information Systems C 3

II COST 32172 Web Programming C 2

COST 32183 Multimedia Technologies C 3

B.Sc. (Special) Degree Programme – Computer Studies

Level Semester Code Title Type Credits

1

I COSC 11014* Theoretical Foundations of Computer

Science

C 4

II COST 12115* Introduction to Programming C 5

2

I COST 21123* Database Management Systems C 3

II COST 22133* Structured Systems Analysis and Design C 3

COST 22144* Web Technology and e-Commerce Applications C 4

3

I

COST 31414 Mathematics for Information Technology C1 4

COST 31153* Visual Programming C 3

COST 31163* Management Information Systems C 3

COST 31424 Data Structures and Algorithms C 4

PRPL 31012 Professional Placement O 2

II

COST 32434 Statistics for Information Technology C2 4

COST 32172* Web Programming C 2

COST 32183* Multimedia Technologies C 3

COST 32444 Object Oriented Programming C 4

4 I and II

COST 44513 Operating Systems C 3

COST 44522 Human-Computer Interaction C 2

COST 44532 Software Quality Assurance C 2

COST 44542 Software Project Management C 2

COST 44554 Data Communication and Networks C 4

COST 44562 Systems and Network Administration C 2

COST 44574 Object Oriented Analysis and Design C 4

COST 44584 Computer Architecture and Organization C 4

COST 44594 Advanced Database Systems with Applications C 4

COST 41604 e-Business Technologies O 4

COST 41614 Industrial Training O 4

COST 44624 Computer Graphics C 4

COST 44634 Logic Programming C 4

COST 44644 Information Security C 4

COST 43656 Research Project C 6

Note 1. Students are allowed to register to follow either COST 41604 or COST 41614, but not

both in Semester I of Level 4.

2. Other Level 4 course units will be offered either in Semester I or Semester II of Level 3

or Level 4.

3. * signifies course units offered for the B.Sc. (General) Degree Programme.

4. C1 - Compulsory only for students entered to the Special Degree Programme from

the Biological Science stream.

5. C2 - Compulsory for all students who have not followed the course units STAT

11014 and STAT 11021 in Level 1.

Course Units offered for B. Sc. (General) Degree Programme

Level 1

Type/Status : Compulsory

Course Code : COSC11014

Course Title : Theoretical Foundations of Computer Science

Pre-requisites : G.C.E (A/L)

Co-requisites : None

Learning Outcomes:

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

explain fundamental theoretical aspects of Computer Science.

Course Content:

Definition of a Computer, Digital Computers, Main components of a Computer: I/O devices,

CPU, Memory; Organization of a computer, Secondary storage devices and types of Secondary

storage, Peripheral devices; Classification of computers; Software: Systems Software and

application Software; Operating Systems, functions and types of operating systems; Utility

Programs, Translators (compilers, interpreters, assemblers); Application Software Packages;

Application Programs: Algorithms, computer programs, Computer Programming Languages;

Generations of programming languages; Number Systems: decimal , Binary, Octal and

Hexadecimal; Conversion between number systems; Use of number systems; Binary addition

and subtraction; Representation of Characters: ASCII,EBCIDIC, Unicode; Representation of

Numbers: Whole numbers and Floating-point numbers, Sign magnitude form, Two’s

complement form, Scientific form, Standard form; Introduction to Computer Networks:

Networking and Internetworking devices and their functions; Need for standard protocols; Data

transmission; Introduction to the Internet; Services available on the Internet: Word Wide Web

(WWW) and E-mail; Web browsers and search engines; Effective use of e-mail; Information

systems: Types of Information Systems, Systems development Life Cycle (SDLC) ; Social ,

Ethical, Legal and Economic impacts of the use of computers; Cyber crime

Method of Teaching and Learning:Lectures, tutorials and assignments.

Assessment:End of semester examination, tutorials and assignments.

Recommended Reading:

1. Peter Norton, (2006),Introduction to Computers, 6th Edition, Tata McGraw-Hill

Publishing Company limited, India.

2. June Jamrich Parsons and Dan Oja, (2003),New Perspective on Computer Concepts,6th

Edition, Course Technology a division of Thomson learning Inc.

3. B.Ram, (2005),Computer Fundamentals: Architecture and organization, 3rd Edition, New

Age Publications, India.

4. Forouzan, B. A. and Mosharraf, Firouz, (2008), Foundations of Computer Science, 2nd

Edition, Cengage Learning EMEA.


Course Code : COST 12115

Course Title : Introduction to Programming

Pre-requisites : COSC11014


Learning Outcomes:


demonstrate skills on structured programming concepts, algorithm designing and

writing structured programs using a procedural language.

Course Contents:

Introduction to Programming: A brief history and types of programming languages.

Program Design: Introduction to software development, Algorithms, Algorithm specifications

(flow charts, N-S diagrams and pseudo code), Modular programming concepts, Elegance in

program design, Implementing an algorithm using a programming language, Program testing and

program documentation.

The C programming language: Data types, Constants, Identifiers, Variables, Expressions and

assignment, Input and output, Control structures, Functions, Storage classes, Pointers, Scope of

arguments, Structured data types (arrays, structures, unions), Programmer defined data types,

Recursion, File processing, Multi-file programming, Enumeration in C, C preprocessor and

advanced features of C language.

Method of Teaching and Learning:Lectures, tutorials and practical sessions using C.

Assessment: End of semester examination, practical examination and assignments.


1. Gottfried, B.S., (2001),Schaum's Outline of Theory and Problems of Programming in C

,2nd Edition, McGraw Hill Professional Publishing.

2. Kelly, A. and Pohl, I., (1999),A Book on C: Programming in C, 4th Edition, Addison

Wesley Longman Inc.

3. Rajaraman, V., (2004),Computer Programming in C, 6th Edition, Prentice Hall.

Level 2


Course code : COST 21123

Course Title : Database Management Systems

Pre-requisites : G.C.E (A/L)


Learning Outcomes:


describe various logical database architectures,

design & develop databases using relational model and manipulate data,

prepare forms and reports using Structured Query Language.

Course Content:

The role of DBMS in organizations. The database approach: database fundamentals, data

modeling, Entity Relationship Diagram, relational database architecture, logical modeling, data

independence, data dictionary, data manipulation and Structured Query Language (SQL).

Historical database architectures and comparison with relational architecture.

Method of Teaching and Learning:Lectures, supervised hands on practical sessions on SQL,

tutorials and group work.

Assessment: End of semester examination and continuous assessment.


1. Fred R. McFadden, Jeffrey A. Hoofer, and Mary B. Prescott,Modern Database

Management, 7th Edition, Addison-Wesley Pub Co.

2. CJ Date, (2004), Introduction to Database Systems, 8th Edition, Addison Wesley.

3. Elmira R. and Navathe S. B., (2003), Fundamentals of Database Systems, 4th Edition,

Addison Wesley.



Course Title : Structured Systems Analysis & Design

Pre-requisites : COSC 11014


Learning Outcomes:


identify the stages of software development life cycle,

describe the methods of requirements gathering to analyze the system and its

implications,

identify human and technical factors involved in systems analysis and design and the

need for a structured approached to the systems development process,

recognize the importance of user interface design,

demonstrate system analyzing and designing for information systems using

Structured System Analysis and Design Methodology.

Course Content:

Early systems and methods used for system analysis & design. Evolution of system analysis &

design. Software Development Life Cycle (SDLC). System Concept: system approaches, system

elements, systems environment, boundary of a system, sub systems, types of systems, control of

a system. Systems analysis and design: facts gathering techniques, feasibility studies,

requirement analysis, system design, logical & physical design, code design, testing, file/

database design, system implementation and maintenance. Structured System Analysis & Design

Methodology (SSADM) version 4+: modules, stages, steps & tasks of SSADM. Data Flow

Modeling & Logical Data Modeling. CASE tool support.

Method of Teaching and Learning:Lectures, supervised practical sessions, tutorials and group

work.

Assessment:End of semester examination, continuous assessment and group project.


1. Philip L. Weaver, Nicholas Lambrou& Matthew Walkley,(1998), Practical SSADM

version 4+, 3rd Edition, Pitman Publishing.

2. Philip L. Weaver, Nicholas Lambrou& Matthew Walkley, (2002), Practical Business

Systems Development using SSADM: A complete tutorial guide,3rd Edition,Financial

Times/ Prentice Hall.

3. Goodland & Slater, (1995), SSADM Version 4 – A Practical Approach, McGraw Hill.

4. David Hargrave, (1996), SSADM 4+ for Rapid System Development, McGraw Hill.



Course Title : Web Technology and e-Commerce Applications

Pre-requisites : COST 12115


Learning Outcomes:


demonstrate the theoretical knowledge on basic concepts of Web and the Internet

Technologies and practical skills in their usage in critical systems development.

Course Content:

Principles of multimedia document management, Hypertext, Hypermedia, distributed

multimedia, web designing tools, HTML, creation of web sites, Adding multimedia features,

Aspects of maintaining websites, security features;

Ecommerce: Introduction to E-Commerce/E-Business Technology; Internet Fundamentals, XML

Fundamentals, Architecture & Modeling Fundamentals;

E-Business Applications: Architecture of E-Business Applications, Layer-based design,

Messaging and Event-Driven Architecture, Emerging Trends and Best practices;

Web Services & E-Business Automation: Service-Oriented Architectures, XML-Based Business

Automation and Related Support, Web Services Development, Service-Oriented Architectures,

Workflows, Enterprise Service Buses, etc.

Method of Teaching and Learning: Lectures, practical classes and tutorials.

Assessment: End of semester examination, practical examination and assignments.


1. Jeffrey C. Jackson, (2006), Web Technologies: A Computer Science Perspective, Prentice

Hall.

2. Alison Cawsey and Rick Dewar, Internet Technology and E-Commerce, Palgrave

Macmillan.

3. Janice Reynolds, (2004),The Complete E-Commerce Book: Design, Build, and Maintain

a Successful Web-Based Business, CMP Books.

Level 3



Course Title : Visual Programming



Learning Outcomes:





http://www.amazon.co.uk/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books-uk&field-author=Alison%20Cawsey

http://www.amazon.co.uk/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books-uk&field-author=Rick%20Dewar

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Janice%20Reynolds

define the term event-driven programming,

identify components of the Integrated Development Environment (IDE),

characterize the commonly used events in event-driven programming,

describe main controls and their methods and properties,

choose appropriate controls and events to develop user-friendly interfaces,

perform database access through graphical user interfaces by using available classes

and technologies,

implement an information system for a simple, real-world business problem.

Course Content:

Introduction to visual and event-driven programming, Relevance of object oriented programming

concepts, The Integrated Development Environment, Graphical User Interface (GUI) design,

Controls and their Properties, Methods and Events, Data types, variables and constant, Structured

data types, Control structures, Sub programming, Error Handling and Debugging, Database

programming, Design of Reports, Practical sessions using Visual Basic .NET

Method of Teaching and Learning:Lectures, interactive classroom sessions, hands-on practical

sessions, self-study assignments and a group mini project.

Assessment: End of semester exam, in-class assignments, mid-term test, practical examination.


1. Harvey M. Deitel, Paul J. Deitel, Tem R. Nieto, Visual Basic .Net How to Programme,

2nd Edition, Prentice Hall

2. Richard Blair, Jonathan Crossland, Matthew Reynolds, Thearon Willis, Beginning

VB.NET, 2nd Edition, Wrox Publishing

3. Thearon Willis, Beginning Visual Basic 2005 Databases, Wrox Publishing



Course Title : Management Information Systems



Learning Outcomes:


explain the value of information for decision making

examine the suitability of different types of information systems for enterprises

assess the impact of information systems on enterprises.

Course Content:

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Richard+Blair

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Jonathan+Crossland

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Matthew+Reynolds

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Thearon+Willis

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Thearon+Willis

Role of information systems; Strategic role played by information systems; Types of information

systems; E-commerce and e-business application; Enterprise applications; Ethical, social and

legal impact of information systems; Information security

Method of Teaching and Learning: Lectures, in-class discussions and case studies.

Assessment: Listening and speech tests, end of semester examination and in-class assignments.


1. Kenneth C Laudon and Jane P Laudon, (2007), Management Information Systems, 10th

Edition, Prentice Hall

2. James A O’Brien and George M Marakas, (2006), Management Information Systems, 7th

Edition, McGraw Hill

Type/Status : Optional


Course Title : Web Programming



Learning Outcomes:


demonstrate the theoretical and practical skills on the design and techniques for

developing internet-based applications, mainly focusing on web programming.

Course Content:

Internet Overview: Networks, TCP-IP, Web Organization and Addressing, Ports, Packets,

Routers, and Routing, Web Browsers and Web Servers, Client/Server Architectures, Security;

Web Page Basics: HTML, Java Script, and CGI scripts, Designing a web presence, Building

dynamic applications;

Web Applications: Java and Applets (client side applications), CGI (server side applications),

Servlets and JSP (server side applications);

Advanced Web Applications: Crawlers and Search Engines, Advanced Interactive Tools,

Distributed and Redundant Resources; Legal and Ethical Issues.

Method of Teaching and Learning: Lectures, practical classes and tutorials.

Assessment: End of semester examination, practical examination, assignments and a mini

project.


1. Jeffrey C. Jackson, (2006),Web Technologies: A Computer Science Perspective, Prentice

Hall.




2. Deitel and Deitel, (2003),Java How to Program, 5th Edition.

3. M. Fowler, (2002), Patterns of Enterprise Application Architecture.

4. Sebesta and Robert W, (2007), Programming the World Wide Web, 4th Edition, Addison

Wesley.

5. Chris Bates, (2003),Web Programming: Building Internet Applications, John Wiley &

Sons, Inc.

6. Harvey M. Deitel, Paul J. Deitel and Andrew B. Goldberg, Internet & World Wide Web

How to Program, 3rd Edition, Prentice-Hall, Inc.



Course Title : Multimedia Technologies



Learning Outcomes:


identify suitable business applications for multimedia developments,

use the tools, techniques and technologies used for multimedia production,

describe the basic principles of graphic design,

use Flash and Photoshop software in digital graphic design and multimedia projects,

select suitable colors for multimedia products,

describe concepts in non-linear video editing,

create an after effect composition.

Course Content:

Multimedia fundamentals. Principles of design: contrast, repetition, alignment, and proximity.

Four elements in design: typography, images, color, and layout. Introduction to Flash,

Introduction to Photoshop. Color theory. Design and composition. Digital video editing. After

effects. Flash animation.

Method of Teaching and Learning:

Lectures, case discussions, practical sessions, tutorials and group work.

Assessment: End of semester examination, in-class assessments and Continuous assessment.


1. Jim Ames, Color Theory Made Easy: A New Approach to Color Theory and How to

Apply It to Mixing Paints, Watson-Guptill Publications: New York,

2. Ze-Nian Li and Mark S. Drew, (2004),Fundamentals of Multimedia, Prentice-Hall.

3. Tink aka Stephen Downs, Paul Barnes-Hoggett, Glen Rhodes, Craig Swann, Matt

Voerman, Todd Yard, (2005), Flash 8 Essentials, Friends of ED, an Apress Company.

4. Adobe Creative Team, Adobe® Photoshop 7.0 Classroom in a Book, Adobe Press.

http://www.amazon.com/Web-Programming-Building-Internet-Applications/dp/0470017759/ref=pd_bbs_sr_2?ie=UTF8&s=books&qid=1196931548&sr=1-2

Course Units offered for the B. Sc. (Special) DegreeProgramme in Computer Studies

Level 3

Type/Status : Compulsory (Only for the students from the Biological Science stream)


Course Title : Mathematics for Information Technology

Pre-requisites : None


Learning Outcomes:


apply fundamental concepts in Number systems, Coordinate Systems and Vectors,

Mathematical Logic, Set Theory, Relations, Functions, Differentiation and

Integration, Systems of linear equations, Determinants and Matrices to solve simple

real world problems.

Course Content:

Number systems, Coordinate Systems, Fundamentals of Vector algebra, Mathematical Logic

(Propositional and Predicate Logic), Boolean Algebra, Methods of Proofs, Set Theory, Relations

and Functions, Differentiation and Integration, Systems of linear equations, Matrices up to order

4 and Inverse of a non-singular square matrix, Determinant of a square matrix.

Method of Teaching and Learning:A combination of lectures, tutorials and assignments.

Assessment: End of semester examination, tutorials and assignments.


1. Johnsonbaugh,R., (2003), Discrete Mathematics, 5th Edition, MacMillan.

2. Rosen, K. H., (2003), Discrete Mathematics and Its Applications, 5th Edition, McGraw-

Hill,New York.

3. Lipschutz, S., (1976), Discrete Mathematics, McGraw-Hill, New York.

4. Nachman, L.J., (1978), Fundamental Mathematics, John Wiley, New York.

5. Vatssa, B. S., (2002), Discrete Mathematics, 3rd Edition, WishwaPrakashana, India.

6. Bostock, L. and Chandler, S., Pure Mathematics 1,(2002), Stanley Thornes (Publishers)

Ltd.

7. Bostock, L. and Chandler, S., Pure Mathematics 2,(2002), Stanley Thornes (Publishers)

Ltd.



Course Title :Data Structures and Algorithms



Learning Outcomes:


demonstrate a good knowledge on abstract data types in structured programming,

implement and analyze commonly used searching and sorting algorithms.

Course Content:

Introduction to Data structures: A general introduction to data structures, The need for data

structures in programming, Data types, Data structures and abstract data types, Homogeneous

and heterogeneous structures, Static and dynamic structures;

Structures to be considered: Stacks, Queues, Deques, Lists, Trees and Graphs.

Searching Technique: Linear Search and Binary Search;

Sorting Techniques: Internal and External Sorting, Exchange (Bubble) Sort, Insertion Sort,

Selection Sort, Shell Sort, Quick Sort, Heap Sort, Merge Sort and Radix Sort;

Hashing Techniques.

Method of Teaching and Learning:A combination of lectures, tutorials and practical sessions

using C language.

Assessment:End of semester examination, practical examination and assignments.


1. Kelley, AI & Pohl, Ira, A Book on C, (l998), 4th Edition, Addison-Wesley.

2. Kruse R.L., Leung B. P. and Tondo C. L., Data Structures and Program Design (2001),

Pearson Education (Singapore) Pte. Ltd.

3. Gottfried, B., Programming with C. (l999), 2nd Edition, Tata McGraw-Hill Company Ltd.

4. Rajaraman, V., Computer Programming in C, (1994), Prentice-Hall of India (Pvt) Ltd.

5. Langsam Y., Augenstein M. J. and Tenenbaum A. M., Data Structures using C and C++

(2002), 2nd Edition, Pearson Education (Singapore).



Course Title : Statistics for Information Technology

Pre-requisites : COST 31414 (Only for the students from the Biological Science stream)


Learning Outcomes:


apply fundamental concepts in descriptive statistics and probability theoryfor real

world problem solving,

apply techniques provided in Statistical Software Packages to solve problems in

Statistics.

Course Content:

Introduction: Rationale for learning Statistics, How the Statistics serves the scientists, Basic

terminology, Essence of Science, Types of measurement and Statistical approach. Descriptive

Statistics: Frequency distribution, Measures of Central Tendency, Measures of Dispersion and

Shapes of distributions. Probability: Permutations and Combinations, Random or non-

deterministic experiment, Sample space, Events and event space, Classical definition of

probability, Frequency definition of probability, Axiomatic definition of probability, Conditional

probability, Partition of a sample space, Total probability and Bayes’ Theorem. Probability

distributions: Binomial distribution, Poisson distribution and Normal distribution. Fitting a

theoretical distribution to set of observed values: Binomial, Poisson and Normal distributions.

Sampling and sampling distributions: Random sampling, Stratified sampling, Sampling error,

Sampling distributions and Degrees of freedom. Tests of Hypotheses: Basic terminology of

scientific research, Rationale of scientific decision making, Limitations of scientific decisions

and the ways that they may be in error. Decisions about relationships: Introduction to

Correlation, Relationship between interval/ratio variables, Geometric appearance of relationship,

Product-Moment Correlation.

Introduction of one of the statistical packages Minitab, SPSS, SAS or R, features of the package

and how to use the package to solve problems in Statistics.

Method of Teaching and Learning: A combination of lectures, tutorials, practical sessions and

assignments.

Assessment: End of semester examination, practical examination, tutorials and assignments.


1. Murray R. Spiegel and Larry J. Stephens, (2006), Theory and Problems of Statistics, 3rd

Edition, Tata MaGrew-Hill Publishing Company Limited.

2. Runyon Richard P., HarberAndrey, Pittenger David J., Coleman Kay, (2002),

Fundamentals of Behavioural Sciences, 8th Edition, McGraw-Hill.

3. Horvath Theadore, (1985), Basic Statistics for Behavioural Sciences, LittleBrown&

Company.

4. Erricker B. C., Advanced General Statistics, (1975), Reprint, Alden Press Oxford.



Course Title :Object Oriented Programming

Pre-requisites : COSC 11014, COST 12115


Learning Outcomes:


demonstrate the knowledge of the theory of object–oriented systems,

demonstrate how an object-oriented programming language upholds object-oriented

concepts,

effectively use an industry relevant object-oriented programming language.

Course Content:

Background and motivation of Object Oriented Methods, Concepts of Object-Oriented project

management issues, Principles and features of an industry standard Object-Oriented

Programming language (e.g.: Java/C++). Specific topics include Class and Object models,

Object declaration and creation, Instantiable classes, Visibility modifiers, arrays of objects, Self-

referencing pointers, Re-use of code, Static methods, Arithmetic Expressions, Variables, Scope,

Event-Driven Input and Output, File Objects and Looping Statements, Primitive and reference

types, Strings, Use of String buffer, Passing objects as parameters, Exceptions and Additional

Control Structures, Inheritance, Polymorphism, Encapsulation, Selection, collaboration,

hierarchical classification, using super, creating multilevel hierarchy, method overriding, using

abstract classes, use of final and other relations among classes and objects as well as both static

and dynamic system models and implementations.

Method of Teaching and Learning:A combination of lectures, tutorials and practical

sessionsusing Java/C++.



1. Booch, Rumbaugh and Jacobson,(1996),The Unified Modeling Language User Guide,

Pearson Education.

2. Eriksson and Penker,(1998), UML Toolkit, John Wiley & Sons.

3. Wu, T.,(2001),An Introduction to Object-Oriented Programming with Java, 2nd Edition,

McGraw Hill.

4. Gamma et at. ,(1995),Design Patterns: Elements of Reusable Object-Oriented Software,


Level 4



Course Title :Operating Systems



Learning Outcomes:


explain the role and characteristics of modern operating systems,

describe memory management, file management and storage management,

describe similarities and differences between modern operating systems such as Unix,

Windows etc.,

implementbasic and important OS algorithms.

Course Content:

Computer system overview; Process description and control; Concurrency issues (mutual

exclusion and synchronization); Deadlock prevention, avoidance and detection; Memory

management and paging; Virtual memory; CPU scheduling algorithms; Disk scheduling

algorithms; File management; Other topics (security, networking) as time allows.


Assessment:End of semester examination and assignments.


1. Abraham Silberschatz, Peter Baer Galvin and GregGagne, (2005), Operating Systems

Concepts, 8th Edition, John Wiley Sons.

2. Tanenbaum, A. S., (2008), Modern Operating Systems, 3rd Edition, Pearson Prentice

Hall.



Course Title : Human-Computer Interaction

Pre-requisites :COST 22144, COST 31153


Learning Outcomes:


understand how interface design practices and methods can be integrated with user

centred principles and methods now being employed,

identify current trends in HCI research,

understand the nature of the HCI design process,

apply an integrated perspective to the design process,

understand the difficulties and pitfalls of translating theory and principles derived

from research findings into practical advice on user-centred design,

apply metaphorical reasoning and conceptual models to user interface design,

explore strategies for improving web site usability,

describe the major aspects of usability engineering,

apply usability and design principles to the evaluation of current interfaces.

Course Content:

Human factors issues in the development of software, use of database systems, and design of

user interfaces for interactive systems. Fundamentals of HCI (theories, models, usability studies

and controlled experimentation) and software engineering (gathering user interface requirements,

prototyping user interfaces) with user interface development environments. Issues include:

command languages, menus, forms, and direct manipulation, graphical user interfaces, computer

supported cooperative work, information search and visualization, World Wide Web design,

input/output devices, and display design. Evaluation of user interfaces and current trends in HCI

research.

Method of Teaching and Learning: A combination of lectures, tutorials and assignments.



1. Ben Shneiderman and Catherine Plaisant, (2010), Designing the User

Interface:Strategies for Effective Human-Computer Interaction, 5thEdition, Addison-

Wesley.

2. Alan Dix, Janet E. Finlay, Gregory D. Abowd and Russell Beale, (2004), Human

Computer Interaction, 3rd Edition, Pearson Education.

3. Soren Lauesen,(2005), UserInterfaceDesign: A Software Engineering Perspective,

Pearson Education.

4. Winograd, T. and Flores, F.,(1987),Understanding Computers and Cognition: A New

Foundation forDesign, Addison-Wesley.

5. J. Preece, Y. Rogers, H. Sharp, D. Benyon, S. Holland and T. Carey,(1994), Human-

Computer Interaction: Concepts and Design, Addison-Wesley.



Course Title : Software Quality Assurance



Learning Outcomes:


develop an understanding of software quality and approaches to assure software

quality,

differentiate software verification and validation,

describe the processes of software quality management and software configuration

management,

implement different types of software testing methods.

Course Content:

Software Process assessment overview; Software Quality management; Verification and

Validation; Software configuration Management; Software standards; Software Testing- white

box testing/ black box testing; Software defect prevention.




1. DanielGalin, (2009), Software quality assurance - from theory to

implementation, PearsonEducation.

2. S.Watts Humphrey, (1999), Managing the software process, Addison Wesley.



Course Title : Software Project Management



Learning Outcomes:


explain how a project can be broken down into stages and what each stage contributes

to the project,

select appropriate techniques for use in the stages of a project,

justify the appropriateness of these techniques, and apply them to practical situations,

explain the limitations of the project approach in developing information/software

systems.

Course Content:

Software Project Management Techniques and Concepts: The profile of a project, understanding

and decomposing the task, Estimation and risk, Scheduling and resourcing, Monitoring and

control, Quality assurance, Maintenance, Cost benefit analysis, Software project documentation

issues, The project team and the project manager, Alternative approaches and emerging issues,

Human Resource Management, Professional, legal and ethical issues, Computer crime,

Computer law in Sri Lanka.

Method of Teaching and Learning: A combination of lectures and assignments.

Assessment: End of semester examination and assignments.


1. Yeates, D. and Cadle, J., (1996),Project Management for Information Systems, 2nd Edition,

Pitman Publishing.

2. Pressman, R. S., (1997), Software Engineering, 4th Edition, McGraw-Hill.

3. Kemerer, C. F., (1997), Software Project Management: Readings and Cases, Irwin

Publishers.

4. Hughes, B., Cotterell, M., (2002), Software Project Management, McGraw-Hill.


Course Code :COST 44554

Course Title : Data Communication and Networks



Learning Outcomes:


examine the use of computer networks,

identify the forces behind their development,

demonstrate a good understanding of the design and implementation of various

network topologies, architectures, protocols and algorithms.

Course Content:

Introduction: Data Communication, Network structures, Types of networks, The Internet,

Protocols and standards, Layers of the OSI model; The physical layer: Transmission media

(guided and unguided), analog and digital transmission, Transmission impairment, Encoding

techniques, Modulation techniques and Modems, Multiplexing, Circuit switching; Telephone

Networks and DSL technology; The medium access sub-layer: Ethernet (CSMA/CD), token bus,

token ring and FDDI; The data link layer: Framing, error detection and correction, error control

and flow control and data link protocols; The network layer: Addressing, Routing algorithms,

Internetworking and network layer protocols; The transport layer: Transport layer protocols

(UDP and TCP) and connection management; The session layer: Token management and

synchronization. The presentation layer: Data compression, data security and encryption. The

application layer: Client-Server model, Application level protocols for File transfer, Electronic

mail, Network management, Hypertext transfer and World Wide Web; Advanced

telecommunication services and developments: ISDN, Frame Relay Networks, ATM Networks,

packet switching and X.25 Networks; LAN, MAN , WAN and Networking software;

Networking and Internetworking Devices.


Assessment: End of semester examination, practical examination, tutorials and assignments.


1. Forouzan B. A., (2004), Data Communications and Networking, 4th Edition, McGraw Hill.

2. Forouzan B. A.,(2002),Local Area Networks, 1st Edition, Tata McGraw Hill Edition.

3. Stallings W., (2009), Data and Computer Communications, 8thEdition, Pearson Education

Inc.

4. Tanenbaum A.S., (2008), Computer Networks, 4thEdition, Prentice-Hall International.



Course Title : Systems and Network Administration

Pre-requisites : COSC 11014, COST 44513, COST 44553


Learning Outcomes:


describe the structural components of the Unix/Linux and Windows environments,

install and configure workstations, servers, software and network devices,

handle system backup and recovery,

create and manage user accounts and groups,

monitoring and optimizing system/network performance and security.

Course Content:

An overview of the Unix/Linux and Windows OS/servers; Virtual Machine Environment;

System startup and shutdown; Installing Unix/Linux; Installing and configuring network devices

and operating systems; Package managers and software installation; Bash Shell/vim editor;

Administrator responsibilities and getting help; Administrating user accounts and groups; File

system and storage device; System Backup and Emergency Recovery; System/Network

Monitoring.

Method of Teaching and Learning:A combination of lectures, practical, assignments.



1. Wale Soyinka, (2009), Linux Administration – A Beginner’s Guide, 5th Edition, McGraw-

Hill.

2. Mark Burgess, (2004), Principles of network and system administration, 2nd Edition, John

Willy & Sons Ltd.



Course Title : Object Oriented Analysis and Design



Learning Outcomes:


design software in an object-oriented environment,

use UML as a notation to support this design.

Course Content:

The course covers life-cycle and tasks for OO software development, up to, the actual writing of

code;

Object Concepts: Objects ,Classes, Inheritance, Software Development Methodology, Using

UML, CRC Cards, Requirements Capture, Business Perspective, Developer Perspective,

Analysis: Introduction, Static Analysis, Dynamic Analysis;

System Design: Introduction, Networked System Topologies, Choosing Technologies,

Partitioning Software, Subsystem Design, Designing the Business Logic, Persistence using a

Relational Database, Finalizing the User Interfaces, Designing the Business Services, Thread

Safety;

Code Specification: Object-Oriented Specification, Design by Contract, Informal Specification

in Java, UML Notation will be used throughout the course unit.

Method of Teaching and Learning:A combination of lectures, tutorial and practical sessions

using Java/C++.



1. Booch, Rumbaugh and Jacobson., (1996), The Unified Modeling Language User Guide,

Pearson Education.

2. Eriksson and Penker., (1998), UML Toolkit, John Wiley & Sons.

3. Wu, T., (2001),An Introduction to Object-Oriented Programming with Java. 2nd Edition,

McGraw Hill.

4. Gamma et al.,(1995), Design Patterns: Elements of Reusable Object-Oriented Software,




Course Title : Computer Architecture and Organization



Learning Outcomes:


demonstrate an understanding of the important aspects of the hardware of a

conventional computer and the architectural features of microprocessors provided to

support efficient computation.

Course Content:

Introduction to Computer Organization, Architecture and Design Concepts;

Basic Principles: Boolean Algebra, Combinational and Sequential circuits, logic design

techniques, Karnaugh map, Latches, Flip-flops, Half-adder, Full-adder, counters and Shift

registers;

Classification of Processor architectures: SISD, SIMD and MIMD;

Instruction sets: Sequential execution of the instructions with respect to a single Processor,

Machine instruction characteristics, Instruction set design, Addressing modes and formats,

Instruction Pipelining;

Input and Output: I/O modules, System bus, I/O channels and processors;

CPU Structure: Processor and register organization, Design of ALU, Arithmetic, Shift and logic

control systems, RISC machine and parallel architectures;

Memory Management: Memory hierarchy, Virtual memory and Cache memory, Performance

comparison;

Programmed data transfer: Interrupts and interrupt handling systems, Assembly language

programming.

Method of Teaching and Learning: A combination of lectures and tutorials.

Assessment: End of semester examinationand assignments.


1. Stallings W., (2008), Computer Organization and Architecture, 7thEdition, PrenticeHall

International Inc.

2. Ram B., (2007), Computer Fundamentals: Architecture and Organization, 4th Edition,

Wiley Eastern Limited.

3. Tananbaum A. S., (2006), Structured Computer Organization, 5thEdition, Prentice Hall

International Inc.

4. Morris Mano M., (2007), Computer System Architecture, 3rd Edition, Prentice-Hall

International Inc.



Course Title : Advanced Database Systems with Applications



Learning Outcomes:


demonstrate the theoretical knowledge on advanced database management system

design principles and techniques, and practical skills on applications of them.

Course Content:


ISAM, B-tree, more trees, hashing, textual, distributed/P2P indexing; Query processing: scan,

sort, hash, index-based, systems view; Buffer management; Query optimization: Rewrite, cost

estimation, algorithms; Online query processing and optimization, downsizing the DBMS;

Transaction Processing Concepts; Concurrency Control Techniques; Practical Database Design

and Tuning; Database Recovery Techniques; Object-Oriented Databases; Deductive databases;

Emerging Database Technologies and Applications; Distributed Databases and Client-Server

Architecture; XML, DTD, XPath, XQuery, XSLT, XML storage, XML indexing, streaming

XML; Database Security and Authorization; Enhanced Data Models for Advanced Applications;

Recent advances in database systems such as Spatial and Active Databases; Introduction to Data

warehousing and data mining.

Method of Teaching and Learning:A combination of lectures, practical sessions on Oracle,

tutorials, assignments and group projects.



1. Ullman, J. D. and Widom, J., (2002), Database Systems: The Complete Book, Hector


2. Stonebraker M., andHellerstein, J. M., (1998), Readings in Database Systems, 3rd Edition,

Morgan Kaufman.

3. Elmasri, R. and Navathe, S. B.,(2006), Fundamentals of Database Systems,5th Edition,

Addison-Wesley Longman Publishing Co., Inc.

4. McFadden, F. R., Hoffer, J. A. and Prescott, M. B., (2006), Modern Database Management

8th Edition, Pearson Education Series.

5. Ramakrishnan, G., (2002), Database Management Systems, 3rd Edition, McGraw-Hill.

6. Hansen, G. W. and Hansen, J. V., (2002), Database Management and Design, 2nd Edition,

Prentice Hall.

7. Brundage, M., (2004), XQuery: The XML Query Language, Addison Wesley Longman

Publishing Co., Inc.

Type/Status :Optional


Course Title : e-Business Technologies



Learning Outcomes:


understand advanced aspects of e- commerce and steps involved in launching an

online business.

Course Content:

Foundations of Information Systems in Business, Components of e-Business, Competing with

Information Technology, Comparison of e-Marketing with traditional marketing , Managing

Data Resources, Functional and Cross-Functional e-Business Systems, Enterprise e-Business

Systems in Practice, Electronic Commerce Systems, Decision Support in e-Business, Developing

e-Business Strategies, Developing E-Business Solutions, Electronic Payment Systems,

Evaluation of EPSs, Comparison of EPS with traditional payment systems, Electronics Funds

Transfer, Payment Cards, Electronic cash, electronic checks and electronic wallets, Tools and

Technologies for secure EPS, Security Management of e-Business, Legal and Ethical Issues in e-

Business.

Method of Teaching and Learning:A combination of lectures and assignments.



1. Napier, H. A., (2002), E-Business Technologies, Thomson Course Technology.

2. Bidgoli, H., (2002),Electronic Commerce: Principles and Practice, Academic Press, New

York.

3. Deitel, H. M., Deitel, P. J. and Steinbuhler, K., (2001), e-Business and e-Commerce for

Managers, Prentice-Hall, New Jersey.

4. Elsenpeter, R. C. and Velte, T. J., E-Business: A Beginner’s Guide, Osborne, McGraw-

Hill.

5. Amor, D., (2000), Electronic Business Revolution, Prentice Hall, NJ.



Course Title : Industrial Training

Pre-requisites : All COST core course units

Co-requisite : None

Learning Outcomes:


work in an industrialized environment and to analyze, design and implement a real

world problem assigned by the industry.

Course Content:

Major aspects to be covered are the main phases of software and/or hardware systems

development, generic skills needed to work in an industrialized environment and understanding

of expectations of an organization.

Method of Teaching and Learning:This internship is for one complete semester covering

nearly 600 hours. Training is under the supervision and guidance of a suitable trainer in an IT

related industry.

Assessment:Evaluation of the progress report submitted by the trainer and the technical report

submitted by the student.

Recommended Reading:Reading and reference material will be recommended by the relevant

industry.



Course Title : Computer Graphics

Pre-requisites : COST 12115, COST 31414 (For Biological Science students)


Learning Outcomes:


demonstrate a good understanding of concepts behind computer graphics and be able

to apply the techniques and algorithms for modeling and rendering 2D and 3D

graphical objects.

Course Content:

Hardware and software components of graphics systems, Fundamental techniques in graphics

Output and filled data primitives. Graphic systems, Scan conversions, 2D and 3D geometric

transformations. Two dimensional viewing: viewing pipeline, clipping, and windowing. Three

dimensional viewing: viewing pipeline, viewing parameters, projections, viewing

transformations, clipping, visible surface detection. Basic rendering, Introduction to illumination

models and surface rendering, Geometric modeling and representations, Computer animation,

Visualization.

Method of Teaching and Learning:A combination of lectures, assignments and practical

sessions using OpenGL.



1. Hearn, D. and Baker, M.P., (2004), Computer Graphics with OpenGL, 3rdEdition, Prentice

Hall.

2. Giambruno M., (2002), 3D Graphics&Animation, Prentice Hall.

3. Hearn, D. and Baker, M.P., (1997), Computer Graphics C Version, 2ndEdition, Prentice

Hall.

4. Foley, J. D., van Dam, A, Feiner, S. K. and Hughes, J. F., (2002), Computer Graphics,

2ndEdition, Pearson Education.

5. Xiang, Z. and Plastock, R., (2005), Theory and Problems of Computer Graphics,

2ndEdition, Tata McGraw-Hill Publishing Company Limited New Delhi.



Course Title : Logic Programming

Pre-requisites : COST 12115, COST 31414 (For Biological Science students)


Learning Outcomes:


demonstrate an understanding of important aspects in an Intelligent Knowledge Based

System,

demonstrate skills in writing programs using PROLOG.

Course Content:

Introduction: Introduction to Artificial Intelligence (AI), Major areas of AI, Description of

Intelligent Knowledge Based Systems (IKBS);

Knowledge and Reasoning:Knowledge representation and types of reasoning;

Declarative/Procedural Programming Languages;

Logic Programming: Introduction to Logic Programming using PROLOG.

Basic Concepts: Facts, Rules and Queries, Data Objects, Goal Matching, Unification and

Backtracking, Recursion in Prolog, List manipulation in Prolog; Search strategies and Search

trees;

Cut and Negation: The Use of the Cut, Negation as Failure, Problems with Cut and Negation,

Implementing IF-THEN-ELSE condition;

Input and Output: Input and Output in Prolog, Communicating with files, Processing files of

terms.

Advanced features in Prolog: Difference Lists, Binary Trees in Prolog;

Some applications of Prolog: Searching and Sorting Techniques, Problem solving, Theorem

proving, Database applications, etc.;

Logic and Prolog: Deductive reasoning in Propositional and Predicate Logic, Resolution in

Propositional and Predicate Logic, First-order Predicate logic, Prolog and First-order Predicate

logic, Writing Logical Statements, Conversion of Logical Statements into Normal Form,

Unification, Unification algorithm, Resolution principle.

Method of Teaching and Learning:A combination of lectures, assignments and practical

sessions.



1. Bratko, Ivan, (2001), Prolog Programming for Artificial Intelligence, 3rd Edition, Addison-

Wesley.

2. Sterling, L & Shapiro, E.,(1994), The art of Prolog: Advanced programming Techniques,

The MIT Press.

3. KaushikSaroj,(2007), Logic and Prolog Programming, 1st Edition, New Age International

Publishers, India.

4. Russel, S. and Norvig, P., (2005), Artificial Intelligence – A Modern Approach, 2ndEdition,

Prentice-Hall of India Private Limited.

5. Shinghal, R., (1992), Formal Concepts in Artificial Intelligence, 1st Edition, Chapman &

Hall.

6. Luger, G. F.& Stubblefield, W. A., (1993), Artificial intelligence, 2nd Edition, The

Benjamin/ Cummings Publishing Company, Inc.

7. Mc Donald C, Yazdani M.,(1990),Prolog Programming, 1st Edition, Blackwell Scientific

Publications.



Course Title : Information Security



Learning Outcomes:


understand the need for good security management,

identify the security attacks associated with computer networks and how to provide

security against such attacks.

Course Content:

An introduction to cryptography and security mechanisms: Security Services, mechanisms and

Attacks;

Conventional cryptography: Symmetric Ciphers, Block Ciphers; Modular Arithmetic;

Public-key cryptography: Key management, Message Authentication and Hash functions, Digital

Signatures and Authentication Protocols;

Network security: Protection of data transferred over commercial information networks, Generic

security technologies relevant to networks, user identification techniques, authentication

protocols and key distribution mechanisms, Electronic mail security, IP Security, Web Security;

Computer Security: Defining security requirements (security policy). Intruders, Malicious

Software, Firewalls, Security models, The main security features and mechanisms in operating

systems, Security-related issues of computer architecture.

Security of Middleware: Software protection, Standards and security evaluation criteria,

Security-related standards, Guidelines for managing IT security.

Method of Teaching and Learning:A combination of lectures and assignments.



1. Stallings, W., (2006), Cryptography and Network Security, 4th Edition, Prentice-Hall of

India Private Limited.

2. Pfleeger, C. P. and Pfleeger, S. L., (2005), Security in Computing, 3rd Edition, Pearson

Education.

3. Bishop, M.,(2005),Introduction to Computer Security, Addison-Wesley, Pearson

Education, Inc.

4. Carroll, J M, (1996), Computer Security, Butterworth-Heinemann.

5. Leiss ,E.L., (1962), Principles of Data Security, Plenum Press.

6. Forouzan, B, A., (2007), Cryptography and Network Security, Tata McGraw-Hill

Publishing Company Ltd, India.



Course Title : Research Project

Pre-requisites :All COST core course units


Learning Outcomes:


demonstrate an understanding in planning and carrying out a substantial research

based project in the field of Information Technology/Computer Science,

express themselves both in verbal and written forms.

Course Content:

A study and/or an implementation of a computer system related to major sub disciplines of

Information Technology/Computer Science curriculum under an assigned supervisor.

Method of Teaching and Learning:A combination of self-study, Seminars, Presentations and a

dissertation.

Assessment:Dissertation, Seminars and Oral presentations.

Recommended Reading:Reading list and material relevant for each selected topic to be

provided at the beginning of the academic year by the supervisor.

Level 1

Course Code: STAT 11014

Title: Statistical Modelling

Pre-requisite: A/L Combined Mathematics/Mathematics.

Co-requisite: STAT 11021

Learning Outcomes:

On successful completion of this course module the students will be able to: (i) demonstrate

the importance of statistics in making decisions on day-to-day life problems, (ii) construct

appropriate statistical models for real-life situations and apply concepts and procedures in

statistical decision making to those models.

Course Content: Introduction: Rationale for learning Statistics, How the Statistics serves

the scientists, Basic terminology, Essence of Science, Types of measurement and Statistical

approach. Descriptive Statistics: Frequency distribution, Measures of Central Tendency,

Measures of Dispersion and Shapes of distributions. Probability: Algebra of sets,

Permutations and Combinations, Random or non-deterministic experiment, Sample space,

Events and event space, Classical definition of probability, Frequency definition of

probability, Axiomatic definition of probability, Conditional probability, Partition of a

sample space, Total probability and Bayes' Theorem. Probability distributions: Binomial

distribution, Poisson distribution and Normal distribution. Fitting a theoretical distribution

to set of observed values: Binomial, Poisson and Normal distributions. Sampling and

sampling distributions: Random sampling, Stratified sampling, Sampling error, Sampling

distributions and Degrees of freedom. Tests of Hypotheses: Basic terminology of scientific

research, Rationale of scientific decision making, Limitations of scientific decisions and the

ways that they may be in error. Decisions about relationships: Introduction to Correlation,

Relationship between interval/ratio variables, Geometric appearance of relationship, Product-

Moment Correlation. Linear Regression: Prediction of one variable from another, Linear

regression.

Recommended reading:

1. Runyon Richard P., Harber Andrey, Pittenger David J., Coleman Kay, 8th edition, (2002),

‘Fundamentals of Behavioural Sciences’, McGraw-Hill.

2. Horvath Theadore, (1985), ‘Basic Statistics for Behavioural Sciences’, LittleBrown &

Company.

3. Erricker B. C., (1975), Reprint, ‘Advanced General Statistics’, Alden Press Oxford.

Method of teaching and learning: A combination of lectures and tutorials.

Assessment: End of course examination and assignments.


Title: Statistical Laboratory I

Pre-requisite: A/L Combined Mathematics/Mathematics.

Co –requisite: STAT 11014

Learning Outcomes:

On successful completion of this course module the students will be able to apply techniques

provided in Statistical Packages to solve problems in Statistics.

Course Content: Introduction of one of the statistical packages Minitab, SPSS, or SAS,

Features of the package, How to use the package, Solving problems based on the course unit

STAT 11014 using the statistical package.


Manuals relevant to statistical packages

1. Runyon Richard P., Harber Andrey, Pittenger David J., Coleman Kay, 8th edition, (2002),

‘Fundamentals of Behavioural Sciences’, McGraw-Hill.

2. Horvath Theadore, (1985), ‘Basic Statistics for Behavioural Sciences’, LittleBrown &

Company.

3. Erricker B. C., (1975), Reprint, ‘Advanced General Statistics’, Alden Press Oxford.

Method of teaching and learning: Laboratory work.

Assessments: End of course practical examination and assignments


Title: Probability Distributions and Applications I

Pre-requisite: STAT 11014 & STAT 11021

Co-requisite: None

Learning Outcomes:

On successful completion of this course module the students will be able to design an

experiment and generate the probability distribution corresponding to a given real-life

situation involving one variable and apply principles of Probability and Statistics to such

experiments.

Course Content: Random variables: Rationale for the introduction of random variables,

Definition of a random variable, Types of random variable. Distributions: Discrete

probability density function of a discrete random variable, Probability density function of a

continuous random variable and Cumulative probability distribution function of a random

variable. Expectation of a function of a random variable: General definition, Properties of

expectation, Mathematical expectation, Variance and Standard deviation, Moments, Moment

generating function, Probability generating function and Characteristic function. Discrete

Probability Distributions: Discrete uniform, Bernoulli, Binomial, Poisson, Geometric and

Negative binomial. Continuous Probability Distributions: Uniform, Normal, Negative

exponential and Gamma.


1. Alexander M. Mood., Franklin A. Graybill, Pittenger Duane C. Boes, 3rd edition, Reprint

(2005), ‘Introduction to the Theory of Statistics’, McGraw-Hill.

2. Hoel Paul G., Port Sidney C., Stone Charles J., 1st edition, (1971), ‘Introduction to

Probability Theory’, Houghton Mifflin Company




Title: Operational Research (OR) I

Co-requisite: None

Learning Outcomes: At the end of this course unit the students should be able to (i)

translate real life situations involving linear relationship into Linear Programming (LP)

Models, (ii) apply OR techniques in solving LP models.

Course Content: Introduction to OR, Construction of Mathematical Models, Solution

Techniques: Graphical Method, Simplex Method: Algebraic Approach, Tabulation

Approach : Standard Form, Non Standard Form, Duality and Sensitivity Analysis:

Formulation of the dual problem, Primal Dual relationship, Dual Simplex method, The

Primal-Dual method, Interior Point Method: Karmarkar"s Projective Algorithm.

Recommended readings: 1. Fredrick S. Hiller, Gerald J. Lieberman, 8th Edition,(2005),’Introduction toperations

Research’, Mc-Graw Hill.

2. Hamdy A.. Taha, 8th Edition, (2007), ‘Operations Research’, Tara Mc-Graw Hill.

3. Bazaraa M.S., Javis J.J., Sherali H. D., 2nd Edition, (1990), ‘Linear programming and

network flows’.

Methods of teaching and learning: A combination of lectures and tutorials. Assessment: End of course examination and assignments.

Level 2


Title: Probability Distributions and Applications II

Pre-requisite: STAT 12033

Co-requisite: None

Learning Outcomes: On successful completion of this course module the students will be

able demonstrate the knowledge and the skills of multi dimensional probability models in

solving real-life situations involving more than one variable.

Course Content: Two-dimensional Random variables: Rationale for the introduction of

two-dimensional random variables, Cumulative distribution function of two-dimensional

random variable: Definition, Properties of bivariate cumulative distribution function, Joint

density functions, Marginal density functions, Conditional probability distributions,

Independence and related theorems. Expectations: Expectation of a function of two-

dimensional random variable, Covariance and correlation coefficient, Conditional

expectation and related theorem, Conditional variance and related theorem, Joint moments,

Joint moment generating function, Uncorrelated random variables and Cauchy-Schwartz

Inequality. Distributions of functions of random variables and Expectations:

Distributions of sum, difference, product and quotient of two continuous random variables,

Expectations and related theorems. Probability distributions of maximum and minimum

of a set of random variables, Moment generating technique for obtaining probability

distributions, Transformations, Sampling and sampling distributions: Sampling,

Distribution of a sample, Sample moments, Sample variance, Law of Large Numbers,

Central Limit theorem, sampling from Normal distribution, Chi-square distribution, Student’s

t distribution and F distribution.


Alexander M. Mood., Franklin A. Graybill, Pittenger Duane C. Boes, 3rd edition, Reprint

(2005), ‘Introduction to the Theory of Statistics’, McGraw-Hill.




Title: Statistical Inference I


Co-requisite: None

Learning Outcomes: At the end of this course unit students should be able to demonstrate a

good knowledge and techniques of estimation in finding estimators of the population

parameters.

Course Content: Techniques of Finding Point Estimators: Introduction to Point Estimation; Methods of

Estimation : Methods of Moments, Maximum Likelihood Method, Bayes Estimators: Loss

and Risk Functions, Prior and Posterior Distributions, Bayes Risk, Bayes Estimator,

Properties of Point Estimators: Closeness, Mean – Squared Error, Unbiasedness,

Consistency and BAN, Loss and Risk Functions, Sufficiency, Unbiased Estimation:

UMVUE, Cramer-Rao Inequality, Sufficiency and Completeness, Rao-Blackwell Theorem,

Lehmann-Scheffe Theorem;

Interval Estimation: An Introduction to Intervals, Pivotal Quantity Method, Sampling

from Normal Distribution: Confidence Interval for the Mean, Confidence Interval for the

variance, Simultaneous Confidence Region for the Mean and Variance, Confidence Interval

for difference in Means, Approximate Confidence Intervals, Basian Interval Estimates

Recommended readings: 1. Alexander M.. Mood , Franklin A..Graybill , Pittenger Duane C. Boes, 3rd Edition,

Reprinted ( 2005 ), ’Introduction to the Theory of Statistics’, McGraw-Hill.

2. Ronald E. Walpole, Raymand H..Mayers, 6th Edition (1997), ‘Probability and Statistics

for Engineers and Scientits’.

Methods of teaching and learning: A combination of lectures and tutorials. . Assessment: End of course examination and assignments.


Title: Statistical Inference II


Co-requisite: None

Learning Outcomes: At the end of this course unit students should be able to demonstrate

the knowledge of testing hypotheses in making decisions.

Course Content:

Test of Hypotheses: Introduction and Termonology to Hypotheses, Simple Versus

Simple : Simple Likelihood Ratio Test, Most Powerful Test : Definition, Neyman-Pearson

Lemma.Composite Hypotheses: Generalized Likelihood Ratio Test, Uniformly Most

Powerful Test ; Sampling from the Normal Distribution: Tests on the mean, Test on the

variance, Tests on the several Means, Tests on Several Variances; Chi-Squre Tests:

Asymptotic Distribution of Generalized Likelihood-ratio, Chi-square Goodness-of-fit Test,

Test of Independence in Contingency Tables : Two-way Contingency Tables;

Nonparametric Methods: Introduction to Nonparametric Methods, Equality of Two

Distributions: Two-sample Sign Test, Run Test, Signed Rank Test, Median Test, Rank-sum

Test, Wald-wolfwoitz Runs Test.

Recommended readings: 1. Alexander M.. Mood , Franklin A..Graybill , Pittenger Duane C. Boes, 3rd Edition,

Reprinted ( 2005 ), ‘Introduction to the Theory of Statistics’, McGraw-Hill.

2. Ronald E. Walpole, Raymand H..Mayers, 6th Edition,(1997),’Probability and Statistics

for Engineers and Scientits’.



Title: Survey Methods and Sampling Techniques


Co-requisite: None

Learning Outcomes: At the end of this unit the students will be able to demonstrate the

knowledge in planning surveys and analyzing the results using the most appropriate sampling

technique.

Course Content: Introduction and terminology, Techniques of data collection,

Sampling methods: Introduction, Simple Random Sampling, Sampling Proportions and

Percentages, Stratified Random Sampling, Ratio estimators, Regression estimators,

Introduction to cluster sampling and systematic sampling.

Recommended reading: 1. Cochran, W.G., 3rd Edition, (1977), ‘Sampling Techniques’ , John Wiley & Sons

2. Barnet, V. , 3rd Edition, (1974),‘Elements of Sampling Theory’ , London: The English

University Press Ltd

Method of teaching and learning: A combination of lectures, tutorial and practical sessions

Assessments: End of course examination and assignments

Level 3


Title: Operational Research (OR) II



Learning Outcomes: At the end of this course unit the students should be able to apply OR

techniques in Queuing Theory, Network models, Integer programming models and Game

theory to real life problems.

Course Content:

The Transportation Models and their solution techniques , The Assignment Models and

their solution techniques, Queuing Theory: Queuing Systems, Queue Characteristics,

Poisson Process, and M/M/1 System, Other Systems (M/M/s, M/M/1/k, and M/M/s/k).

Network Models: Shortest path Problem, The Minimum Spanning Tree Problem, The

Maximum Flow Problem, The Minimum Cost Flow Problem, Project Planning and Control

with PERT-CPM, Project Management, Integer programming: Introduction to Integer

programming, Formulation Possibilities with Binary Variables, Perspectives on Solving

Integer programming Problems, The Branch and Bound Techniques and its Application to

Binary Integer Programming, Game Theory: Formulation of Two-Person, Zero-Sum

Games, Solving Simple Games, Game with Mixed Strategies, Graphical Solution Procedure,

Solving by Linear Programming .

Recommended readings: 1. Fredrick S. Hiller, Gerald J. Lieberman, 8th Edition , (2005), ‘Introduction to Operations


2. Hamdy A.. Taha, 8th Edition, (2007), ‘Operations Research’, Tara Mc-Graw Hill.

3. Bazaraa M.S., Javis J.J., Sherali H. D., 2nd Edition, (1990), ‘Linear programming and

network flows’.

4. Laurence A. Wolsey, Edition, (1998), ‘Integer Programming’.



Title: O.R. Laboratory



Learning Outcomes: On successful completion of this course module the students will be

able to apply techniques provided in O.R. Packages to solve problems in O.R.

Course Content: Introduction of one of the O.R. Packages TORA, Solver, LINGO or

LINDO, features of the package, Solving problems based on the course units STAT 12042

and STAT 31094 using the statistical package.

Recommended readings: 1. Fredrick S. Hiller, Gerald J. Lieberman, 8th Edition,(2005), ‘Introduction to Operations


2. Hamdy A.. Taha, 8th Edition, (2007), ‘Operations Research', Tara Mc-Graw Hill.




Title: Statistical Quality Control


Co-requisite: None

Learning Outcomes: At the end of this unit the student will be able to demonstrate the

knowledge and understanding of the principles of statistical quality control to solve problems

that arise in the industry.

Course Content: Classification of Inspection plans, OC-curve, process curve, methods of

choosing sampling plans, acceptance sampling schemes, inspection by variables, control

charts, cumulative sum charts, decision rules, continuous sampling plans, process trouble

shooting.


Douglas C. Montgomery, 5th Edition, (2004), Introduction to Statistical Quality Control’,

John Wiley and Sons




Title: Linear Models


Co-requisite: None

Learning Outcomes: At the end of this unit the students will be able to effectively apply

regression techniques to build statistical models derived from real life problems.

Course Content: Regressions: Simple linear regression model, Parameter estimation,

Gauss-Markov Theorem, Inferences about the model, Prediction, Analysis of variance

approach, Model adequacy, Lack of fit. Multiple linear regression model, Parameter

estimation, Inferences about the model, Prediction, Model adequacy, Variable selection and

model building procedures, Multicolinearity

Design an Analysis of experiments: Principles of design, Analysis of variance for one-way

classification, Analysis of variance for two-way classification, Missing values, Multiple

comparisons.


1. Draper, N.R and smith, 3rd Edition, (1998), ‘Applied Regression Analysis’, John Wiley

& Sons.

2. Montgomery, D.C., 5th Edition, (2001), ‘Design and Analysis of Experiments’, John

Wiley & Sons.

3. Hicks, C.R., 4th Edition, (1993), ‘Fundamental Concepts in Design of Experiments’,

Oxford University Press.

Method of teaching and learning:

A combination of lectures, tutorial and practical sessions

Assessments:

End of course examination and assignments


Title: Statistical Laboratory II


Co-requisite: None

Learning Outcomes: On successful completion of this course module students will

be able to effectively analyse data using a standard statistical package.

Course Content: Solving problems based on inferential statistics, sampling

techniques and linear modules using a standard statistical package

Recommended reading: Manual relevant to the statistical package




Title: Statistics for Natural Sciences

Pre-requisite: None

Co-requisite: None

Learning Outcomes: On successful completion of this course module students will

be able to design random experiments using the appropriate statistical method.

Course Content: Introductory concepts: Descriptive statistics, An introduction to

probability, Some important probability distributions, Sampling techniques. Parametric

Inference: Inferences associated with a single population, Inferences associated with two

populations. Goodness-of-fit tests, Correlation and Regression analysis, An introduction to

analysis of variance procedures. Nonparametric statistical methods.


1. Anderson, D.R., Sweeney, D.J. and William, T.A, 2nd Edition, (1991), ‘Introduction to

Statistics Concepts and Applications’, West Publishing Company.

2. McClave, J.T., Dietrich, F.H. and Sincich, T., 7th Edition, (1997), ‘Statistics’, Prince Hall

Method of teaching and learning: A combination of lectures and tutorials

Assessments: End of course examination and assignments.


Title: Fundamentals of Applied Statistics

Pre-requisite: None

Co-requisite: None

Learning Outcomes: At the end of this unit students should have the knowledge to apply the

basic statistical concepts to real life applications in the field of Humanities and Social

Sciences. They will be able to design and perform any random experiments and use statistical

methods to analyze the data obtained from the experiment.

Course Content: Introduction: The definition of statistical analysis, Basic mathematical

concepts. Descriptive statistics: Frequency distributions and graphing techniques, Percentiles,

Measures of central tendency, Measures of dispersion, The standard deviation. Distributions:

Some important discrete distributions, Standard Normal distribution, An introduction to

contingency tables and Chi-Square tests, Correlation and Regression, An introduction to

Multivariate Data Analysis, Inferential Statistics: Introduction to statistical inference,

Parametric tests of significance, Design and Analysis of simple comparative experiments,

Non-Parametric tests of significance.


1. Elifson. Kirk, Runyon. Richard, and Haber. Audrey, 3rd Edition, (1998), ‘Fundamentals

of Social Statistics’, McGraw-Hill.

2. Miller. Janet, 2nd Edition, (1983), ‘Statistics for Advanced Level’, Cambridge Press.


Assessments: End of course examination and assignments.

Special Degree Course Units


Title: Time Series Analysis

Pre-requisites: Core courses covered in the first two years

Co-requisite: None

Learning Outcomes:

At the end of this unit students should be able to analyse time series data, fit suitable

models to the data, and use forecasting techniques to predict future values of the model

under consideration.

Course Content: An introduction to Time Series, Stationary time series, Components of

a time series, Simple forecasting techniques: Moving averages, Exponential smoothing,

Halt-Winters procedure, Auto-Covariance function, Auto-Correlation function, Models

of time series: Auto Regressive models, Moving Average models, Auto Regressive

Moving Average models, and Auto Regressive Integrated Moving Average models,

Tentative identification of a model for a given set of observations, Estimation of model

parameters, Model checking and forecasting, Case Studies.


1. Brockwell and Davis, 2nd Edition, (1991), ‘Time Series- Method and Forecasting’,

Springer.

2. Box and Jenkins, (1976), ‘Time Series Analysis’, John Willy.

3. DeLurgio, S.A., (1998), ‘Forecasting Principles and Applications’, McGraw Hill.

4. Chatfield, C., 2nd Edition, (1980), ‘Analysis of Time Series’, Chapman-Hall.

Method of teaching and learning: A combination of lectures tutorials and group projects.

Assessments: End of course examination and assignments


Title: Categorical Data Analysis

Pre-Requisites: Core courses covered in the first two years

Co-requisite: None

Learning Outcomes:

At the end of this unit students should be able to analyse categorical data, fit suitable

models and interpret these models.

Course Content:

Introduction to categorical variables, Contingency tables, Methods for two binomial variates,

Logit models for categorical data, Methods for log-linear models for multiway contingency

tables, Fitting logit and loglinear models, Selection of a model, Testing goodness of fit of a

model, Association and independence in multidimensional tables.


1. S. E. Fienberg, 2nd Edition,( 1980), ‘The analysis of cross- classified categorical data’,

New York Springer

2. A. Aqresti, 2nd Edition, (2002), ‘Categorical data analysis’, John Wiley& sons.

3. D. Collet, 2nd Edition, (2003) ‘Modelling Binary data’, Chapman & Hall




Title: Optimization

Pre –Requisites:

Co-requisite: None

Learning Outcomes:

At the end of this course unit students should demonstrate the knowledge and understanding

of the mathematical concepts of linear optimization and some classical polynomially solved

network optimization problems.

Course Content: Introduction and Applications, The simplex Method: Linear Programs in Standard Form,

Basic Solution: Optimality Test and Basis Exchange, The Fundamental Theorem of Linear

Programming, Degeneracy and Fitness of the simplex Method, Finding a feasible Starting

Solution, The revised Simplex Method. Duality and Further Variations of the Simplex

Method: Dual Programs and the Duality Theorem, Complementary Slackness Conditions,

The Dual Simplex Method, The Primal –Dual Simplex Method ,Linear Programs with

Bounded Variables Interior Point Method: Karmarkar’s Projective Algorithm: One

Iteration of Karmarkar’s Projective Algorithm, Projective Transformation, Move in the

Direction of Steepest Descent, Inverse Transformation ,Karmarkar’s Projective Algorithm, A

purification Scheme, Converting a Given LP into the Required Format. Introduction to

Graph Theory and Shortest Path Problems:Introduction to Graph Theory ,Shortest

Dipaths with Nonnegative Costs, Shortest Dipaths with Negative Costs, Pairwise Shortest

Dipaths, Shortest Path Problems,Shortest Dipath Problems and Linear Programs. Network

Flow Problems: Definition and Basic Properties of Network Flows, Maximal Flows,

Feasible Flows for NFP and the Negative Dicycle Algorithm ,The Network Simplex

Algorithm .Matchings: Definition and basic Properties, Bipartite Matching Problems,

Maximal Matching Problem in Bipartite Graphs, Minimal cost Matching Problem in

Bipartite Graphs, Matching Problem in Arbitrary Graphs, MMP in Arbitrary Graphs ,MCMP

in Arbitrary Graphs.


1. Bazaraa, M.S., Jarvis,J.J., Sherali, H.D., 3rd edition, (2004), ‘Linear Programming and

Network Flows’, Wiley, New York.

2. Papaddimitriou, C.H., Steiglitz, K., Unabridged edition, (1998), ‘Combinatorial

Optimization:Algorithem and Complexity’, Dover Publications.


Assessment: End of semester examination and assignments


Title: Actuarial Mathematics

Pre-Requisites: STAT 22073

Co-requisite: None

Learning outcomes:

At the end of this unit students should be able to demonstrate the knowledge and

understanding of the underlying principles of actuarial statistics in life insurance industry.

Course Content:

Life Insurance: Survival distribution and the life tables, Discrete & continuous Life Insurance

Models, Life Annuities: Introduction to Annuity Models, continuous whole life & the

Discrete Life Annuity Due Model, Net Premiums: Fully discrete whole life, the aggregate

determination & loss function approach, Net Premium Reserves, Multiple Life Functions,

Multiple Decrement Models, Valuation theory for pension plans, Insurance models including

expenses, Non-Forfeiture benefits and dividends.


1. S. D. Promislow, 1st Edition, (2006), ‘Fundamentals of Actuarial Mathematics’, JohWiley and Sons.

2. Hans U. Gerber, 3rd Edition, (1997), ‘Life Insurance Mathematics’, Springer.




Title: Bayesian Inference & Decision theory

Pre-Requisites: STAT 22073

Co-requisite: None

Learning Outcomes:

At the end of this course unit students should be able to solve real world problems related to

Bayesian Inference and Decision Theory.

Course Content: Bayesian Inference for Discrete Random Variables: Two equivalent ways of using

Bayes’ theorem, Bayes’ theorem for binomial with discrete prior. Bayesian inference

for Binomial Proportion: Using a uniform prior, Using a beta prior, Choosing your

prior, Summarizing the posterior distribution, Estimation the proportion, Bayesian

credible interval. Comparing Bayesian and Frequentist Inferences for Proportion:

Frequent interpretation of probability and parameters, Point estimation, Comparing

estimators for proportion, Interval estimation, Hypothesis testing, Testing a one-Sided

hypothesis, Testing a two-sided hypothesis. Bayesian inference for Normal mean:

Bayes’ theorem for normal mean with a discrete prior, Bayes’ theorem for normal

mean with a continuous prior, Choosing your normal prior, Bayesian credible interval

for normal mean, Predictive density for next observation. Comparing Bayesian and

Frequentist Inferences for Mean: Comparing frequentist and bayesian point

estimators, Comparing confidence and credible intervals for mean, Testing a one-sided

hypothesis about a normal mean, Testing a two-Sided hypothesis about a normal mean.

Bayesian Inference for Difference between Means: Independent random samples

from two normal distributions, Case 1: Equal variances, Case 2: Unequal variances,

Bayesian inference for differences between two proportions using Normal

approximation, Normal random samples from paired experiments. Bayesian Inference

for Simple Linear Regression: Least square regression, Exponential growth model,

Simple linear regression assumptions, Bayes’ theorem for the regression model,

Predictive distribution for future observation


1. William M.B., 2nd Edition, (2004), ‘Introduction to Bayesian Statistics’, John Wiley &

Sons.




Title: Stochastic Processes

Pre-requisite: STAT 21042.

Co-requisite: None

Learning Outcomes:

On successful completion of this course module students should be able to: (i) demonstrate

the importance of stochastic models in the study of Physical, Biological, Social and

Economic systems encounterd in day-to-day life, (ii) construct appropriate stochastic models

for such real-life situations giving the better representation, description and specification and,

apply concepts and procedures in Stochastic Processes to those models.

Course Content: Introduction: Rationale for learning Stochastic Processes, Role of

stochastic models in the study of Physical, Biological, Social and Economic systems

encounter in day-to-day life. Stochastic Processes in General: States and the State Space,

Parameter Space and a realization of a Stochastic Process, Classification of Stochastic

Processes, Probability Distribution of a Stochastic Process, Transition Probability

Distributions, Markov Dependence of a Stochastic Process, Markov Processes and Chapman

Kolmogorov Equation. Markov Chain: Two-state Markov chains, Two-state Markov

process as a limiting case of a Two-state Markov chain, Classification of States of a Markov

Chain, Limit theorem on Markov Chain, Periodicity, Limits of the Higher Probabilities.

Finite Markov Chains: One-step and n-step Transition Probability Distributions, Irreducible

Aperiodic finite Markov chains, Finite Markov Chains with Transient and Recurrent States.

Infinite Markov Chains: Irreducible Aperiodic Infinite Markov Chains, Queuing Processes,

Non-irreducible Infinite Markov Chains, Branching Processes. Markov Processes with

Discrete State Space: Poisson Processes and its properties, Pure Birth Processes, Pure Death

Processes and, Birth and Death Processes.


1. Narayan Bhat, U., (1972), ‘Elements of Applied Stochastic Processes’, John Wiley &

Sons Inc.

2. Bailey, N. T. J., 1st Edition 3rd Reprint (1967), ‘The Elements of Stochastic Processes’,

John Wiley & Sons Inc.

3. Medhi, J. 1st Edition Reprint (1991), ‘Stochastic Processes’, Wiley Eastern Ltd.

4. Hoel, P. G., Port, S. C., Stone, C. J., (1994), ‘Introduction to Stochastic Processes’,

Houghton Miffin Company.

5. Feller, W., 2nd Edition Reprint (1966), Volume I, ‘An Introduction to Probability Theory

and Its Applications’, John Wiley & Sons Inc.

Method of teaching and learning: A combination of lectures, group work and tutorials.



Title: Multivariate Data Analysis

Pre-requisite: Core courses covered in first three years

Co-requisite: None

Learning outcomes:

At the end of this unit students should be able to (i) recognize multivariate data and identify

contexts where multivariate data analysis techniques should be used (ii) test hypotheses about

the population means based on multivariate data (iii) compare two or more population means

based on multivariate data (iv) analyse the correlation structure of multivariate data using the

appropriate technique.

Course Content:

Introduction to multivariate data, Multivariate normal distribution, Wishart distribution and

Hotelling's T-squared distribution, Testing hypotheses on single population means based on

Hotelling's T-squared distribution, Comparison of two multivariate population means,

Multivariate Analysis of Variance (MANOVA), Discriminant data analysis, Principal

component analysis, Factor analysis, Cluster analysis


1. Richard A. Johnson and Dean W. Wichern, 4th edition, (1998), ‘Applied Multivariate

Statistical Analysis’, Prentice Hall

2. Morrison, D. F., 4th Edition, (2004), ‘Multivariate Statistical Methods’, Duxbury Press

Method of teaching and learning: A combination of lectures and tutorial.



Title: Design and Analysis of Experiments


Co-requisite: None

Learning Outcomes:

At the end of this unit students should be able to demonstrate advanced designing skills so as

to ensure the best possible designs in statistical experiments.

Course Content:

Experiments with a Single Factor: The Analysis of Variance, Analysis of the Fixed Effects

Model, Model Adequacy Checking, Practical Interpretation of Results, Determining Sample

Size, The Regression Approach to the Analysis of Variance, Randomized Blocks, Latin

Square and Related Designs: The Randomized Complete Block Design, The Latin Square

Design, Balanced Incomplete Block Design, Introduction to Factorial Designs: Basic

Definitions and Principles, The Advantage of Factorials, The Two-Factor Factorial Design,

The General Factorial Design, Blocking in a Factorial Design. The 2k Factorial designs: the

22 Design, the 23 Design and The General 2k Design.

Recommended reading: 1. Hicks C. R., 4th Edition, (1993), ‘Fundamental Concepts in Design of Experiments’,

Oxford University Press.

2. Hicks C. R., Turner K. V., 5th Edition, (1999),’Fundamental Concepts in Design of

Experiments’, Oxford University Press.

3. Douglas C. Montgomery, 5th Edition, (2001), ‘Design and Analysis of Experiments’,

John Wiley & Sons, Inc.

Method of teaching and learning: A combination of lectures, tutorials and assignments.



Title: Econometrics


Co-requisite: None

Learning Outcomes:

On successful completion of this course module the students will be able to identify the

appropriate techniques in modelling economic data and produce their own empirical

econometric analysis.

Course Content: Relaxing the assumptions of the classical model: Multicollinearity and

micronumerosity, Heterosecdasity, Auto-correlation, Econometric modelling, Topics in

Econometrics: Regression models on dummy independent variables, Regression models on

dummy dependent variables, Dynamic econometric model, Simultaneous-Equation models.

Time series Econometrics: Stationarity, Unit roots, Cointegration, Forecasting.


1. Domodar N. Gujarat., 4th Edition, (2002), ’Basic Econometrics’, McGraw-Hill.

2. William H. Greene., 5th edition, (2002), ‘Econometric Analysis’, Prentice Hall.

3. Maddala. G.S., 3rd Edition, (2001), ‘Introduction to Econometrics’, Wiley


Assessment: End of course examination and assignments


Title: Special Topics in Statistics

Pre-requisite: Core courses covered in the first three years

Co-requisite: None

Learning Outcomes:

At the end of this course students should be knowledgeable about modern trends in

Statistics.

Course Content:

This course is designed to include specialized modern topics in Statistics.

Recommended reading: Required reading material will be recommended by the lecturer depending on the

relevant topic.

Method of teaching and learning: A combination of lectures, tutorials and group projects.

Assessment: End of semester examination, assignments and group projects.


Title: Research Project/ Independent Study

Pre-requisite: Core courses covered in the first three years

Co-requisite: None

Learning Outcomes:

At the end of this course students should be able to conduct a research project/ an

independent study in an area in Statistics.

Recommended reading: Required reading material will be recommended by the supervisor depending on the

relevant project/study.

Method of teaching and learning: A project/ study project under the supervision of a senior

staff member of the department.

Assessment: A dissertation should be submitted and the results should be presented at a

seminar. The work will be assessed on the dissertation and the seminar.


TITLE : Advanced Database Systems with Applications



LEARNING OUTCOMES :

At the end of this course module a student will be able to demonstrate the theoretical

knowledge on advanced database management system design principles and techniques, and

practical skills on applications of them.

COURSE CONTENT:


ISAM, B-tree, more trees, hashing, textual, distributed/P2P indexing; Query processing:

scan, sort, hash, index-based, systems view; Buffer management; Query optimization:

Rewrite, cost estimation, algorithms; Online query processing and optimization, downsizing

the DBMS; Transaction Processing Concepts; Concurrency Control Techniques; Practical

Database Design and Tuning; Database Recovery Techniques; Object-Oriented Databases;

Deductive databases; Emerging Database Technologies and Applications; Distributed

Databases and Client-Server Architecture; XML, DTD, XPath, XQuery, XSLT, XML

storage, XML indexing, streaming XML; Database Security and Authorization; Enhanced

Data Models for Advanced Applications; Recent advances in database systems such as

Spatial and Active Databases; Introduction to Data warehousing and data mining.




RECOMMENDED READING

1. Ullman, J. D. and Widom, J., Database Systems: The Complete Book, (2002), Hector


2. Stonebraker M., and Hellerstein, J. M., Readings in Database Systems, (1998, 3rd

Edition), Morgan Kaufman.

3. Elmasri, R. and Navathe, S. B., Fundamentals of Database Systems (2006, 5th

Edition), Addison-Wesley Longman Publishing Co., Inc.

4. McFadden, F. R., Hoffer, J. A. and Prescott, M. B., Modern Database Management

(2006, 8th Edition), Pearson Education Series.

5. Ramakrishnan, G., Database Management Systems, (2002, 3rd edition), McGraw-Hill.

6. Hansen, G. W. and Hansen, J. V., Database Management and Design, (2002, 2nd

Edition), Prentice Hall.

7. Brundage, M., XQuery: The XML Query Language, (2004), Addison Wesley

Longman Publishing Co., Inc.


TITLE : Machine Learning

PRE-REQUISITES : All COSC core course units followed in the General Degree.


LEARNING OUTCOME:

At the end of this course module a student will be able to demonstrate the ability to solve

problems using learning algorithms, neural networks and genetic algorithms.

COURSE CONTENT:

Learning: Learning Agent, Concept of learning, Nature inspired and other types of learning,

Decision tree learning;

Artificial Neural Networks: Basic Models, Hopfield Model, Design and implementation,

Perceptrons, Multilayer Networks and Backpropagation, Counterpropagation and recurrent

networks;

Statistical Training: Evaluating hypotheses, Bayesian learning, Bayesian belief networks,

Optimal Classifiers, Computational learning theory, Vapnic-Chervonenkis Dimension,

Mistake bound analysis, Sample complexity analysis, Instance-based learning;

Genetic Algorithms: Schemata, Design and implementation, Genetic operations, Hypothis

Space search, Genetic Programming, Models of Evolution and Learning;

Inductive and Analytical Learning; Reinforcement Learning; Classifier Systems;

Selected applications in data mining, automated knowledge acquisition, pattern recognition,

program synthesis, text and language processing, internet-based information systems, human-

computer interaction, semantic web, bioinformatics and computational biology.

METHOD OF TEACHING AND LEARNING: A combination of lectures and

assignments.



1. Mitchell, T. M., Machine Learning, (1997), McGraw-Hill International Editions.

2. Bishop, C., Pattern Recognition and Machine Learning, (2006), Springer-Verlag.

3. Hertz, J., Krogh, A. and Palmer, R. G., Introduction to The Theory of Neural

Computation, (1991), Addison-Wesley Publishing Company.

4. Goldberg, D. E., Genetic Algorithms, (1989), Addison-Wesley Publishing Company.

5. Rao, M. A. and Srinivas, J., Neural Networks, (2004), Narosha Publishing House Pvt.

Ltd.

6. Mehothra, K., Mohan, C.K. and Ranka, S, Artificial Neural Networks,(1997), Oxford

University Press.

7. Looney, C.G., Pattern Recognition Using Neural Networks, (2000), MIT Press.

Documents

COURSE CODE : COSC 11014 TITLE : PRE …science.kln.ac.lk/web/syllabus/stat2015.pdf1 COURSE CODE : COSC 11014 TITLE : Theoretical Foundations of Computer Science PRE-REQUISITES : G.C.E