Department of Statistics & Computer Science

Level 01

COSC 11012 - Introduction to Computing

Course Code	: COSC 11012
Title	: Introduction to Computing
Pre-Requisites	: G.C.E. A/L
Co-Requisites	: COSC 11023

Learning Outcomes:

At the completion of this course student will be able to:

explain how data are represented, stored, and manipulated by computer hardware
use abstraction and decomposition when reasoning about complex systems and problems
describe how data can be transmitted over networks and the security concerns that arise
apply computing tools and techniques to solve problems at multiple levels of abstraction
discuss the impact of computing within economic, social, and cultural contexts
recognize problem solving techniques and algorithm development using computers.

Course Content:

Main components of a Computer; Organization of a Computer; Classification of Computers; Software: Systems Software and Application Software; Operating Systems, functions and types of operating systems; Utility Programs, Translators (compilers, interpreters, assemblers); Application Programs: Algorithms, Computer programs, Computer programming Languages; Generations of programming languages; Number Systems; Conversions between number systems; Use of number systems; Binary addition and subtraction; Representation of Numbers; Representation of Characters: ASCII, EBCDIC, Unicode; Representation of Images and Video; Introduction to logic gates; Introduction to Computer Networks; Network topologies; Advantages and disadvantages of computer networks, Introduction to the Internet; Services available on the Internet; 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:

Lectures, Tutorials and Assignments

Assessment:

End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Norton, P. (2006). Introduction to Computers. 6^th Tata McGraw-Hill Publishing Company limited, India.
Parsons, J. J., Oja, D. (2003). New Perspective on Computer Concepts. 6^th Course Technology a division of Thomson learning Inc.
Ram, B. (2005). Computer Fundamentals: Architecture and organization. 3^rd New Age Publications, India.
Forouzan, B. A., Firouz, M. (2008). Foundations of Computer Science. 2^nd Cengage Learning EMEA.
Gaddis, T. (2017). Starting out with Python. 4^th Edition. Pearson.

COSC 11023 - Fundamentals of Programming

Course Code	: COSC 11023
Title	: Fundamentals of Programming
Pre-Requisites	: G.C.E. A/L
Co-Requisites	: COSC 11012

Learning Outcomes:
At the completion of this course student will be able to:

define the basic concepts of the structured programming
identify suitable data types and data structures for the real-world problems
explain main control structures of procedural programming languages
develop structured programs using a procedural language
describe functional hierarchical code organization
demonstrate knowledge on textual information, characters and strings
use language specific features on program development and error handling.

Course Content:

Introduction to Programming: A brief history and types of programming languages; Program Design: Modular programming concepts, Elegance in program design Implementing an algorithm using a programming language Program testing and program; The High Level programming language: First program, compilation, syntax errors, Data types and variable scopes, Constants, Identifiers, Expressions and assignment, Input and output, Arrays, Program selections (if, if-else, switch), Repetition (for, do-while), Control structures; Introduction to Functions; Storage classes; Scope of a variable; Pointers; Structured data types (arrays, structures, unions), Programmer defined data types; Recursion; Inheritance; Virtual Functions, and Dynamic Binding; File processing; Multi-file programming; Bit manipulation and enumerations; Static and Dynamic memory handling; error handling (debugging).

Method of Teaching and Learning:

Lectures, Tutorials, Assignments and Practical

Assessment:

End-of-course written examination and practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Gottfried, B.S. (2001). Schaum’s Outline of Theory and Problems of Programming in C. 2^nd McGraw Hill Professional Publishing.
Gaddis, T. (2017). Starting out with Python. 4^th Pearson.
Kelly, A. and Pohl, I. (1999). A Book on C: Programming in C. 4^th Addison Wesley Longman Inc.
Rajaraman, V. (2004). Computer Programming in C. 6^th Prentice Hall.
Zelle, J. M. (2016). Python programming: an introduction to computer science. 3^rd Franklin, Beedle & Associates, Inc.
Guttag, J. (2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. 2^nd Edition. MIT Press.

COSC 12033 - Data Communication and Networks

Course Code	: COSC 12033
Title	: Data Communication and Networks
Pre-Requisites	: COSC 11012
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

examine the use of computer networks to identify the forces behind their development
explain the principles of a layered protocol architecture
identify and describe the system functions in the protocol layers
discuss different coding schemes
design 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 and packet 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, 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.

Method of Teaching and Learning:

Lectures, Tutorials and Assignments

Assessment:

End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Stallings, W. (2013). Data and computer communications. 10^th Pearson.
Forouzan B. A. (2012). Data Communications and Networking. 5^th McGraw Hill.
Tanenbaum A.S., Wetheral David J. (2010). Computer Networks. 5^th Prentice-Hall International.
Kurose, J. F., Ross, K. W. (2016). Computer networking: a top-down approach. 7^th Pearson.
Stallings, W., Case, T. (2012). Business Data Communications- Infrastructure, Networking and Security. 7^th Edition. Pearson.

COSC 12043 - Object Oriented Programming

Course Code	: COSC 12043
Title	: Object Oriented Programming
Pre-Requisites	: COSC 11023
Co-Requisites	: None

Learning Outcomes:

At the completion of this course student will be able to:

apply design and development principles in the construction of software systems of varying complexity
develop the structures to represent objects and the algorithms to perform operations
explain and utilize object-oriented concepts
use an industry-leading Integrated Development Environment (IDE) to develop and manage software projects.

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 (OOPL) (e.g.: Java/C++); Basic OOPL features: Class and object models, object declaration and creation, instantiable classes, visibility modifiers, arrays of objects, self-referencing pointers, reuse 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; Advanced OOPL features: Overloading, data abstraction, encapsulation, inheritance, polymorphism, file processing, templates, exceptions and container classes.

Method of Teaching and Learning:

Lectures, Tutorials, Assignments and Practical

Assessment:

End-of-course written examination and practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Wu, T. (2009). An Introduction to Object-Oriented Programming with Java. 5^th McGraw Hill.
Savitch, W. (2017). Problem Solving with C++. 10^th Pearson.
Liang, Y. D. (2017). Introduction to Java Programming and Data Structures, Comprehensive Version. 11^th Pearson.
Dale, N. B., Weems, C. (2004). Programming in C++. 3^rd Jones & Bartlett Learning.
Gamma, E. (1995). Design patterns: elements of reusable object-oriented software. Pearson Education, India.

Level 02

COSC 21052 - Software Engineering

Course Code	: COSC 21052
Title	: Software Engineering
Pre-Requisites	: COSC 11012, COSC 12043
Co-Requisites	: None

Learning Outcomes:

At the completion of this course student will be able to:

list software development process models
recognize the need of formal approach for software development
identify software requirements of an organization
analyze, design and review software systems for organizations
discuss ethical issues posed by technological advances.

Course Content:

Software and Software Engineering: Software processes, requirement engineering, system modeling, software prototyping, formal specification, architectural design, object-oriented design, user interface design, software testing, project management, managing people, software cost estimation, quality management, configuration management; Introduction to Ethics: Variety of ethical views, justice and social contract theory; professional conduct followed in the IT industry.

Method of Teaching and Learning:

Lectures, Tutorials and Assignments

Assessment:

End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Sommerville, I. (2015). Software engineering. 10^th Pearson.
Pressman, R. S. (2014). Software engineering: a practitioner's approach. 8^th McGraw-Hill Education.
Wiegers, K., Beatty, J. (2013). Software requirements. 3^rd Microsoft Press.
Cohn, M. (2010). Succeeding with agile: software development using Scrum. 1^st Addison-Wesley Professional.

COSC 21063 - Data Structures and Algorithms

Course Code	: COSC 21063
Title	: Data Structures and Algorithms
Pre-Requisites	: COSC 12043
Co-Requisites	: None

Learning Outcomes:

At the completion of this course student will be able to:

apply object‐oriented programming, as a modern tool to solve practical computing problems
identify basic data structures and algorithms
demonstrate the ability to analyze, design, apply and use data structures and algorithms to solve practical problems and evaluate their solutions
use complexity analysis to compare algorithm performances.

Course Content:

Data structure concepts; Arrays; Simple linked lists; Different implementations of lists; Stacks and queues; Sets; Binary-trees; Balanced trees; Heaps; Priority queues; Dictionaries/maps; Graphs; Introduction to complexity: Big or little O-notation; Algorithms: Recursion and backtracking, Sorting and searching, hashing.

Method of Teaching and Learning:

Lectures, Tutorials, Assignments and Practical

Assessment:

End-of-course written examination ,practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Lewis, J., DePasquale, P., Chase, J. (2016). Java Foundations: Introduction to Program Design and Data Structures. 4^th Pearson.
Shaffer, C. A. (2011). Data Structures and Algorithm Analysis in Java. 3^rd Dover Publications.
Weiss, M. A. (2013). Data structures & algorithm analysis in C++. 4^th Pearson Education.
Lee, K. D., Hubbard, S. (2015). Data Structures and Algorithms with Python. Springer Publications.
Weiss, M. A. (2011). Data structures and algorithm analysis in Java. 3^rd Pearson.
Carrano, F. M., Henry T. M. (2015). Data Structures and Abstractions with Java. 4^th edition. Pearson

COSC 22073 - Computer Architecture and Operating Systems

Course Code	: COSC 22073
Title	: Computer Architecture and Operating Systems
Pre-Requisites	: COSC 11012, COSC 11023
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain how data and programs are represented in computers
compare and contrast different computer architectures
describe the functionality and working of the building blocks of computer systems
demonstrate an understanding of assemblers and programming in assembly language
explain the key roles played by an operating system
identify the major components of operating systems
describe the concepts, models and approaches involved in design of operating systems.

Course Content:

Computer Architecture: Data and program representation, different computer architectures, processor architectures, instruction set architecture, fetch-execute cycle, energy and cost, instruction pipelining, branch prediction, operand addressing, microcode, parallelism, hardware and software integration, assemblers, programming in assembly language, memory and storage, bus architectures, I/O architectures.

Operating Systems: roles of an operating system, different operating systems, multi-programming, time sharing, concurrency control, deadlocks and starvation, memory management, process management (processes models, processor scheduling), synchronization.

Method of Teaching and Learning:

Lectures, Tutorials and Assignments

Assessment:

End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Comer, D. E. (2004). Essentials of Computer Architecture. Pearson.
Stallings, W. (2011). Computer Organization and Architecture: Designing for Performance. 10^th Pearson.
Hennessy, J. L., Patterson, D. A. (2011). Computer architecture: a quantitative approach. 5^th Morgan Kaufmann.
Stallings, W. (2014). Operating Systems: Internals and Design Principles. 8^th Pearson.
Silberschatz, A., Gagne, G., Galvin, P. B. (2018). Operating System Concepts. 10^th Edition. Wiley.

COSC 22083 - Database Management Systems

Course Code	: COSC 22083
Title	: Database Management Systems
Pre-Requisites	: COSC 11023
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe various logical database architectures
design & develop databases using relational model and manipulate data
use databases in software solutions
apply theories behind various database models and query languages in practical scenarios
discuss security and integrity policies relating to databases.

Course Content:
Introduction to database systems: Database system concepts and architecture, Three tire architecture and mapping; Data Modelling: 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, Security and integrity in databases; Physical database design: Storing data and primary file organization, File organization and indexes (primary, secondary, clustering and multilevel indexes etc.).

Method of Teaching and Learning:

Lectures, Tutorials, Assignments and Practical

Assessment:

End-of-course written examination, Practical Examination and other assessments announced at the beginning of the course unit.

Recommended Reading:

Ricardo, C. M., Urban, S. D. (2015). Databases Illuminated. 3^rd Jones & Bartlett Publishers.
Elmasri, R., Navathe S.B. (2017). Fundamentals of database systems. 7^th Pearson Education India.
Hoffer, J. A., Venkataraman, R., H., (2019), Modern Database Management, 13^th Edition Pearson.
Ramakrishnan, R., Gehrke, J. (2002). Database management systems. 3^rd McGraw Hill.
Molinaro, A. (2005). SQL Cookbook: Query Solutions and Techniques for Database Developers. O'Reilly Media, Inc.

Level 03

PRPL 31992 - Professional Placement

Course Code	: PRPL 31992
Title	: Professional Placement
Pre-Requisites	: All Level 1 and 2 course modules
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

develop significant commitment in the students’ profession/specialization
integrate classroom theory with workplace practice
develop greater clarity about academic and career goals
develop new or advanced skills.

Course Content:
The students will be placed in selected industries and institutions where they carryout computer science related work/research for a period of six weeks. The required resource materials will be supplied by the relevant institution/industry.

Method of Teaching and Learning:

Training under the supervision and guidance of a suitable trainer in the computing industry.

Assessment:

Evaluation of the progress report submitted by the trainer, and the student’s technical report.

Recommended Reading:

Reading and reference material recommended by the industry supervisor and the course coordinator.

COSC 31093 - Enterprise Software Design and Architecture

Course Code	: COSC 31093
Title	: Enterprise Software Design and Architecture
Pre-Requisites	: COSC 21063, COSC 22083
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain the concept of Enterprise Software Architecture
apply different architectural styles to solve different business models
analyze and design appropriate enterprise applications
identify how design patterns will resolve subtle coding issues.

Course Content:
Introduction to Enterprise Applications: web, standalone and cloud applications; Software System Architecture; Architecture Views and Decomposition; Architecture Styles; Middleware, strategies for data Processing; Service-Oriented-Architecture; Resource-Oriented Architecture; Domain-Driven Architecture; Event-Driven Architecture; Design Patterns; Micro services; RESTful Architecture; Best practices in application development; Web Services: Web Services Frameworks and their Performance, Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL), Web Services Security.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Taylor, R. N., Medvidovic N., Dashofy, E. (2009). Software Architecture: Foundations, Theory, and Practice. 1^st Edition. Wiley.
Gorton, I. (2006). Essential software architecture. Springer Science & Business Media.
Brooks Jr, F. P. (2010). The design of design: Essays from a computer scientist. Pearson Education.
Gamma, E., Helm, R., Johnson, R., Vlissides, J. (1994). Design Patterns: Elements of Reusable Object-Oriented Software. 1^st Edition. Addison-Wesley.
Freeman, E., Robson, E., Bates, B., Sierra, K. (2004). Head First Design Patterns. O'Reilly Media Inc.

COSC 31103 - Web & Internet Technologies

Course Code	: COSC 31103
Title	: Web & Internet Technologies
Pre-Requisites	: COSC 12033, COSC 12043, COSC 22083
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe basic concepts of web and the internet technologies
use client-side technologies for building, usable, accessible, standard compliant web pages
use server-side technologies for building secure database driven web applications
describe and critically discuss the design, engineering, legal, social, ethical and professional issues and considerations involved in web application development.

Course Content:
Overview of the Internet; Web technologies: Standard client side technologies including HTML/ XHTML, CSS, JavaScript and related libraries, DOM, cookies. Web servers and server-side technologies including Apache, PHP, session state and database connectivity using MySQL; Issues and considerations in web application development: standards, maintenance, efficiency, stability, usability, accessibility, law, security and privacy, emerging trends and best practices.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and Practical Examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Connolly, R., Hoar, R. (2015). Fundamentals of Web Development. 2^nd Pearson.
Deitel, P. J., Deitel, H. M., Deitel, A. (2011). Internet & World Wide Web How to Program. 5^th Pearson.
Welling, L., Thomson, L. (2016). PHP and MySQL Web Development. 5^th Addison-Wesley.
Jackson, J. C. (2006). Web Technologies: A Computer Science Perspective. Prentice Hall.
Scobet P., Lingras, P. (2016). Web Programming and Internet Technologies: An E-Commerce Approach. 2^nd Edition. Jones & Bartlett Learning.

COSC 31112 - Visual Programming

Course Code	: COSC 31112
Title	: Visual Programming
Pre-Requisites	: COSC 21052, COSC 22083
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

apply the concepts of visual programming and algorithm design
use appropriate controls and events to develop user-friendly graphical interfaces
use available classes and technologies to access database through graphical user interfaces
design and develop stand-alone and web-based software applications for real-world problems.

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, 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; Introduction to web application development.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Hoisington, C. (2017). Microsoft Visual Basic 2017 for Windows, Web, and Database Applications: Comprehensive. 1^st Course Technology.
Gaddis, T., Irvine, K. (2016). Starting out with Visual Basic. 7^th Pearson.
Deitel, P. J., Deitel, H. (2016). Visual C# How to Program. 6^th Pearson.
Sharp, J. (2018). Microsoft Visual C# Step by Step. 9^th Microsoft Press.
Gaddis, T (2016). Starting out with Visual C#. 4^th Edition. Pearson.

COSC 34122 - Cyber Security

Course Code	: COSC 34122
Title	: Cyber Security
Pre-Requisites	: COSC 12033
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

identify the elements that define cybersecurity
explain the legal, social, and political frameworks that affect cybersecurity
identify and define challenges to achieving cybersecurity
identify and explain social, legal, political, and economic impediments to cybersecurity
examine approaches to maintain a reasonable state of cybersecurity and to address breaches effectively, ethically, and according to law.

Course Content:
Introduction to cybersecurity: Ethics and Legal Issues, Responsibilities of Knowledge and Power, Ethical Disclosure, Surveillance vs Attack; Classes of Attack: Code injection, Time-of-check-to-time-of-use race conditions, Sybil attack; Systems Security: Authentication, Policy, Secure design principles (Saltzer and Schroeder), Information flow; Network Security: Web: SQL command injection, phishing, and cross-site scripting (XSS), No trusted external party, Network reconnaissance and information theory, Botnets; Data Hiding: Cryptography, Obfuscation and diversity methods, Differential Privacy (Dwork), Anonymity; Intrusion Detection and Response: Anomaly (network and host), Specification based (network and host), Viruses, Worms, Denial of Service. Human Factors: Captcha’s, Social engineering, Economics of Security, Incentives and motivations for attack.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Stallings, W. (2018). Effective Cybersecurity: A Guide to Using Best Practices and Standards. 1^st Addison-Wesley Professional.
Singer, P. W., Friedman, A. (2014). Cybersecurity: What everyone needs to know. 1^st OUP USA.
Moschovitis, C. (2018). Cybersecurity Program Development for Business: The Essential Planning Guide. 1^st John Wiley & Sons.
Pfleeger, C. P., Pfleeger, S. L., Margulies, J. (2015). 5^th Security in computing. Prentice Hall.
Schneier, B. (2015). Applied cryptography: protocols, algorithms, and source code in C. 20^thAnniversary Edition. John Wiley & Sons.

COSC 32133 - Full-Stack Software Development

Course Code	: COSC 32133
Title	: Full-Stack Software Development
Pre-Requisites	: COSC 31093, COSC 31103
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

apply the software development process
use front-end, back-end and middle-tier application frameworks, libraries, and tools
develop problem solving and logic building aptitude by gaining expertise in data structures and algorithms
demonstrate professional skills needed in application development
evaluate developed software using appropriate tools
discuss threats to privacy posed by modern technology.

Course Content:
Engage in software development process: use HTML (latest version), JavaScript libraries or framework, server can be built-in or separate, write services and work with Object Relational Mappers; Determine how to modularize components: models, controllers, data and interfaces; Single Page Application; Dependency Injection; Services; Containerization; Scalability; Testing tools; Version controlling; Introduction to the current practices and technologies. Privacy and Intellectual property: Perspective on privacy, public information intellectual property rights, trade secrets, patents and copyright; Networked Communications: email and spam, censorship, social media censorship, internet addition.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End semester project and other assessments announced at the beginning of the course unit.

Recommended Reading :

Northwood, Chris. (2018). The Full Stack Developer: Your Essential Guide to the Everyday Skills Expected of a Modern Full Stack Web Developer.
Zammetti, F. W. (2020). Modern Full-Stack Development: Using TypeScript, React, Node.js, Webpack, and Docker
Hinkula, Juha. (2018). Hands‑On Full Stack Development with Spring Boot 2.0 and React: Build.
Hoque , Shama (2018). Full‑Stack React Projects: Modern Web Development Using React 16, Node.
Baase, S., Henry, T. M. (2017). A Gift of Fire: Social, Legal, and Ethical Issues for Computing Technology. 5^th Edition. Pearson.

COSC 32142 - Artificial Intelligence

Course Code	: COSC 32142
Title	: Artificial Intelligence
Pre-Requisites	: COSC 11012, COSC 11023
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

understand various definitions of Artificial Intelligence
apply search techniques for problem solving
identify and use different knowledge representation techniques
analyze and select the most efficient and appropriate mechanism applicable to a given problem that can be solved using artificial intelligence.

Course Content:
The history of artificial intelligence and foundations of artificial intelligence; Intelligent agents: Agents and Environments, The Nature of Environments, Structure of agents, Classes of intelligent agents, Hierarchies of agents, Problem-Solving Agents; Intelligent search techniques: Uninformed search, Informed (Heuristic) search, Adversarial Search, Games as search problems; Learning and Knowledge Representation: Forms of Learning, Logic-based representation, Procedural representations, Structured representations; Languages and programming techniques for Artificial Intelligence; Overview of major areas and Future trends in Artificial Intelligence.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and Practical Examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Russell, S. J., Norvig, P. (2009). Artificial Intelligence: A Modern Approach. 3^rd Prentice Hall.
Luger, G. F. (2008). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. 6^th The Benjamin/Cummings Publishing Company, Inc.
Yao, M., Zhou, A., Jia, M. (2018). Applied Artificial Intelligence: A Handbook for Business Leaders. 1^st TOPBOTS.
Neapolitan, R., Jiang, X. (2012). Contemporary Artificial Intelligence. Chapman & Hall.
Stone, J. V. (2019). Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning. Sebtel Press.

COSC 32152 - Mobile Application Development

Course Code	: COSC 32152
Title	: Mobile Application Development
Pre-Requisites	: COSC 12043, COSC 22083
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe various mobile computing applications, technologies and wireless communication
explain common paradigms in mobile computing
develop mobile application using a selected development environment
construct different user interfaces and review user experiences
discuss current trends in mobile development.

Course Content:
Overview: Mobile Technologies, anatomy of a mobile device, survey of mobile devices, applications of mobile computing; Application Design: Context, information architecture, design elements, mobile web vs native Applications; Development Environments: Introduction to Android Studio and Xcode, The Model-View-Controller model, The Delegate Pattern, The iPhone and Android SDKs; The User Experience: The small screen problem, the unified look and feel paradigm, common user interface guidelines; The current trends and future of mobile development.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Pattanaik, P. K., Mall, R. (2015). Fundamentals of Mobile Computing. 2^nd PHI Learning.
Sinha, K., Ghosh S. C., Sinha, B. P. (2016). Wireless Networks and Mobile Computing. 1^st Chapman and Hall/CRC.
Huang, D., Wu, H. (2017). Mobile Cloud Computing: Foundations and Service Models. 1^st Morgan Kaufmann.
Horton, J. (2018). Android Programming for Beginners: 2^nd Packt Publishing.
Clayton, C. (2018). iOS 12 Programming for Beginners. 3^rd Edition. Packt Publishing.

COSC 32162 - Big Data Technologies

Course Code	: COSC 32162
Title	: Big Data Technologies
Pre-Requisites	: COSC 12033, COSC 12043
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe MapReduce as a computation model and an execution framework
use the big data application stack
compare different tools in the Hadoop stack fit in the big picture of big data analytics
design distributed machine learning algorithms
use cloud computing services (Amazon Web Services) to build your clusters and run large-scale data processing applications
identify and describe the data warehousing concepts and operations.

Course Content:
Big Data introduction: Big data: definition and taxonomy, Big data value for the enterprise, Setting up the demo environment, first steps with the Hadoop ecosystem; The Hadoop ecosystem: Introduction to Hadoop, Hadoop components: MapReduce/Pig/Hive/HBase, loading data into Hadoop, Handling files in Hadoop, Getting data from Hadoop; Querying big data with Hive: Introduction to the SQL Language, from SQL to HiveQL, using Hive to query Hadoop files; Big Data & Machine Learning: Machine learning tools: Spark & SparkML, H2O, Azure ML; Data Warehousing: Data Warehouse introduction, SQL OLAP Extensions, An Algebraic OLAP Operator; Object-Oriented and Object-Relational Databases: Object-Oriented Data Model, Object-Relational Database Systems.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

White, T. (2015). Hadoop: The definitive guide. 4^th O'Reilly Media, Inc..
Mayer-Schönberger, V., Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
Marz, N., Warren, J. (2015). Big Data: Principles and best practices of scalable real-time data systems. New York; Manning Publications Co.
Jukic, N., Vrbsky, S., Nestorov, S. (2020). Database systems: Introduction to databases and data warehouses. 2^nd Edition. Prospect press.

Level 04

COSC 44172 - Human Computer Interaction

Course Code	: COSC 44172
Title	: Human Computer Interaction
Pre-Requisites	: COSC 21052, COSC 31103
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe how interface design practices and methods can be integrated with user centered principles and methods now being employed
identify current trends in HCI research
discuss the nature of the HCI design process
apply an integrated perspective to the design process
recognize the difficulties and pitfalls of translating theory and principles derived from research findings into practical advice on user-centered design
apply metaphorical reasoning and conceptual models to user interface design
express 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:
Fundamentals of HCI (theories, models, paradigms, usability studies and controlled experimentation); Interaction design basics; HCI in the software process: Design and implementation, Evaluation of user interfaces, universal design and user support; Current trends in HCI research; Ubiquitous and pervasive computing; Human factors that affect the development of software, and design of user interfaces for interactive systems.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Sharp, H., Preece, J., Rogers, Y. (2019). Interaction Design: Beyond Human-Computer Interaction. 5^th Wiley.
Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N. (2016). Designing the user interface: strategies for effective human-computer interaction. 6^th Pearson.
Dix, A., Finlay, J. E., Abowd, G. D., Beale, R. (2003). Human-Computer Interaction. 3^rd Pearson.
Platt, D. (2016). The Joy of UX: User Experience and interactive design for developers. 1^st Edition. Addison-Wesley Professional.

COSC 44183 - Computer Graphics and Visualization

Course Code	: COSC 44183
Title	: Computer Graphics and Visualization
Pre-Requisites	: COSC 11023
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain fundamental concepts within computer graphics such as geometrical transformations, illumination models, removal of hidden surfaces and rendering
identify the features in fundamental algorithms of computer graphics to compare and evaluate them
apply fundamental principles within interaction programming
demonstrate fundamental concepts within information visualization and scientific visualization.

Course Content:
Introduction: History of computer graphics, applications, graphics pipeline, physical and synthetic images, synthetic camera, modeling, animation, rendering, relation to computer vision and image processing, review of basic mathematical objects (points, vectors, matrix methods); Introduction to OpenGL: OpenGL architecture, primitives and attributes, simple modeling and rendering of two- and three-dimensional geometric objects, indexed and RGB color models, frame buffer, double buffering, GLUT, interaction, events and callbacks, picking; Geometric transformations: Homogeneous coordinates, affine transformations (translation, rotation, scaling, shear), concatenation, matrix stacks and use of model view matrix in OpenGL for these operations; Viewing: Classical three dimensional viewing, computer viewing, specifying views, parallel and perspective projective transformations, Visibility- z-Buffer, BSP trees, Open-GL culling, hidden-surface algorithms; Shading: Light sources, illumination model, Gouraud and Phong shading for polygons. Rasterization- Line segment and polygon clipping, 3D clipping, scan conversion, polygonal fill, Bresenham's algorithm; Discrete Techniques: Texture mapping, compositing, textures in OpenGL; Ray Tracing- Recursive ray tracer, ray-sphere intersection; Representation and Visualization: Bezier curves and surfaces, B-splines, visualization, interpolation, marching squares algorithm.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Angel, E., Shreiner, D. (2014). Interactive Computer Graphics with WebGL. 7^th Pearson.
Gordon, V. S., Clevenger, J. L. (2018). Computer Graphics Programming in OpenGL with JAVA 2^nd Mercury Learning & Information.
Gortler, S. J. (2012). Foundations of 3D computer graphics. MIT Press.
Marschner, S., Shirley, P. (2015). Fundamentals of computer graphics. 4^th Edition. CRC Press.

COSC 44193 - Advanced Databases

Course Code	: COSC 44193
Title	: Advanced Databases
Pre-Requisites	: COSC 22083
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

apply the principles of query optimization to a database schema
identify transaction processing
apply concurrency control techniques
describe different types of database failures
apply appropriate recovery techniques
formulate complex queries
develop stored procedures, functions and triggers
design queries against a distributed database management system
design queries against database designed with object-relational extensions
develop and query No-SQL databases.

Course Content:

Advanced Features of SQL: Relational algebra review and join commands, additional join operations, SELF join, FULL joins, Set-Theoretic operators, the HAVING clause, views; Stored Procedures and Triggers: Stored Procedures and their usage in relational database management systems, stored functions, constraints and triggers; Query Optimization: Stages in query processing, query processing algorithms, query plan execution, cost-based query optimization; Concurrency and Recovery: Transactions and the ACID property of transactions, serializability and the serializability theorem, two-phase locking, time ordering techniques, recovery techniques; Database System Architectures: Centralized and Client-Server systems, parallel databases, distributed databases, heterogeneous and homogeneous databases, distributed query processing;

No-SQL Databases: Motivations for Not Just/No SQL (NoSQL) databases, variety of NoSQL databases, introduction to Key-Value databases, Key-Value database.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Elmasri, R., Navathe S.B. (2017). Fundamentals of database systems. 7^th Pearson Education India.
Silberschatz, A., Korth, H. F., Sudarshan, S. (2019). Database system concepts. 7^th McGraw-Hill.
Molinaro, A. (2005). SQL Cookbook: Query Solutions and Techniques for Database Developers. O'Reilly Media, Inc.
Perkins, L., Redmond, E., Wilson, J. (2018). Seven databases in seven weeks: a guide to modern databases and the NoSQL movement. 2^nd Pragmatic Bookshelf.
Harrison, G. (2015). Next Generation Databases: NoSQL and Big Data. Apress.
McLaughlin, M., Harper, J. (2014). Oracle Database 12c PL/SQL advanced programming techniques. McGraw-Hill Education Group.

COSC 44202 - Object Oriented Analysis and Design

Course Code	: COSC 44202
Title	: Object Oriented Analysis and Design
Pre-Requisites	: COSC 32133
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

design and implement projects using Object Oriented concepts
model real-world scenarios using UML diagrams
apply appropriate software design patterns effectively in large-scale software development
generate code from design using an appropriate software tool
compare and contrast various testing techniques.

Course Content:
UML Diagrams: Introduction to OOAD, Unified Process, UML diagrams, use case, class diagrams, interaction diagrams, state diagrams, sequence diagrams, activity diagrams, package diagrams, component and deployment diagrams, relationship between different diagrams, logical architecture and its refinements; General Responsibility Assignment Software Patterns (GRASP): Designing objects with responsibilities, creator, information expert, low coupling, high cohesion, controller, polymorphism; Coding and Testing.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Larman, C. (2004). Applying UML and patterns: an introduction to object oriented analysis and design and iterative development. 3^rd Prentice Hall.
Bennett, S., Farmer, R. (2010). Object-oriented systems analysis and design using UML. 4^th McGraw-Hill.
Gamma, E., Helm, R., Johnson, R., Vlissides, J. (1994). Design Patterns: Elements of Reusable Object-Oriented Software. 1^st Addison-Wesley.
Fowler, M. (2004). UML distilled: a brief guide to the standard object modeling language. 3^rd Addison-Wesley Professional.
Jorgensen, P. C. (2013). Software testing: a craftsman's approach. 4^th Edition. Auerbach Publications.

COSC 44213 - Information Assurance and Security

Course Code	: COSC 44213
Title	: Information Assurance and Security
Pre-Requisites	: COSC 12033
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain basic security concepts such as identity management and access control, as well as the basic cryptographic operations
demonstrate knowledge in SAML, XACML and Web services security
practice Multilevel and multilateral security
formulate forensics analysis after a security attack
describe the enterprise security relating to the business process modeling.

Course Content:
Introduction: Enterprise security and risk analysis; Identity management: Passwords and two-factor authentication, authentication protocols, single sign-on: SAML and OpenID; Access control: Access control matrix, ACLs, capabilities and PKI, attacks and sandboxing; Safety analysis; HRU and Take-grant; Federated access control: XACML; Web service security; Enterprise web service security and SAML; REST security and OAuth; Private key security: Stream ciphers, Birthday theorem, Block ciphers, Hash functions; Public key security: Diffie-Helman, El Gamal, RSA, Public key protocols, Advanced cryptosystems; Cryptography APIs: Java Cryptography Extension (JCE), Bouncy Castle, PKI and certificate management; Multilevel security: Bell-Lapadula, noninterference, non-deducibility, Role-based access control; Multilevel integrity: Biba, Multilateral security: Compartmentation, Chinese Wall, Clark-Wilson, BMA; Secondary uses and privacy.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Anderson, R. (2008). Security engineering. 2^nd John Wiley & Sons.
Saydjari, S. O. (2018). Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time. 1^st McGraw-Hill Education.
Shimeall, T., Spring, J. (2013). Introduction to information security: a strategic-based approach. Syngress.
Hook, D. (2005). Beginning cryptography with Java. John Wiley & Sons.
Vacca, J. R. (2017). Computer and information security handbook. 3^rd Edition. Morgan Kaufmann.

COSC 44223 - Machine Learning

Course Code	: COSC 44223
Title	: Machine Learning
Pre-Requisites	: COSC 32142
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain necessity of machine learning
describe wide variety of learning algorithms
compare and contrast different learning models
solve real world problems using machine learning techniques
demonstrate programming skills that will necessary to build intelligent, adaptive artifacts.

Course Content:
Introduction to machine learning; Types of Learning (Supervised, Unsupervised and Reinforcement); Inductive Classification; Decision Tree Learning; Ensemble Learning; Experimental Evaluation of Learning Algorithms; Computational Learning Theory; Rule Learning: Propositional and First-Order; Artificial Neural Networks; Support Vector Machines; Bayesian Learning; Instance-Based Learning; Clustering; Dimensionality Reduction; Association Rule Discovery; Language Learning; Text Classification; Introduction to Reinforcement Learning models.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Mitchell, T. M. (1997). Machine Learning, McGraw-Hill.
Bishop, C. M. (2011). Pattern recognition and machine learning. Springer.
Alpaydin, E. (2016). Machine learning: the new AI. MIT press.
Shalev-Shwartz, S., Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.
Hastie, T., Tibshirani, R., Friedman, J. (2016). The elements of statistical learning: data mining, inference, and prediction. 2^nd Springer Series in Statistics.
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning. MIT press.
Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc..

COSC 44232 - Advanced Computer Architecture and Operating Systems

Course Code	: COSC 44232
Title	: Advanced Computer Architecture and Operating Systems
Pre-Requisites	: COSC 22073
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

discuss how modern computer systems are designed, including details about pipeline, memory organization, virtual and physical memory, and memory technology
explain how to use instruction-level parallelism, memory-level parallelism, and thread-level parallelism
compare design of multiprocessor systems of combination of SIMD and MIMD structures, especially with graphic processors and other types of accelerating devices, including programming them
define, explain, and apply operating systems concepts
demonstrate experience in implementing and debugging operating system components, including the kernel module, system call, synchronization primitives, and the file system.

Course Content:

Computer Architecture: Combinational and sequential circuits, architectural design constraints; Instruction Level Parallelism: ILP concepts, Compiler techniques for exposing ILP, Dynamic Branch prediction, Dynamic Scheduling, Multiple instruction issue, Hardware based speculation, Static Scheduling, Multi-threading; Data Level Parallelism: Vector architecture - SIMD extension, GPU, Loop level parallelism; Thread Level Parallelism: Symmetric and distributed shared memory architectures, Performance issues, Synchronization, Models of Memory consistency, SMT and CMP processors.

Operating Systems: review of OS, file systems, privileged modes and protection levels, notable computer architectures, current trends and future insights, OS Interface, Memory Management, I/O System, Protection and Security.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Hennessy, J. L., Patterson, D. A. (2011). Computer architecture: a quantitative approach. 5^th Morgan Kaufmann.
El-Rewini, H., Abd-El-Barr, M. (2005). Advanced computer architecture and parallel processing. 1^st John Wiley & Sons.
Fountain, T., Kacsuk, P. (1997). Advanced computer architectures: a design space approach. 1^st Addison-Wesley.
Anderson, T., Dahlin, M. (2014). Operating systems: principles and practice (Vol. 2). Recursive books.
Tanenbaum, A. S., Woodhull, A. S. (2015). Operating systems: design and implementation. 3^rd Pearson India.
Stevens, W. R., Rago, S. A. (2013). Advanced Programming in the UNIX Environment. 3^rd Edition. Addison-Wesley.

COSC 44243 - Parallel and Distributed Computing

Course Code	: COSC 44243
Title	: Parallel and Distributed Computing
Pre-Requisites	: COSC 22073
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

develop and apply knowledge of parallel and distributed computing techniques and methodologies
apply design, development, and performance analysis of parallel and distributed applications
use the application of fundamental Computer Science methods and algorithms in the development of parallel applications
explain the design, testing, and performance analysis of a software system.

Course Content:

Introduction to Parallel and Distributed Computing; Parallel and Distributed Architectures; Socket programming; Parallel Performance; Shared Memory and Threads; Parallel Algorithms; OpenMP; Scalable Algorithms; Message Passing; MPI and Teragrid; Distributed Systems; MapReduce; Clusters; Distributed Coordination and Security; Distributed File Systems and Security.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Grama, A., Kumar, V., Gupta, A., Karypis, G. (2003). Introduction to parallel computing. 2^nd Pearson Education.
Tanenbaum, A. S., Van Steen, M. (2016). Distributed systems: principles and paradigms. 2^nd CreateSpace Independent Publishing Platform.
Quinn, M. J. (2004). Parallel Programming in C with MPI and OpenMP. McGraw Hill.
Kirk, D. B., Wen-Mei, W. H. (2016). Programming massively parallel processors: a hands-on approach. Morgan Kaufmann.

COSC 44252 - Research Methodologies

Course Code	: COSC 44252
Title	: Research Methodologies
Pre-Requisites	: COSC 11012
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

demonstrate knowledge of qualitative and quantitative research methods
explain the ethical considerations in research projects
formulate a scientific problem and evaluate relevant information for a scientific problem
analyse results with appropriate statistical methods and present results in a scientific manner
demonstrate understanding of the opportunities and limitations of science and its role in society and the responsibility for how it is used.

Course Content:
Information gathering; Formulation of aims for a research project; Formulation of scientific problems and hypotheses; Selection of methods for solving a scientific problem; Qualitative and quantitative research methods; Statistical analysis; Research Ethics.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
Final report/viva and other assessments announced at the beginning of the course unit.

Recommended Reading :

Oates, B. J. (2005). Researching information systems and computing. Sage.
Zobel, J. (2015). Writing for computer science. 3^rd Edition. Springer.

COSC 44263 - Theory of Computing

Course Code	: COSC 44263
Title	: Theory of Computing
Pre-Requisites	: COSC 11012
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

demonstrate the knowledge of advanced concepts of the theory of computing
distinguish computing models
simulate computing models.

Course Content:

Finite Automata: Deterministic and nondeterministic finite automata, Recursively Enumerable (RE) languages; Regular Expressions and Languages: Criteria for regular languages and expressions, pumping lemma, closure properties of regular languages; Context-Free Grammars and Languages: Context Free Grammars (CFGs), Context Free languages (CFLs), parse trees, and ambiguity in grammars and languages;

Pushdown Automata: Pushdown Automata (PDA) and Context-Free Languages (CFLs) and nondeterministic CFLs; Turing Machine (TM): Basic Turing machine, variations of TMs, restricted TMs, TMs and computers;

Undecidability: Codes for TMs, diagonalization language, recursive languages, complements of recursive languages and RE languages, universal language, reductions, post correspondence problem; Intractable Problems: Class P, NP and NP complete problems.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Sipser, M. (2007). Introduction to the Theory of Computation. 2^nd Thomson Course Technology.
Hopcroft, H. J., Motwani, R., Ullman, D. J. (2007). Introduction to Automata Theory Languages and Computation. 3^rd Pearson Education.
Sudkamp, A. T. (2005). Languages and Machines: An Introduction to the Theory of Computer Science. 3^rd Pearson.
Martin, C. J. (2014). Introduction to Languages and the Theory of Computation. 2007^thEdition. McGraw Hill Education.

COSC 44273 - Logic Programming

Course Code	: COSC 44273
Title	: Logic Programming
Pre-Requisites	: COSC 32142
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

demonstrate a deeper understanding of the basic logical concepts
implement logic programs for real world problems using an appropriate language
explain the notion of formal proof, and construct simple proofs in a natural deduction proof system for predicate logic
explain how logic programming differs from other programming paradigms.

Course Content:
Propositional Logic: syntax and semantics, natural deduction proofs, decision procedures; Predicate Calculus: syntax and semantics, natural deduction proofs, undesirability and incompleteness; Logic Programming: Horn fragment of predicate logic, unification and top-down operational semantics, use of a logic programming language, Datalog and bottom up operational semantics; Reasoning about sequential programs: partial correctness assertions, computing weakest preconditions, loop invariants, reasoning about termination.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Huth, M., Ryan, M. (2004). Logic in Computer Science: Modelling and reasoning about systems. 2^nd Cambridge university press.
Sterling, L., Shapiro, E. (1994). The Art of Prolog: Advanced Programming Techniques. 2^nd The MIT Press.
Shinghal, R. (1992). Formal Concepts in Artificial Intelligence. 1^st Chapman & Hall.
Bramer, M. (2013). Logic programming with Prolog. 2^nd Edition. Springer.

COSC 44283 - Theory of Compilers

Course Code	: COSC 44283
Title	: Theory of Compilers
Pre-Requisites	: COSC 22073
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

express the grammar of a programming language
develop lexical and syntax analyzers and use them in the construction of scanners and parsers
employ the operations of semantic analysis
develop a code generator
use different compiler optimization schemes in addition to efficient register allocation and garbage collection
design and program a complete working compiler for a given language.

Course Content:
Introduction to the structure of a compiler; Lexical Analysis: The role of the lexical analysis, input buffering, specification of tokens, recognition of tokens, finite automata; Syntax Analysis: Context –Free grammars, writing a grammar, top down parsing, bottom up parsing, LR Parsers, parser generators; Syntax Directed Translations: Definitions, applications, syntax directed translation schemes; Intermediate Code Generation: Variants of syntax trees, three-address code, types of declarations, translation of expressions, type checking, control flow, backpatching; Run Time Environments: Storage organization, stack allocation space, heap management, garbage collection; Code Generation: Issues in the design of code generator, The target language, address in the target code, basic blocks and flow graphs, code generation Algorithm, developing a simple compiler.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Cooper, K., Torczon, L. (2011). Engineering a compiler. 2^nd Morgan Kaufmann.
Aho, A. V., Lam, M.S., Sethi, R., Ullman, J. D. (2006). Compilers: Principles, Techniques, and Tools. 2^nd Addison-Wesley.
Grune, D., Van Reeuwijk, K., Bal, H. E., Jacobs, C. J., Langendoen, K. (2012). Modern compiler design. 2^nd Edition. Springer

COSC 44293 - Wireless Communication and Networks

Course Code	: COSC 44293
Title	: Wireless Communication and Networks
Pre-Requisites	: COSC 12033
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain the concepts of wireless communication systems
discuss generations of mobile networks
assess different wireless networks and communication infrastructures
review basic cellular system, frequency reuse, channel assignment strategies, handoff strategies and interference
explain the multiple access technique for wireless communications
design a simple wireless communication network.

Course Content:
Introduction to wireless communication systems: Evaluation of mobile radio communications, examples of wireless communication systems, paging systems, cordless telephone systems, compression of various wireless systems; Mobile wireless communication systems: second generation cellular networks, third generation wireless networks, wireless in local loop, wireless local area networks, Bluetooth and personal area networks; Cellular system design fundamentals: spectrum allocation, basic cellular system, frequency reuse, channel assignment strategies, handoff strategies, interference and system capacity, trucking and grade off service, improving coverage and capacity, cell splitting; Multiple access technique for wireless communications: introduction to multiple accesses, FDMA, TDMA, spread spectrum multiple access, SDMA, packet radio, capacity of cellular systems; Wireless Networking: Difference between wireless and fixed telephone networks, development of wireless networks, fixed network transmission hierarchy, traffic routing in warless networks, wireless data services, common channel signaling, Wireless LAN technology and Standards, Bluetooth, GPRS and 3G wireless systems, Wireless WAN communication in the infrastructure.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Stallings, W. (2004). Wireless Communications and Networking. 2^nd Prentice-Hall of India.
Garg, V. (2010). Wireless communications & networking. Elsevier.
Faludi, R. (2010). Building wireless sensor networks: with ZigBee, XBee, arduino, and processing. O'Reilly Media, Inc..
Osseiran, A., Monserrat, J. F., Marsch, P. (Eds.). (2016). 5G mobile and wireless communications technology. Cambridge University Press.

COSC 44303 - Natural Language Processing

Course Code	: COSC 44303
Title	: Natural Language Processing
Pre-Requisites	: COSC 32142
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe characteristics of language and its use and understand their implications for Natural Language Processing (NLP) and NLP applications
explain a broad selection of NLP applications, and describe problems or tasks addressed, materials and methods used, how the applications are evaluated, and opportunities for future developments for each application
analyze basic NLP tools, and use them to analyze small text corpora
use benchmark corpora, commercial and open-source text analysis and visualization tools to explore interesting patterns.

Course Content:
NLP: Introduction, Human languages, models, ambiguity, processing paradigms; Phases in NLP; NLP applications; Text representation in computers, encoding schemes; Linguistics resources: Introduction to corpus, elements in balanced corpus, TreeBank, PropBank, WordNet, VerbNet etc, resource management with XML, management of linguistic data with the help of GATE, NLTK; Regular expressions; Finite State Automata; Word recognition; Lexicon; Morphology; Acquisition models; Finite State Transducer; N-grams; Smoothing; Entropy; Hidden Markov Models (HMM); Support Vector Machines (SVM); Conditional Random Fields (CRF); Part of Speech (POS) tagging: Stochastic POS tagging, transformation based tagging in TBL, handling of unknown words, named entities, multi word expressions; Introduction text analytics; Information Extraction; Machine Translation.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Jurafsky, D., Martin, J. H. (2009). Speech and Language Processing. 2^nd Pearson Education.
James, A. (1994). Natural language Understanding. 2^nd Pearson Education.
Weiss, S. M., Indurkhya, N., Zhang, T. (2010). Fundamentals of Predictive Text Mining. Springer: New York.
Manning, C. D., Manning, C. D., Schütze, H. (1999). Foundations of statistical natural language processing. MIT press.
Deng, L., Liu, Y. (Eds.). (2018). Deep Learning in Natural Language Processing. Springer.

COSC 44313 - Image Processing and Computer Vision

Course Code	: COSC 44313
Title	: Image Processing and Computer Vision
Pre-Requisites	: COSC 11023
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

demonstrate the steps for solving of image processing/vision problems
apply knowledge and understanding of pre-processing, segmentation, description, and recognition
choose appropriate methods and implement solutions to small-scale image processing and vision problems.

Course Content:
Overview of Applications of Image Processing and Vision; Digital Image Formats; Colour Models; Data Types; Operators; Manipulating Matrices; File I/O; The Image Processing Toolbox; Thresholding; Histogram Equalization; Linear Filtering (convolution); Noise Reduction; Nonlinear Filtering; Gradients; Edge Magnitude and Direction; Finite Difference Filters; Laplacian of Gaussian Filter; Canny Edge Detector; Colour Transformations; Colour Histogram Equalization; Colour Median Filtering; Colour Gradient and Edge Detection; Thresholding as a form of Segmentation; Basic Global Thresholding; Optimal Global Thresholding; Techniques to improve global thresholding; Region Labeling; Boundary Tracing; Edge Based Segmentation; Region-based Segmentation; Hybrid Methods; Boundary-based measures of accuracy; Region-based measures of accuracy; Measuring Reproducibility; Boundary Descriptors; Region and Shape Descriptors; Texture Description; SIFT Features and Bags of Words; Supervised and Unsupervised Clustering; Nearest Neighbor Classifiers; Bayesian Classification; Training and Testing Methodologies; Morphological Image Processing; Frequency Domain Filtering.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Davies, E. R. (2017). Computer vision: principles, algorithms, applications, learning. 5^th Academic Press.
Solem, J. E. (2012). Programming Computer Vision with Python: Tools and algorithms for analyzing images. O'Reilly Media, Inc..
Hartley, R., Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge university press.

COSC 44323 - Emerging Technologies in Computer Science

Course Code	: COSC 44323
Title	: Emerging Technologies in Computer Science
Pre-Requisites	: All compulsory COSC course units of level 3
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

demonstrate the theoretical and practical knowledge on concepts learned on the selected emerging technologies.

Course Content:
Depends on the selected emerging technologies.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :
Reading list and material relevant for each selected topic to be provided at the beginning of the academic year by the lecturer.

COSC 44333 - Game Development

Course Code	: COSC 44333
Title	: Game Development
Pre-Requisites	: COSC 11023
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

express and practice basic game development (process: design, develop, test and present a beta version of a game of your own design)
use an Editor to create interesting game levels
create game scripts using appropriate languages and APIs
explain the fundamentals of using 2D and 3D graphics
express advanced topics such as shaders, physics, AI, and Network based games.

Course Content:
Introduction to game design and production; Production basics: Lighting, materials, effects, etc; Creating a simple game, working with 3D, games Rigidbody, physics, User input and UI, UI Development, Raycasting; 3D Characters: Animation & control, Intermediate Game Production, Event Systems & Delegates, Intermediate Game Production Topics, Data Structures, Global Illumination, 3D Models, Debugging, Introduction to Git, Beyond Game Design: Programming applications Algorithms & Trees.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination, practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Fullerton, T. (2018). Game design workshop: a playcentric approach to creating innovative games. 4^th AK Peters/CRC Press.
Schell, J. (2014). The Art of Game Design: A book of lenses. 2^nd AK Peters/CRC Press.
Nystrom, R. (2014). Game programming patterns. Genever Benning.
Hocking, J. (2018). Unity in action: Multiplatform game development in C#. 2^nd Manning Publications.
Bond, J. G. (2017). Introduction to game design, prototyping, and development: from concept to playable game with Unity and C#. 2^nd Edition. Addison-Wesley Professional.

COSC 44343 - Data Science

Course Code	: COSC 44343
Title	: Data Science
Pre-Requisites	: COSC 32142
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

describe what is data science and the skill sets needed to be a data scientist
explain the basic concepts of statistical inference
identify probability distributions commonly used as foundations for statistical modeling
use an appropriate language to carry out basic statistical modeling and analysis
explain the significance of Exploratory Data Analysis (EDA) in data science
describe the data science process and how its components interact
use APIs and other tools to scrap the Web and collect data
apply basic machine learning algorithms for predictive modeling
identify common approaches used for feature generation
identify basic dimensionality reduction algorithms.

Course Content:
Data Wrangling: Static Files, SQL, Web Scraping, APIs and Messy Data; Statistical Inference: Event Space, Probability, Distributions and Hypothesis Testing; Summarizing and Visualizing Data: Descriptive Statistics, Univariate and Multivariate Exploratory Data Analysis; Predictive Modeling: Regression, Classification, Data Preprocessing, Model Evaluation and Ensembles; Data Mining: Dimensionality Reduction, Clustering, Association Rules, Anomaly Detection, Network Analysis and Recommender Systems; Specialty Topics: Data Engineering, Natural Language Processing, and Web Applications.

Method of Teaching and Learning:
Lectures, Tutorials and Assignments

Assessment:
End-of-course written examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

O'Neil, C., Schutt, R. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc..
VanderPlas, J. (2016). Python data science handbook: essential tools for working with data. O'Reilly Media, Inc..
Kelleher, J. D., Tierney, B. (2018). Data science. MIT Press.
EMC Education Services (Ed.). (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons.

COSC 44353 - Cloud Computing

Course Code	: COSC 44353
Title	: Cloud Computing
Pre-Requisites	: COSC 12033
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

explain and use cloud computing environments
apply knowledge of standards in distributed computing, and the impact of standardisation on application programs
practice the deployment of cloud computing applications
explain the cloudlet architecture.

Course Content:
Fundamentals of Cloud Computing; Concepts and Models; Cloud-enabling Technology; Cloud Security; Cloud Infrastructure Mechanisms; Cloud Management; Cloud deployment; Cloud Computing Architecture; Cloudlet architecture; Parallel programming in the cloud; Virtualization; Distributed storage systems; Cost Metrics and Pricing; Service Quality Metrics and Software License Agreements.

Method of Teaching and Learning:
Lectures, Tutorials, Assignments and Practical

Assessment:
End-of-course written examination, practical examination and other assessments announced at the beginning of the course unit.

Recommended Reading :

Erl, T., Puttini, R., Mahmood, Z. (2013). Cloud Computing: Concepts, Technology & Architecture. Prentice Hall
Rosenberg, J., Mateos, A. (2010). The Cloud at Your Service: The When, How, and Why of Enterprise Cloud Computing. Manning Publications Co.
Rafaels, R. J. (2018). Cloud Computing: From Beginning to End. 2^nd Edition. CreateSpace Independent Publishing Platform.

COSC 44364 - Industrial Training

Course Code	: COSC 44364
Title	: Industrial Training
Pre-Requisites	: All compulsory COSC course units of level 1, 2 and 3
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

relate concepts learned in the class to computing project in the industry
use and apply the theory, knowledge, skills and values acquired through their first, second and third years in areas related to their interests and learning needs
identify the issues in applying concepts and acquire skills in resolving these issues in working environment
demonstrate a range of work skills required for the industry
employ a range of soft skills in the industry setting
identify the networks developed to advance their career opportunities.

Course Content:
Major aspects to be covered are the main phases of system 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 a computing industry.

Assessment:
Basd on Presentations/ Viva, Diary, Report and Industry Evaluation

Recommended Reading :
Reading and reference material recommended by the relevant industry.

COSC 43378 - Research Project

Course Code	: COSC 43378
Title	: Research Project
Pre-Requisites	: All the compulsory COSC courses
Co-Requisites	: None

Learning Outcomes:
At the completion of this course student will be able to:

demonstrate the ability to research in an area of computer science
interpret and assess literature related to a current area of research
experiment with algorithms whose properties are not known in advance
demonstrate the ability to work and plan independently.

Course Content:
A study and/or an implementation of a computer system related to major sub disciplines of computer science domain under an assigned supervisor.

Method of Teaching and Learning:
A combination of self-study, seminars, presentations and a dissertation.

Assessment:
Based on Progress Reports / Presentations, Thesis and Viva

Recommended Reading :
Reading list and material relevant for the selected research topic.