• State key characteristics of a database
• Develop conceptual models for databases
• Create efficient databases
• Apply query language to create and manipulate databases

• Introduction to database concepts and architecture: File systems, database system concepts, three-schema architecture, classifications of database systems and database users
• Data Modeling: Entity Relationship model, relational model, network model, hierarchical model, object relational model, UML class diagrams.
• Relational database design: Relational model concepts, defining a relational schema from an ER diagram, basics of functional dependencies and normalization (1NF, 2NF, 3NF and BCNF)
• Developing and manipulating databases: Data development and manipulation using SQL, MySQL, PostgreSQL and MongoDB
• Relational algebra and relational calculus: Binary operations, Cartesian product, extended relational operator, tuple relational calculus and domain relational calculus
• File organization for conventional DBMS: Storage devices and their characteristics, file organization, fixed-length records, variable-length records, sequential file organization, indexed sequential access method
• Introduction to transaction management, concurrency control and recovery: Concept of transactions, concurrency in transaction processing, recovering databases from failure

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Ramez Elmasri and Shamkant B. Navathe, Fundamentals of Database Systems, 7^th Ed, Addison-Wesley, 2015.
• J. Date, An Introduction to Database Systems, 8^th Ed, Addison-Wesley, 2003
• Ramakrishnan and Gehrke, Database Management Systems, 3^rd Ed., McGraw-Hill, 2003.

• Implement appropriate data structures to manipulate data for various computational problems
• Devise programs to solve complex computational problems
• Create databases using database management systems
• Develop web based applications that interact with databases

• Designing and Implementing algorithms: Recursion, backtracking, Divide-and-conquer, and Dynamic programming.
• Fundamental data structures and their applications: Arrays, Lists, Stacks, Queues, Linked lists, Trees, and Graphs
• Database design, modeling and development: SQL (MySQL, MariaDB) and NoSQL (MongoDB, PostgreSQL)
• Develop web based applications: Web development using HTML, CSS and Scripting languages (PHP, JavaScript, JQuery, NodeJS)

• Assessment on practical records———————————–10%
• End-of-First Semester Practical Assessment———————-30%
• End-of-Second Semester Practical examination——————60%

• P. Deitel and H. Deitel, Java How To Program (Early Objects), 10^th Ed, Prentice Hall, 2014.
• R. Sedgewick and K. Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011.
• N. Karumanchi, Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles, 5^th Ed, 2016
• D. Kalemis, The Fundamental Concepts of Object-Oriented Programming, 2013.
• R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 7^th Ed, Addison-Wesley, 2015.
• D. Bartholomew, Getting Started with MariaDB, 2^nd Ed, 2015.

• Describe the objective and functions of modern operating systems
• Explain how resources (such as CPU, memory, storage, file and devices) are managed by the operating system
• Demonstrate the operations of a prototypical process manager
• Compare various techniques used for concurrency control

• Introduction to operating system: Architecture of modern operating systems (OS), evolution of OS, OS operations and functionalities, and open source OS
• Processes and Threads: Concept of process, process states, process control block, schedulers, context switch, interprocess communication, process scheduling, overview of threads, multicore programming and multithreading models
• Concurrency: Process synchronisation (race condition, critical-section problem, mutex locks, semaphores, classic problems of synchronization and monitors), deadlock (characterization, prevention, avoidance, detection and recovery)
• Memory management: Swapping, memory allocation, fragmentation, paging, segmentation, virtual memory and address translation
• Storage management: Mass Storage, host attached storage, network attached storage, storage area network and RAID
• File and I/O Device management: File organization and access, file system security, device drivers, direct memory access and interrupt handling

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• W. Stalling, Operating systems: Internals and Design Principles, 8^th Ed, Pearson, 2014.
• A. Silberschatz, P. B. Galvin, G. Gagne, Operating System Concepts, 9^th Ed, 2013.
• S. Tanenbaum and H. Bos, Modern Operating Systems, 4^th Ed, 2014.

• Analyse the correctness and the performance of complex algorithms
• Discuss the implementation of standard data structures
• Demonstrate skills in solving complex computational problems using suitable data structures
• Explain the divide-and-conquer paradigm and dynamic programming strategies and their usages

• Proof of correctness of algorithms: Contrapositive and contradiction, Induction, and Loop invariants
• Recurrence relations: Analysis of iterative and recursive algorithms (Quick sort and merge sort, etc.)
• Fundamental Data Structures: Arrays, Lists, Stacks, Queues, Linked lists, Trees, and Graphs
• Algorithm Design and Implementation Techniques: Divide-and-conquer paradigm, Dynamic programming algorithms, Recursive, and backtracking
• Applications of Trees and Graphs: Binary search, Dijkstra’s shortest path, minimum spanning tree

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, 3rd Ed., MIT Press, 2009.
• R. Sedgewick and K. Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011.
• N. Karumanchi, Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles, 5th Ed. 2016.
• S. S. Skiena, The Algorithm Design Manual, 2nd Ed., Springer, 2011.

• Discuss the software engineering principles and life-cycle
• Identify different roles played by personnel in software development and their responsibilities
• Construct software designs based on user requirements
• Apply appropriate techniques in software development, testing, maintenance, and evolution

• Introduction to Software Engineering: Software characteristics, impact of software, importance of engineering approaches, challenges and ethics in software development
• Introduction to systems analysis and design: Types of systems (transaction processing, management information, decision support, etc.), need for systems analysis and design, the system development life cycle, roles played by different personnel in system development life cycle including the role of the systems analyst
• Software development process models: Waterfall model, prototyping model, spiral model, evolutionary model, iterative model and agile methodology
• Software requirements and specifications: Types of requirements, requirement gathering processes and techniques, documenting requirements
• Software analysis techniques: Data flow diagrams, data dictionaries, process specifications and structured decisions
• Software design techniques: Object-oriented design using UML, Agile methodologies using SCRUM
• Software testing: Development testing, test-driven development, release testing and user testing
• Software maintenance and evolution: Evolution processes, program evolution dynamics, software maintenance and legacy system management

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• L. Sommerville, Software Engineering, 10^th Ed, 2015.
• E. Kendall and J. E. Kendall, System Analysis and Design, 9^th Ed, 2013.
• R. E. Beasley, Software Engineering: Principles and Practices, 2^nd  Ed, 2015.

• Develop an understanding of the theory of vector spaces.
• Use the theory of linear transformations and their matrix representation
• Solve systems of linear equations and understand the conditions for the existence of solution
• Use determinations and spectral properties.

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• Devi Prasad, Elementary Linear Algebra, 2^nd Ed,, Narosa Publishing House, New Delhi, 2012
• David Lay C, Linear Algebra and Its Applications, 4^th Ed,, Pearson (Addison Wesley) Publication, 2012;
• Seymour Lipschutz, Schaum’s Theory and problems of linear algebra, 2011
• Datta K.B, Matrix and Linear Algebra, Prentice hall of India Pvt. Ltd, New Delhi–110001, 2003

• Explain the conceptual design and the organisation of a computer system
• Describe processor unit design and its operations
• Summarise memory and Input/Output organisation
• Build Assembly language programs

• Introduction to modern computer architecture: Architectural and technological design and development, and performance measures of a processor
• Instruction set architecture models: Instruction set architectures and design, memory locations and operations, addressing modes, instruction types, microprogramming
• Processing unit design: CPU basics, register set, data path, CPU instruction cycle, control unit design, instruction pipelining techniques
• Memory hierarchies and Input/Output organisation: Memory structure and hierarchy, cache memory mapping, direct memory access, virtual memory, interrupt-driven I/O, and Input-Output interfaces
• Assembly language programming: Instructions mnemonics and syntax, assembler directives and commands, assembly and execution of programs

• In-course Assessment (Theory) —————————— 10%
• In-course Assessment (Practical) —————————- 30%
• End-of-course Examination ———————————- 60%

• D. A. Patterson and J. L. Hennessy, Computer Organization and Design: The Hardware and Software Interface, Morgan Kaufmann Publishers, 5^th Ed, 2013.
• M. Abo-El-Barr and H. El-Rewini, Fundamentals of Computer Organization and Architecture, A John Wiley & Sons Publication, 2004.
• W. Stallings, Computer Organization and Architecture, Prentice Hall Publishers, 10^th Ed, 2015.

• Describe the fundamental issues in the design and the use of major programming languages
• Demonstrate the differences of programming paradigms in different programming languages
• Discuss the use of formal methods for program verification
• Build concurrent and functional programs

• Introduction: Programming domains, evaluation criteria for programming languages, influences on language design, programming language categories
• Introduction to syntactic and semantic description of programming languages
• Programming paradigms in different programming languages: Data types, Abstract data types, Data objects, Control structures, subprograms, lifetime and scope of variables and functions, object-oriented programming, exception handling
• Concurrency: Basics of concurrency, subprogram-level concurrency, monitors, message passing, threads
• Functional programming: Fundamentals and programming with functional programming languages

• In-course Assessment (Theory)  ——————————– 10%
• In-course Assessment (Practical) ——————————- 30%
• End-of-course Examination ————————————- 60%

• R. W. Sebesta, Concepts of Programming Languages, Pearson, 2016.
• J. C. Mitchell, Concepts in Programming Languages, Cambridge University Press, 2003.
• C. Ghezzi and M. Jazayeri, Programming language concepts, 3rd Ed, 1997.

• Describe computational genomics and phylogenetics concepts
• Demonstrate the use of computational tools for sequence analysis in bioinformatics
• Perform Data analysis and Pattern recognition in biological data
• Formulate a biological problem as a computational problem

• Introduction to bioinformatics: Aims and tasks of bioinformatics, scope of bioinformatics and its applications, bioinformatics databases.
• Structural bioinformatics: Protein structure and its visualsation, comparison and classification, protein structure prediction, RNA structure prediction, compression of genomic sequences such as Burrows–Wheeler transform, etc.
• Pairwise sequence alignments and database search: Scoring matrix, Needleman-Wunsch algorithm, Smith-Waterman algorithm, Gotoh algorithm, heuristic methods
• Phylogenetic tree and multiple sequence alignment: Neighbour-joining and UPGMA algorithms, phylogenetic tree,  Sequence profile & profile based alignments
• Pattern Recognition: Clustering and visualisation, Hidden Markov models and Viterbi algorithm
• Genomics and proteomics: Genome mapping, genome assembly, genome  comparison, functional genomics, proteomics and metabolomics

• In-course Assessment (Theory) ————————————– 10%
• In-course Assessment (Practical) ———————————— 30%
• End-of-course Examination —————————————— 60%

• B. Bergeron, Bioinformatics Computing, Prentice Hall, 2002.
• K. Stephen, Introduction to Bioinformatics: A Theoretical and Practical Approach, 1^st Ed, 2003
• F. Azuaje and J. Dopazo, Data Analysis and Visualization in Genomics and Proteomics, John Wiley, 1^st Ed, 2005

• Design websites using advanced features of Markup and Client-side scripting languages
• Use XML technologies for web applications
• Employ knowledge on web programming to develop and maintain web applications
• Develop secure web-based systems using server-side scripting languages
• Recommend  practices that ensure legal and ethical responsibilities

• Advanced use of scripting languages: Client-side scripting (HTML, CSS, JavaScript, etc.) and Server-side scripting (PHP, JSP, ASP, etc.)
• XML Technologies: XSL, XSLT, xPath and xQuery
• Secure web programming: Authentication, access control, session management, SQL injections and cross site scripting (XSS)
• Trends in Web development: Web 2.0, AJAX, JSON, Web Services
• Best practices in Web Development: Architectural patterns, search engine optimization (SEO), frameworks, auditing and logging

• In-course Assessments (Theory) ———————————– 10%
• In-course Assessments (Practical) ——————————— 30%
• End-of-course practical Examination —————————– 60%

• S. Purewal, Learning Web App development, 1^st Ed., 2014.
• D. Stuttard and M. Pinto, The Web Application Hacker’s Handbook: Finding and Exploiting Security Flaws, 2^nd Ed., 2011.
• J. J. Jackson, Web Technologies: A Computer Science Perspective, 1st Ed., 2006.
• A. Godbole and A. Kahate, Web Technologies, TCP/IP, Web/Java Programming, and Cloud Computing, McGraw Hill Education, 3rd Ed, 2017.

• Demonstrate familiarity with latest trends in computer science and their applications
• Describe the key architectures and applications in edge computing
• Summarise standard open-source cloud and edge computing software for data analytics
• Build microcontroller programs for IoT
• Discuss the latest languages and frameworks used in IT industries

• Edge computing: Introduction to edge computing, cloud computing analytics pipeline, cloud databases
• Data analytics: Introduction to deep learning, data mining, and its applications; introduction to hadoop, spark, and mapreduce
• Internet of things (IoT): IoT concepts and technologies, its applications, micro-controller programming using sensors and actuators with arduino, IoT security and privacy issues
• Blockchain: Fundamentals of blockchain, distributed ledger technology, cryptocurrency, and related algorithms
• Introduction to mobile application development: Mobile app development platforms (Android, iOS, etc.), development and deployment of applications

• Formative Assessment: Industrial Visit* ——————————————10%
• Summative Assessment: Individual/Group Assignments† ———————-90%

*Students will be taken to four to six leading software development companies in Sri Lanka in one or two industrial visit(s). Each visit may take up to three days. The type of assignments includes but are not limited to presentations and report writings on the observation of the industrial visit.
†At the end of each of the five chapters, students will be given five assignments (including programming tasks) based on the key areas covered in the five chapters. Of the five assignments, at most two may be done in groups.

• Holler, V. Tsiatsis, C. Mulligan, S. Avesand, S. Karnouskos and D. Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence”, 1^st Ed, Academic Press, 2014.

>>    Conference and journal papers related to the course material will be posted on the course website.

• Discuss the concepts of professional practice in computing
• Explain the context in which computer professionals work
• Apply the key skills, knowledge, attributes and attitudes required to be an IT professional, with particular reference to professional practice, code of ethics and professional standards
• Analyse legal issues in relation to data privacy and software use
• Recognize professional conduct in an ethical manner  in day to day activities as an IT professional
• Apply the principles of group work and reflect on the nature of working in teams, with the appreciation of the issues, such as ethics, conflict resolution, negotiation in culturally diverse workplace

• Computer ethics and professional practice: Ethical argumentation and theories, moral assumptions and values, the role of computing professional, and professional communication practices
• Intellectual property: Intellectual property rights, Intangible digital intellectual property, legal foundations for intellectual property protection
• Privacy and data protection: Privacy of computer data, respecting human dignity, protecting data stored on computers, ethical hacking and its implications
• Security policies, laws and computer crimes: Computer crimes and legal redress for computer criminals, Issues surrounding the misuse of access and breaches in security, crime prevention strategies

• In-course Assessments —————————————30%
• End-of-course Examination ———————————-70%

• G. W. Reynolds, Ethics in Information Technology, 5^th Ed, 2014.
• M. F. Bott, Professional Issues in Information Technology, The British Computer Society, 2^nd Ed, 2014.
• ACM Code of Ethics, ACM, www.acm.org, 2017.

• Describe the importance of Software Engineering
• Use appropriate techniques to gather requirements
• Create software designs based on gathered requirements
• Apply best practices in software development, maintenance and evolution

• Introduction to Software Engineering: Software characteristics, Impact of Software, Importance of Engineering approach, Challenges and Ethics
• Models of Software Process: Waterfall model, Prototyping model, Spiral model, Evolutionary model, Iteration model and Agile model
• Software Requirements and Specifications: Types of Requirements, Requirement gathering processes and techniques, Documenting requirements
• Software Design and Implementation: Object-oriented design using UML, Design patterns, Implementation issues, Open Source development
• Software Testing: Development testing, Test-driven development, Release testing and User testing.
• Software maintenance and evolution: Evolution processes, Program evolution dynamics, Software maintenance and Legacy system management

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Ian Sommerville, Software Engineering, 10th Edition, 2015
• Robert E Beasley, Software Engineering: Principles and Practices, 2nd Edition, 2015

• Demonstrate the concepts of Object Oriented Programming paradigm
• Create applications using Object Oriented Programming concepts
• Design Relational Databases using database management principles
• Develop web based applications that interact with database management systems

• Introduction to Concepts of Object Oriented paradigm: Objects, classes, methods, parameter passing, information hiding, inheritance, encapsulation and polymorphism
• Object Oriented Programming using a high level programming language
  Data management using a relational model
• Database design, modeling and development using Structured Query Language
• Develop web based applications using PHP and MariaDB

• Continuous assessment on practical records ————30%
• Two mid-semester practical examinations —————30%
• Two end-of-semester practical examinations ————40%

• P. Deitel and H. Deitel. Java How To Program (Early Objects), 10th Edition, Prentice Hall, 2014.
• D. Kalemis, The Fundamental Concepts of Object-Oriented Programming, 2013.
• R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 7th edition, Addison-Wesley, 2015.
• Daniel Bartholomew, Getting Started with MariaDB, 2nd edition, 2015.

• State key characteristics of a database
• Develop a conceptual model for given requirements
• Create an efficient relational database
• Apply Structured Query Language to create and manipulate database

• Information Management Concepts: Information capture and representation, Information storage and retrieval, supporting human needs (searching, retrieving, linking, browsing, navigating, Analysing and indexing), Quality issues (Reliability, security, scalability, efficiency) and Information management applications
• Database Systems: File systems, Evolution of DBMS, Database architecture, Data independence, DBMS user (designer, application developer, administrator), DBMS functions and system components
• Data Modeling: Entity Relationship model, Relational model, Network model, Hierarchical model and object relational model
• Relational Database Design: Schema design, mapping conceptual schema to relational schema, Normal Forms (1NF, 2NF, 3NF and BCNF)
• Structured Query Language: Basic structure, Subqueries, Set operations, Aggregate functions, Derived relations, and Views
• Relational algebra: Relational algebra, Tuple relational calculus and domain calculus

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• RamezElmasri and Shamkant B. Navathe, Fundamentals of Database Systems, 7th edition, Addison-Wesley, 2015.
• C.J. Date, An Introduction to Database Systems, 8th edition, Addison-Wesley 2003
• Ramakrishnan and Gehrke, Database Management Systems, 3rd edition, McGraw-Hill 2003.

• Explain the conceptual design and the organisation of a computer system
• Describe processor unit design and its operations
• Summarise memory hierarchies and Input / Output organisation
• Write assembly language programs

• Introduction to modern computer architecture: Architectural and Technological Development, and Performance Measures
• Instruction set architecture models: Instruction Set Architectures and Design, Memory Locations and Operations, Addressing Modes, Instruction Types, Microprogramming
• Hardware structures for digital arithmetic computations
• Processing Unit Design: CPU Basics, Register Set, Data Path, The CPU Instruction Cycle, Control Unit, Instruction pipelining techniques
• Memory hierarchies and Input / Output organisation: Memory structure, Interrupt-Driven I/O, and Input-Output Interfaces
• Assembly language programming: Instructions Mnemonics and Syntax, Assembler Directives and Commands, Assembly and Execution of Programs

• In-course Assessment (Theory) —————–20%
• In-course Assessment (Practical) —————20%
• End-of-course Examination ———————60%

• David A. Patterson and John L. Hennessy, Computer Organization and Design: The Hardware and Software Interface, Morgan Kaufmann Publishers, 5th Edition, 2013
• Mostafa Abo-El-Barr and Hesham El-Rewini, Fundamentals of Computer Organization and Architecture, A John Wiley & Sons Publication, 2004
• William Stallings, Computer Organization and Architecture, Prentice Hall Publishers, 10th Edition, 2015

• Develop an understanding of the theory of vector spaces.
• Understand the theory of linear transformations and their matrix representation
• Solve systems of linear equations and understand the conditions for the existence of solution
• Use Determinations and spectral properties.

Vectors in Rⁿ, norms and inner products in Rⁿ, Cauchy- Schwartz and triangular inequalities, Gram-Schmidt process. Elementary operations and elementary matrices, echelon and row reduced echelon matrices. Vector spaces, linear dependence, and independence, subspaces, basis and dimension, Steinitz replacement theorem. Linear transformations, matrix representation and change of base, column rank, row rank and nullity of matrix. Determinants and their properties, invertibility of a square matrix, eigenvalues and eigenvectors, characteristic polynomials, cayley-Hamilton theorem, orthogonal, symmetric and skew symmetric matrices, quadratic forms, diagonalization, System of linear equations.

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Devi Prasad, Elementary Linear Algebra (2nd Edition), Narosa Publishing House, New Delhi, 2012
• David Lay C, Linear Algebra and Its Applications, 4th edition, Pearson (Addison Wesley) Publication, 2012;
• Seymour Lipschutz, Schaum’s Theory and problems of linear algebra, 2011
• Datta K. B, Matrix and Linear Algebra, Prentice hall of India Pvt. Ltd, New Delhi – 110001, 2003

• Outline modern operating systems
• Describe the core functionalities of an Operating System
• Demonstrate the operations of a prototypical process manager

• Introduction to Operating System: Architecture of Modern Operating Systems (OS), Evolution of OS, OS operations and functionalities, and Open source OS
• Processes and Threads: Concept of Process, Process states, Process Control Block, Schedulers, Context switch, Interprocess Communication, Process scheduling, Overview of Threads, Multicore Programming and Multithreading Models
• Concurrency: Process synchronization (Race Condition, Critical-Section Problem, Mutex Locks, Semaphores, Classic Problems of Synchronization and Monitors), Deadlock (Characterization, Prevention, Avoidance, Detection and Recovery)
• Memory management: Swapping, Memory allocation, Fragmentation, Paging, Segmentation, Virtual memory and Address Translation
• Storage management: Mass Storage, Host attached storage, Network attached storage, Storage Area Network and RAID
• File and I/O Device management: File Organization and Access, file system security, Device drivers, Direct Memory Access and Interrupt handling

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Stallings, W. Operating systems: internals and design principles, 8th edition, Pearson, 2014.
• Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, Operating System Concepts, 9th edition, 2013.
• S. Tanenbaum and Herbert Bos, Modern operating systems ,4th edition, 2014.

• Describe the fundamental issues in the design and the use of major programming languages
• Demonstrate the differences of Programming paradigms in different programming languages
• Write concurrent and functional programs

• Introduction: Programming Domains, Evaluation criteria for programming languages, Influences on Language design, Programming language categories
• Introduction to syntactic and semantic description of programming languages
• Programming paradigms in different programming languages: Data types, Abstract data types, Data objects, Control structures, Subprograms, object-oriented programming, Exception handling
• Concurrency: Basics of concurrency, Subprogram-Level Concurrency, Monitors, Message Passing, Threads
• Functional programming: Fundamentals and Programming with Functional Programming Languages

• In-course Assessment(Theory) —————–20%
• In-course Assessment (Practical) ————–20%
• End-of-course Examination ——————–60%

• Robert W Sebesta, Concepts of Programming Languages, Pearson.
• John C Mitchell, Concepts in Programming Languages”, Cambridge University Press

• Explain the structure of complex data structures, their implementation, the algorithms that manipulate them and their amortized analysis
• Develop efficient algorithms to solve complex problems
• Evaluate complexity of algorithms
• Demonstrate skills with applied algorithmic settings on operational research, parallel and distributed computing, and operating systems

• Proof of correctness of algorithms: Inductive proofs and loop invariants
• Divide-and-conquer paradigm: Sorting and Searching algorithms that use this paradigm, synthesize divide-and-conquer algorithms, Derive and solve recurrences describing the performance of divide-and-conquer algorithms
• Abstract data types: Implementation of Trees and Graphs, Dijkstra’s Algorithm, Depth-First vs Breadth-First Searches and their complexity
• Greedy approach: Principles of Greedy Algorithms and their applications, Synthesize greedy algorithms, and analyze them

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, 3rd Ed., MIT Press, 2009.
• R. Sedgewick and K. Wayne, Algorithms, 4th Ed., Addison Wesley Publishers, 2011.
• N. Karumanchi, Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles, 5th Ed. 2016.
• S. S. Skiena, The Algorithm Design Manual, 2nd Ed., Springer, 2011.

• Discuss the concepts of professional practice in computing
• Explain the context in which computer professionals work
• Analyse legal issues in relation to data privacy and software use

• Computer ethics and professional practice: Ethical argumentation and theories, Moral assumptions and values, the role of computing professional, and Professional Communication practices
• Intellectual Property: Intellectual property rights, Intangible digital intellectual property, Legal foundations for intellectual property protection
• Privacy and data protection: Privacy of Computer data, respecting human dignity, protecting data stored on computers
• Security Policies, Laws and Computer Crimes: Computer crimes and legal redress for computer criminals, identity theft and recovery, Issues surrounding the misuse of access and breaches in security, Crime prevention strategies

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• George W. Reynolds , Ethics in Information Technology, 5th Edition, 2014.
• Penny Duquenoy, Simon Jones, Barry G. Blundell: Ethical, Legal and Professional Issues in Computing, 2008

• Apply software engineering principles and practices for the planning and the development of a software product
• Practice as an effective player of a software project team
• Create professional-quality deliverables
• Demonstrate abilities to manage pressures and procedures of a team work in an industrial setup

• This course unit introduces and applies a range of topics in software engineering in the context of a team project
• Students will be assigned to a group of three to four members and each group works to specify, design, implement, and document a software project
• The course unit is oriented around directed and self-paced learning, supported by weekly mentoring and discussions

• Group Project Report —————– 40%
• Software product ———————- 30%
• Project Presentation —————— 10%
• Individual viva voce —————— 20%

• Explain the importance of human resource management in IT industry
• Discuss how to strategically plan for the human resources needed to meet organizational goals and objectives with the support of Information Technology
• Explain how legislation impacts human resource management practice
• Describe importance of human resource information system and its applications
• Summarize the activities involved in evaluating and managing employee performance

Evolution of Human Management, Definitions of Human Resource Management, Human Resource Management and Personnel Management Approaches, Equal Employment opportunity and Laws, Managing Diversity, Human Resource Audit, Human Resource information system, Job Analysis, Human Resource Planning, Recruitment and selection(e recruitment), Training and Development, Performance Appraisal.

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Gray Dessler, Introduction to Human Resource Management 13^th edition, 2011

• Explain the concepts of number representation and error analysis in digital computers
• Apply numerical methods to solve systems of linear, nonlinear and differential equations
• Use Curve fitting, Interpolation and Polynomial approximation techniques

• Numerical Representation and Error Analysis: floating-point arithmetic, Precision and Accuracy, rounding rules, Absolute and Relative error, and computational error analysis
• Solving Systems of Linear Equations: Eigen values, Eigen vector, vector and matrix norms, LU decomposition, Gaussian elimination, Pivoting, Jacobi method, Gauss-Seidel method
• Solving Systems of Nonlinear Equations: Bisection, false-position, incremental searches, Fixed-point iteration, Newton-Raphson, Secant method
• Curve fitting, Interpolation and Polynomial Approximation: Lagrange interpolating polynomials, Newton interpolating polynomials, cubic and natural spline interpolation, convergence and error bound for polynomial interpolation of a continuous function
• Numerical Integration and Differentiation: Newton-Cotes quadrature, Trapezoidal rule, Simpson’s rule, Romberg integration, and numerical differentiation

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• K. Sankara Rao: Numerical Methods for Scientists and Engineers, 2008
• George W. Collins: Fundamental Numerical Methods and Data Analysis, 2003.

• State various types of threats and attacks, and countermeasures
• Describe various measures to avoid vulnerabilities in programs
• Analyse network and platform to detect threats
• Perform basic digital forensics

• Threats and Attacks: Types of Attacks, and Attacker goals, capabilities and motivations.
• Defensive Programming: Input validation and data sanitization, Handling of exceptions and unexpected behaviours
• Network specific threats and attacks: Denial of Service, spoofing, sniffing and traffic redirection, man-in-the-middle, message integrity attacks, routing attacks, traffic analysis, architectures for secure networks, and defense mechanisms and countermeasures
• Platform Security: Code integrity and code signing, Secure booting, TPM and secure coprocessors, Attestation, and Security of embedded devices
• Digital forensics: Principles and methodologies, attack detection and investigation

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• William Stallings, Cryptography and Network Security: Principles and Practice, 6th edition, Pearson, 2013.
• Michael Jang, Security Strategies In Linux Platforms And Applications (Information Systems Security & Assurance), 1st edition, Jones & Bartlett Learning, 2010.
• Charles P. Pfleeger and Shari Lawrence Pfleeger, Analyzing Computer Security: A Threat / Vulnerability / Countermeasure Approach, 1st Edition, Prentice Hall, 2011

• Design websites using advanced features of Markup and Client-side scripting languages
• Use XML technologies for web applications
• Develop secure web-based systems using server-side scripting languages

• Advanced use of scripting languages: Client-side scripting (HTML, CSS and JavaScript) and Server-side scripting (PHP/JSP/ASP)
• XML Technologies: XSL, XSLT, xPath and xQuery
• Secure web programming: Authentication, Access Control, Session Management, SQL Injections and Cross Site Scripting (XSS)
• Trends in Web development: Web 2.0, AJAX, JSON, Web Services
• Best practices in Web Development: Architectural patterns, Search Engine Optimization (SEO), Frameworks, Auditing and Logging

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Semmy Purewal, Learning Web App development, First edition, 2014.
• Dafydd Stuttard and Marcus Pinto, The Web Application Hacker’s Handbook: Finding and Exploiting Security Flaws, Second edition, 2011.

• Explain the fundamental principles of multimedia
• Demonstrate compression techniques used in multimedia
• Discuss theories behind the multimedia components

• Introduction: Uses of Multimedia, Interaction Technologies, Multimedia Hardware and Devices
• Compression Techniques in Multimedia: Compression Basics, Lossless and lossy compression techniques
• Text in Multimedia: Visual Representation of Text, Digital representation of characters
• Fundamentals of Colours, Colour Models and Dithering
• Fundamentals of Images: Characteristics of Images, Image file formats, and Image compression standards
• Digital Audio: Sound Processing, Representation of Audio files
• Fundamentals of Video and Animation: Analogue and Digital Video Standards, Video Processing, Video compression standards and file formats, Basics of animation
• Designing Multimedia: Development Phases, Multimedia Authoring and Tools, Multimedia in Internet and World Wide Web.

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• Ze-Nian Li and Mark S. Drew, “Fundamentals of Multimedia”, Second Edition, 2014
• Ashok Banerji; Ananda Mohan Ghosh, “Multimedia technologies”, 2010
• T.M. Savage and K.E. Vogel, “An Introduction to Digital Multimedia”, Second Edition, 2013.

• Appraise concepts, methods and tools used in Bioinformatics
• Demonstrate the fundamental computational knowledge required to tackle problems in bioinformatics
• Develop computational applications in bioinformatics using computer algorithms

• Introduction to Bioinformatics: aims and tasks of Bioinformatics, history and scope of bioinformatics and its applications, bioinformatics databases.
• Structural Bioinformatics: protein structure basics, protein structure visualization, comparison and classification, protein secondary structure prediction, protein tertiary structure prediction, RNA structure prediction.
• Sequence Alignment: pairwise sequence alignment, multiple sequence alignment, Hidden Markov Models.
• Pattern Recognition: Clustering and Visualisation
• Genomics and Proteomics: genome mapping, genome assembly, genome comparison, functional genomics, proteomics and metabolomics

• In-course Assessments —————–30%
• End-of-course Examination ————70%

• B. Bergeron, Bioinformatics Computing, Prentice Hall, 2002.
• Krawetz. Stephen A: Introduction to Bioinformatics: A Theoretical and Practical Approach, 2003
• F. Azuaje and J. Dopazo, Data Analysis and Visualization in Genomics and Proteomics, John Wiley, 2005

• To introduce computer architecture focusing on microprocessor design, including CPU and memory.

• Introduction to Computer Systems: Historical Background, Architectural Development and Styles, Technological Development, Performance Measures.
• Instruction Set Architecture and Design: Memory Locations and Operations, Addressing Modes, Instruction Types.
• Assembly Language Programming: Instructions Mnemonics and Syntax, Assembler Directives and Commands , Assembly and Execution of Programs.
• Processing Unit Design: CPU Basics, Register Set, Datapath, CPU Instruction Cycle, Control Unit.
• Memory System Design: Basic Concepts, Cache Memory, Main Memory, Virtual Memory, Read-Only Memory .
• Input– Output Design and Organization: Basic Concepts, Programmed I/O, Interrupt-Driven I/O, Direct Memory Access (DMA), Buses, Input–Output Interfaces.
• Pipelining Design Techniques: General Concepts ,Instruction Pipeline, Example Pipeline Processors, Instruction-Level Parallelism .
• Reduced Instruction Set Computers (RISCs): RISC/CISC Evolution Cycle RISCs Design Principles, Overlapped Register Windows, RISCs Versus CISCs.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To present the fundamental issues in the design and use of major programming languages.
• To present implementation concepts and designs.
• To prepare the students for further study of programming languages, help develop an appreciation of a programming language as a tool for software construction.
• To enable the students to evaluate and choose a language to match the problem.

• Design and evaluation criteria for programming languages: (Procedural, structured, functional, object oriented and scripts).
• Introduction to syntactic and semantic description of programming languages.
• Basics of programming language implementation.
• A comparison of design choices across programming paradigms: data objects, data types, control structures, sub-programs.
• Basics of concurrency.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To understand the principles and practices of cryptographic techniques.
• To understand a variety of generic security threats and vulnerabilities, and identify and analyse particular security problems for a given application.
• To understand the design of security protocols and mechanisms for the provision of security services needed for secure networked applications.

• General introduction to security.
• Cryptographic techniques: classical cryptography, conventional cryptography such as DES and AES, public-key cryptography such as RSA, and digital signatures such as DSA.
• Security services: key management, agreement and distribution, Public Key Infrastructure (PKI), authentication, authorisation and access control services.
• Network security applications: IP security, Web security, Secure Electronic Transaction (SET), Electronic mail security, firewalls and Virtual Private Networks (VPNs).
• Distributed System security.
• Mobile system & E-commerce securities: 3G security, e-payment systems, fair data exchange.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To familiarise with the latest developments in e-technologies and modern network technologies which are used in e-business, e-banking and e-learning.

• Introduction: Web designing with HTML, CSS, CSS2, Introduction to XML, XSL, XSQL, DTD, WAP, WML and their use in experience, Web transfer of audio, video and text.
• Electronic business: Introduction to electronic business, trends in electronic business, Types of electronic business, models of electronic business, Security of electronic business, electronic signature, Examples of electronic business.
• Computer communication and presentation: Use and programming of PHP scripts (Perl, ASP), Connection of Scripts and databases, Using of application Flash for making web pages.
• Marketing: Marketing, promotion and publicity on Internet, Electronic enterprise, Competence in e-business and marketing.
• Electronic banking: Introduction to electronic banking, Information systems of e-banking, Electronic services of e-banking and security.
• Electronic learning: Introduction to electronic learning and distance studying, Technologies, trends, practices of e-learning.

• In-Course Assessments : Two In-Course Assessments, An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation (30%)
• End of Course examination : A written examination of two hours duration (Expected to answer three out of four questions) (70%)

• To understand basics of algorithms, concepts of time and space complexity in worst, average and best cases, and the asymptotic behaviour and order of magnitude of functions.
• To be able to develop efficient algorithms for simple computational tasks and to compute complexity measures of algorithms, including recursive algorithms using recurrence relations.
• To know and apply a range of algorithm design techniques.
• To know and understand a wide range of searching and sorting techniques and to implement them
• To be familiar with several data structures and to get to know their constructions and uses.

• General concepts : Introduction to Algorithms and Mathematical background – Time and space Complexity, Asymptotic Analysis; Average, best and worst case analysis, recurrence relations and their use in algorithm analysis
• Data structures: Introduction and Implementation of Stacks and Queues, Linked Lists, Hash Tables, and Trees, and their applications
• Algorithm design techniques: Greedy methods, Divide and conquer methods, Dynamic programming, Recursion, Influence of data structure on algorithm performance
• Graphs and digraphs: Representations, Breadth and depth first searches, Shortest path, Minimal spanning tree, Hamiltonian path and travelling salesperson problems etc.
• Sorting: selection, insertion, bubble sort. Quicksort, mergesort, heapsort , Bucket sorting, Worst case and average behaviour, Order statistics

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Two Short exams based on tutorials (10-15 minutes each) ————– 10%
  • Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To give principles and practical solutions for storage and retrieval of information.
• To emphasis both on the use and on the implementation of database management systems, enabling students in both logical and physical database design and complex query execution using standard query languages.
• To introduce the modern databases for multimedia and spatial requirements.
• To familiarize with a broad range of data management issues including data integrity, security, transaction and concurrency.

• Introduction to Database: Evolution of Database technology, Data models, three level schema architecture, Database languages, Physical file structures and Indexing techniques.
• Database design: ER model, Relational Model, Normalisation – First, Second, Third and Boyce-Codd normal forms.
• Relational algebra and calculus: Query languages, Relational algebra operations, relational algebra query expressions, tuple calculus and domain calculus query expressions.
• Introduction to SQL: Basic structure, Sub queries, Set operations, Aggregate functions, Derived relations, Views.
• Advanced Features: Transaction, concurrency control and recovery; Security levels, Integrity constraints,
• Modern databases: Object Oriented Databases and Relational object Database design, Persistence Objects, Query languages.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Two Short exams based on tutorials (10-15 minutes each) ————– 10%
  • Two other exams (20 minutes each) ————————————- 20%
• End-of-course Examination ————————————————– 60%

• To offer a foundation in programming skills for popular languages and platforms.
• To systematically address theories and methods of typical software engineering processes and models governed by industry standards.
• To emphasize the development of reliable and maintainable software via system requirements and specifications.
• To enable students capable in software design methodologies including object-oriented design implementation, integration, and testing.

• Introduction to Software Engineering: Software characteristics, Software applications, software process models.
• Project Management: Project planning, risk management, human interactions in software development.
• Analysis principles: Analysis modelling, design methods, software testing methods
• Computer Aided Software Engineering: Taxonomy of CASE tools, application of case tools, integrated case environment.
• Client-Server Software Engineering: Structure and usage of client-server systems, distribution of software component.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Mini project (implementation + presentation) ————————— 30%
• End-of-course Examination ————————————————– 60%

• To enable to implement several data structures and algorithms
• To enable to be familiar with designing and creation of databases and to manipulate them using query languages.
• To enable to efficiently find solutions to numerical and non-numerical problems.
• To enable to be familiar with programming in logic.

• 90 Hours practical works scheduled by the Department.

• Attendance at practical sessions ——————————————— 10%
• Continuous assessment on practical records ——————————– 20%
• Two mid-semester exams each of 30-45 minutes duration —————- 20%
• Two end semester examinations each of two hours duration ————- 50%

• To enable the students to gain basic knowledge in numerical methods.
• To learn a programming language and tools suitable for numerical computing.
• To develop their programming skill in solving a variety of numerical problems.

• Data Representation and Computational Error: floating-point numbers, rounding rules, machine precision, floating-point arithmetic; well-posed problems, problems vs. algorithms, data error vs. computational error, forward error vs. backward error, conditioning of a problem, stability of an algorithm.
• System of Linear Equations: vector spaces, matrices and other relevant matters; vector norm, matrix norm, condition number of a matrix; LU factorization, Gauss-Seidel elimination, Error estimation; refinement of special cases.
• System of Non Linear Equations: Simple vs. multiple root; bisection, fixed-point iteration, Newton-Raphson method, secant method; fixed-point iteration, Jacobian matrix, Newton’s method; convergence rate and order analysis.
• Interpolation and Polynomial Approximation: Lagrange and Newton forms, Hermite interpolation, cubic and natural spline interpolation; convergence and error bound for polynomial interpolation of a continuous function; Chebyshev polynomials; Weierstass Approximation Theorem, best uniform approximation, least squares approximation.
• Numerical Integration and Differentiation: Newton-Cotes quadrature, Trapezoid rule, Simpson’s rule; Romberg integration.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Mini project (implementation + presentation) ————————— 30%
• End-of-course Examination ————————————————– 60%

• To explain the fundamentals of simple predicate logic and its use for knowledge representation and deduction.
• To understand the fundamentals of logic programming, in particular the sources of non-determinism, the significance of displays of computational spaces and the significance of language semantics.
• To demonstrate familiarity with the language Prolog and its practical use.

• Formal Logic: revision of Propositional and First Order Predicate Logic, conjunctive normal form, clausal form, knowledge representation by clausal form.
• Logic Programming: basic concepts, the resolution rule of inference, application strategies, pattern matching, backtracking, computational space – total and standard, non-determinism, procedural and declarative semantics.
• Prolog: Horn Clauses, terms and terminology, built-in predicates, non-determinism and input/output, the cut, negation, list processing, abstract data types.
• Applications: Realising the suitability of Logic Programming in solving problems, Identifying problems that can be solved by Logic Programming, Conversion of problems in to those suitable for Logic programming, Implement solving methods to find solutions.

• In-course Assessments
  • Attendance at lectures and submission of tutorials ——————— 10%
  • Mini project (implementation + presentation) ————————— 30%
• End-of-course Examination ————————————————– 60%