Bachelor of Computer Science

(a) Description

The course is to create a strong foundation in the principles and practice of modern computer programming. It should give an in depth understanding of structured and Object Oriented programming. It is to cater for both Structured and Object Oriented programming (O-O) topics like control structures, objects and classes, encapsulation, inheritance, polymorphism, interfaces, and exception handling. A state of the art programming language like Python or Java is to be used. Students are to cover both theoretical principles and hands on practical skills.

(b) Course Objectives

The aims of the course unit are to provide the student with:

• Introductory knowledge about structured programming
• Knowledge and techniques of evaluating syntactic and semantic correctness of a computer program
• Knowledge in planning, organization, and development of complete computer applications
• Strong practical basis in object-oriented style of programming

(c) Learning outcomes

Students who successfully complete this course of study will be able to:

• Understand the basic terminology used in computer programming
• Write, compile and debug programs in a structured and O-O manner (like Python)
• Explain the principles of the object oriented programming paradigm specifically including encapsulation, inheritance and polymorphism.
• Design programs involving decision structures, loops and functions
• Design, develop, test, and debug programs using object oriented principles in conjuncture with an integrated development environment
• Describe and explain the factors that contribute to a good object oriented solution, reflecting on your own experiences and drawing upon accepted good practices

(d) Detailed Course Content

• Module 1 (16 hours): Structured Programming
  1. Program Structure
  2. Branching and Iterations: Control flow statements
• Module 2 (16 hours): Data manipulation
1. Functions
2. Strings, Pointers and arrays
• Module 3 (28 hours): Object-Oriented Concepts
  1. Object-Oriented programming: Classes and objects
  2. Inheritance and visibility modifiers
  3. Interfaces and abstract classes
  4. Exception handling

(e) Assessment method

The assessment will be done by tests and take home practical assignments (40%), practical examination (30%) and written examination (30%)

(f) Reading List

1. Brian W. Kernighan, Dennis M. Ritchie: C Programming Language; Prentice Hall, 2000.
2. Patrick Naughton & Herbert Schildt: The Complete Reference – Java 2, Fifth Edition.
3. Problem Solving with Algorithms and Data Structures, Release 3.0 , by Brad Miller, David Ranum, 2013

(a) Description

This course introduces the logical architecture and organization of computer systems. It highlights the lower end operations in a typical computer as well as the way computers manage their resources during operation. The course opens up a student to be an informed user of the computer rather than a passive recipient of the computer services.

(b) Course Objectives

The aims of the course unit are to:

• To provide students with a fundamental understanding of the functional components of a computer system, and how they are organized.
• To highlight to students the way computers process and store the data.
• To highlight internal management issues in computer systems.

(c) Learning outcomes

By the end of the course unit, the student should be able to:

• Explain the difference between Computer Organisation and Computer Architecture.
• Discuss how information is processed in the computer from the time it is input to the time of output.
• Explain how the Micro processers operate.
• Explain the .difference between the various representation codes i.e. ASCII and EBCDIC.
• Understand the organisation of a computer system in terms of its main components
• Understand the operation of electronic logic elements
• Understand different processor architectures
• Understand input/output mechanisms
• Understand the various parts of a system memory hierarchy

(d) Teaching and learning pattern

Teaching will be in terms of lectures as well as tutorials.

(e) Detailed Course Content

• Module 1: Introduction to computers and Data Representation: (12 CH) Understanding of Organisation & Architecture, Function & structure of computer Components, Technology Trends, Instruction set Architectures, Measuring CPU performance.Integer Formats, Binary, Octal and Hexadecimal Systems, Negative integers and 2s Complement, Floating Point Formats, BCD Formats, Alphanumeric. Hardware Codes.
• Module 2: Digital and Sequential circuits (24 CH) Basic Digital Circuits: Logic gates, Karnaugh maps, Combinatorial Circuits, Binary Adders, Multiplexers and Demultiplexers, Comparators, Decoders and Encoders, Code Converters, ROMS and PLAs, Sequential Circuits, Flip Flops and Latches, R-S flip flops, J-K flip flops, T flip Flops, D flip flops, Registers, Shift Registers and Data Transmission, Sequential Network Design.
• Module 3: Assembly Level Machine Organization (12 CH) Basic Organisation of the Von Neumann machine, Control Unit, Instruction fetch, decode & execution, Instruction sets and types (Data manipulation, control, I/o), Assembly/machine language programming, instruction formats,Register Organisation and Addressing modes, sub-routine call and return mechanisms, I/O & interrupts
• Module 4: Micro Computer Architecture:(12 CH) Architecture of the CPU, Memory(cache,internal and external) and memory management, I/O Devices and Interfaces, System Bus, Examples of CPU Structures, Machine Language Instructions, Instruction Formats and Addressing Modes. The Processing Elements: Macroinstruction execution, Internal Bus Transfers, Detailed Internal Architecture, Microcontrol: Hardwired Control, Microprogrammed Control, Reduced Instruction Set Computers.

(f) Assessment method

Assessment will be in form of tests and assignments (40%) and final examination (60%)

(g) Reading List

1. William Stallings, Pearson Education. Computer Organization and Architecture (Designing for Performance), 09th edition, (2012).
2. Glenn A. Gibson. Computer Systems; Concepts and Design:

(a)Course Description

The course gives a foundation and deep understanding of mathematical skills that are necessary in studying advanced aspects of computer science. The course extends the mathematics that was taught in high school but this time focusing on areas that will help the candidates conceptualise computer science knowledge like in image processing, machine learning, graphics, interface design , robotics and security

(b) Course Objectives

The objectives of the course are to equip students with skills on:

• Finding limits for functions
• Establishing continuity of functions
• Evaluating finite and infinite sequences and progressions
• Performing operations of Matrix algebra
• Analysing and interpreting vector spaces
• Performing plane arithmetic

(c) Learning outcomes

By the end of the course unit, the student should be able to:

• Compute limits of polynomial, logarithmic, exponential, trigonometrical and mixed functions
• Establish continuity and diferentiability of functions
• Calculate inverse, determinant and transpose of matrices
• Perform 2D and 3D transformations using matrices
• Perform Vector and vector space algebra
• Generate mathematical characteristics in regard to vectors and planes

(d) Mode of delivery

The study mode will be a blend of learner centered delivery via online or face to face classes. The teacher will introduce the topic and guide students on where further reading can be done. Student centered activities like discussion forum, will be employed to supplement teachers work.

(e) Detailed Course Content

• Module 1: Limits and Continuity (10 CH) This module addresses calculation of limits of simple functions as well as fairly complex functions (like composite polynomials and exponential functions). It involves the , σ, calculation of limits of functions using limit laws (eg Squeeze theorem, L’opitals rule, etc). It also involves establishment of limits to infinity and horizontal asymptotes. It also covers differentiability of functions using continuity tests as well as generating derivatives of functions, applying chain rules and application of derivatives into real life applications like rates of change.
• Module 2: Progressions and Sequences (10CH) This module covers computation of standard progressions (AP and GP), special progressions and sequences as well as computing their limits and Mathematical upper / lower bounds (where they exist). Techniques like breaking sequences into separate progressions will be emphasized.
• Module 3: Matrix Algebra (10 CH) This module will address the fundamental skills in matrix algebra (matrix algebra rules, transposes, inverses, determinants, egine vectors and kernels) as well as how matrices are used in mathematical operations like transformations in vector spaces and solving of equations.
• Module 4: Vectors and Vector Spaces (15 CH) This module will cover computations in vectors and vector spaces, vector operations, linear combination of vectors, spanning sets , linear dependence and Independence and orthogonality.
• Module: 5 Vectors and Planes (15 CH) This module will cater for the analytical interpretation of vectors and planes. This will include vector equations of lines in n- space, equations of planes, as well as characteristics between planes and vectors like angle between them, distance between them, etc

(f) Mode of Assesment

1. Each module will be assessed by at least one assignment and one test. The composite scores shall constitute progressive assessment (40%)
2. There shall be a final exam at the end of the semester that shall constitute 60% of the final core

(g) Reading List

1. Robert A. Beezer (2015) A First Course in Linear Algebra http://linear.ups.edu/html/fcla.html
2. Crowell and Slesnick(2008) Calculus with Analytic Geometry

(a)Course Description

Computer Science is central to digital innovations that solve some of the world’s most challenging problems. This course aims to introduce students to the world of computational thinking and its application to problem solving, process of digital solution development, innovation ecosystem and entrepreneurship.

(b) Course Objectives

The objectives of this course are to

1. equip students with core computational and problem solving skills in computer science including including logical, algorithmic, and abstraction.
2. develop knowledge and understanding of digital innovation and entrepreneurship processes and systems
3. development of business models for digital innovations
4. skills to present complex computational innovations in an easy to understand format

(c) Learning outcomes

By the end of this course students should:

1. demonstrate capability of computational and analytical thinking and problem solving as applied to real world problems
2. have knowledge and understanding of digital innovation and entrepreneurship processes and systems
3. be able to use problem-solving strategies to develop computer science solutions to problems.
4. be able to develop business models for digital innovations and have acquired skills to present complex computational innovations in an easy to understand format.

(d) Mode of delivery

The teaching pattern is by blended learning including lectures, tutorials, practical lab work, self-study, case studies, use of e-learning management system, group discussion and class presentations

(e) Detailed Course Content

• Module 1: Computational thinking (06 Hours) Introduction to what is computational thinking, logical and algorithmic thinking. Deductive and inductive reasoning, Boolean logic and its importance to computation thinking.
• Module 2: Problem solving and decomposition (15 Hours): Problem definition, decomposition, abstraction, patterns and generalisation. Abstraction and modelling. Anticipating and dealing with errors. Evaluation of a solution for correctness, efficiency elegance and usability.
• Module 3: Application of computational thinking to software development (09 Hours) Computational thinking in software development using a programming language like Python. Creating, finding and using abstractions and patterns in programming, testing and evaluating programs, forming generalisations and abstractions,
• Module 4: Digital Innovation thinking (15 Hours) principles, processes and sources of innovative opportunities. Entrepreneurial thinking, processes and strategies. Innovation management, communication and pitching

(f) Mode of Assesment

Course work (40%)and exam (60%) will include review of real-world challenges and translating them into solutions and business models, review of technology articles and pieces, quizzes, mini projects, and final project.

(g) Reading List

1. Karl Beecher Computational Thinking: A beginner’s guide to problemsolving and programming, BCS Learning and Development Ltd, 2017.
2. David Riley and Kenny A. Hunt Computational Thinking for the Modern Problem Solver, Chapman and Hall/CRC, 2014.
3. Paolo Ferragina and Fabrizio Luccio Computational Thinking: First Algortihms, Then Code, Springer, 2018.
4. Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data Second Edition. MIT Press, 2016.
5. Bessant John, and Tidd Joseph. Innovation and entrepreneurship. Wiley & Sons, 2015.
6. Osterwalder A., andPigneur Y.. Business model generation: A handbook for visionaries, game changers, and challengers. Hoboken, NJ: Wiley. ISBN13: 978-0-470-87641-1, 2010.
7. Wise, S. E. Hot or not: How to know if your business idea will fly or fail?. Toronto, ON: Ryerson Entrepreneur Institute. ISBN-13: 978-1-46802-449-4, 2011.

(a)Course Description

In this course, students are to learn about the basic organization, concepts and terminologies in a computerized environment. They are also to get an in depth understanding of common computer applications. The use of related applications in different operating systems will be explored.

(b) Course Objectives

The objectives of this course are to

1. Equip students with basic knowledge about computer organization
2. Equip students with skills of using common office applications;
3. Expose students to different operating systems;
4. Equip students with skills of how to use the Internet;
5. Equip students with knowledge about common text editors in different operating systems

(c) Learning outcomes

On completion of this course unit, the students will be able to:

• Describe the different parts of a computer
• Describe the historical evolution of computers
• Competently use the common office applications in at least two operating systems and
• Competently know how to use the Internet
• Competently use common text editors in at least two operating systems

(d) Detailed Course Content

• General computer organization (12 Hours)
• Historical perspectives of computing (8 Hours)
• Common Microsoft office packages (12 Hours)
• Office packages in other operating systems (8 Hours)
• Text editors (4 Hours)
• Common Command Line commands (Linux, Dos) (8 Hours)
• Using the web (4 Hours)
• Understanding the Internet (4 Hours)

(e) Mode of Delivery

Blended learning including:

• Lectures
• Practical sessions in the computer laboratories
• Online learning management systems
• Class discussions and presentations
• Problem-based/case studies
• Project-based assignments that can be done in groups

(f) Mode of Assesment

The assessment will be in form of:

• Tests 20%
• Individual assignment 20%
• Final written exam 60%

(g) Reading List

1. Preston, J. Preston, S. & Ferrett, R. Computer Literacy: Computing Fundamentals, 2nd Edition. Prentice Hall Press., 2013.
2. Williams, B.K. & Sawyer, S.C., Using Information Technology; A practical introduction to computers & Communications. Richard D Irwin, 2007
3. Snyder, L. 2018. Fluency with information technology: Skills, Concepts, and Capabilities, 4th Edition. NY, NY : Pearson, 2018

(a) Description

The course gives students a firm foundation of data structures and algorithms. The course trains students on systematic development and analysis of algorithms. The importance of algorithm complexity on computer performance is emphasized. Typical computational problems and their solutions/analysis are to be covered.

(b) Course Objectives

The objectives of this course are to educate the students in:

• How to use various data structures while developing in a particular programming language as well as how to implement some of the most common algorithms used with such data structures.
• Define and explain advanced data types such as stacks, queues, lists, trees and graphs; write programs using them.
• Define, discuss and explain the main algorithms and techniques (such as sorting, searching, hashing, traversal and recursion) and write programs using these algorithms.

(c) Learning outcomes

Students who successfully complete this course of study will be able to:

• Appreciate the role of data structures and algorithms in computer programs.
• Describe a step by step analysis and design of computer algorithms.
• Analyze generic algorithmic problems and apply them to computational scenarios

(d) Detailed Course Content

Module 1: Elementary data structures (6 CH) the need for data structures; the role of algorithms in computing, linear and non-linear structures; Abstract Data Types.

Module 2: Recursive data structures (14 CH) recursion; lists, stacks, queues, dequeues, linked lists and their implementation; Trees: binary trees, balanced trees

Module 3: Sorting Algorithms (12 CH) Simple: bubble sort, insertion sort, selection sort; Fast: Mergesort, Heap sort, Quick sort

Module 4: Storing and Searching (8 CH) hash tables, binary search trees, AVL trees

Module 5: Graph traversals (8 CH) Breadth-First Search and DepthFirst Search; shortest path, spanning trees

Module 6: Algorithm analysis (8 CH) Asymptotic analysis; Big-O, Omega and Theta notations, orders of growth, worst case, average case, best case and amortized analysis

Module 7: Algorithm design (4 CH) Dynamic algorithms, Divideand-conquer algorithms, Greedy algorithms.

(e) Mode of Delivery

The teaching pattern is by blended learning including lectures and tutorials, practical lab work, group discussions and class presentations.

(f) Mode of Assesment

The assessment will be done by tests and assignments (40%) and final examination (60%)

(g) Reading List

1. Mark Allen Weiss Data Structures and Algorithm Analysis in C++, 4th Edition, 2014
2. Michael T. Goodrich and Roberto Tamassia and Michael H. Goldwasser. Data Structures and Algorithms in Python, 2013.
3. Alfred V. Aho and John E. Hopcroft and Jeffrey D. Ullman. Data Structures and Algorithms. Addison-Wesley, 1983
4. Thomas H. Cormen and Charles E. Leiserson and Ronald L. Rivest and Clifford Stein. Introduction to Algorithms, Second Edition, 2001.

(a) Course Description

This course strengthens the student’s command of probability and statistics. It is meant to lay ground for the application of probability and statistics concepts in the general field of Computer Science. The course prepares candidates to understand the concept and rules of probability as well as get an in depth understanding and manipulation of statistical distributions (Discrete and Continuous). The course also prepares students to use and apply descriptive statistics and statistical inference to answer questions about real-world data. Students will Students will learn how to use statistical computing packages available R and Python such as Numpy, which are popular in Data Science.

(b) Course Objectives

The objectives of the course are to equip the students with skills to:

1. Generate probabilities of events or sequences of real life events under given conditions
2. Solve probabilistic and statistical problems related to probability distributions
3. Generate representations of distributions real life scenarios and compute related probabilities
4. Solve compounded distributions’ problems
5. Analyse data using descriptive statistics
6. Apply statistical Inference and regression methods to real world examples.

(c) Learning outcomes

By the end of the course, the student should be able to:

1. Solve probability problems using common approaches and tools (probability trees, contingency table, bayes theorem, etc)
2. Formulate and solve mean, variance and instance probability problems for discrete and continuous random variables
3. State distributions and solve distribution based probabilities for common special discrete and continuous random variables
4. Solve problems related to combined distributions including conditional distributions
5. Gained knowledge of descriptive statistics and statistical Inference, and regression methods for data analysis and use of statistical computing packages and libraries.

(d) Detailed Course Content

• Module 1: Introduction to Probabilities and Random Variables (09 CH) Probability of an even, probability experimentation, generation of probabilities of multiple or repeated events using probability trees, generation of probabilities of combined events, conditional probabilities and Bayes Theorem and Independent events.
• Module 2: Discrete Random Variables and Distributions (06 CH) General Discrete Random variables, characteristics, Expectance and Variance of discrete random variables, special discrete random variables: Bernoulli Distribution, Binomial Distribution, Geometric Distribution, Negative Binomial Distribution, Hypergeometric Distribution, Poisson Distribution
• Module 3: Continuous Random Variables and Distributions (06 CH) General Continuous Random Variables, expectance and variance of continuous random variables, special continuous random variables: Uniform Distribution, Gamma Distribution, Beta Distribution, Normal Distribution, Lognormal Distribution, Inverse Gaussian Distribution, Logistic Distribution.
• Module 4: Probability Moments and Chebychevs inequality (06) Moments of Random Variables, Expected Value of Random Variables using moments, Variance of Random Variables using moments, Moments for validation of random variables, Chebychev Inequality, Moment Generating Functions
• Module 5: Bivariate and Conditional Random variables (09 CH) Bivariate Discrete Random Variables, Bivariate Continuous Random Variables, Conditional Distributions, Independence of Random Variables (Covariance)
• Module 6: Introduction to statistics (09 Hours) Population and sample, parameter and statistics, descriptive statistics, graphical statistics.
• Module 7: Statistical Inference and regression (15 Hours) Parameter estimation, confidence, intervals, standard deviation, Bayesian estimation and hypothesis testing. Least squares estimation, analysis of variance, prediction, multivariate regression. Practical projects in R statistics or Python packages such as Numpy.

(e) Mode of Delivery

The study mode shall be blended learning including a combination of learner centered and teacher centered. The teacher shall give overview of the module and also provide examples illustrating the concept. The learner then shall do extra reading in the items in the module as well as doing the extensive module assignment and projects using statistical computing packages available for free in R, Python etc

(f) Method of Assesment

Assesment shall be by:

1. At least one test and at least three assignments (40%)
2. Final examination (60%)

(g) Reading List

1. Prasanna Sahoo Probability and Mathematical Statistical University of Louisville Louisville, KY 40292 USA, 2013: (Springer Textbook) 1st Edition by
2. Yakov G. Sinai and D. Haughton . Probability Theory: An Introductory Course 2012.
3. Introduction to Probability and Statistics for Engineers and Scientists 5th Edition, Academic Press, 2014.
4. Think Stats: Exploratory Data Analysis in Python, Second Edition, O’Reilly Media, 2014.

(a) Course Description

This course aims to give students an experience of developing complete software applications and systems. Focus will be put on practicing programming and program documentations combining all the skills and knowledge acquired in other courses so far.

(b) Course Objectives

The aims of the course unit are to:

• practically give students skills of integrating different concepts of programming into a single application.
• introduce the student to hands on aspects of software development.
• nurture the students ability to independently read different sources of literature so as to identify what (s)he can use to develop his/her application.

(c) Learning outcomes

By the end of the course, the student should be able to:

Apply the skills he/she has acquired to integrate different concepts of programming into a single application;

Apply the skills he/she has acquired to software development

Solve real life problems and challenges

(d) Detailed Course Content

The main content in the course is translation of a real life problem into a working computer program. For each year the course lecturer will provide a theme where students can be challenged to develop a software application that a real life problem. In addition, challenging practical challenges will be sourced from the industry and organisations that students can work on.

(e) Study Materials

The study materials will include examples and case studies of real-world challenges; software development tools and hardware equipment; open sources repositories on Github.

(f) Mode of Delivery

Learning will be largely by self - study/research. Students will be given programming Assignments that will be submitted after a specific period of time. The skills which that will be expected in the assignment will be indicated to the student. A member of staff will meet students at the issue of each of the assignment and address any outstanding issues as well as expectations. A minimum of six assignments will be given. Where necessary a guest speaker or project owner from the industry will be invited to provide additional context on the project or the state-of-the-art industry tools and methods.

(g) Mode of Assesment

Two assignments (each taking two weeks) and three assignments (each taking 3 weeks) will be given. The first two assignments will constitute the coursework (40%) while the last three assignments will constitute the examination mark (60%).

(f) Reading List

1. Students can read any literature like books and online tutorials that can help in addressing the problem in the project at hand. Students can also review available resources on Github and other common software repositories.

(a) Course Description

Operating systems are central to computing activities. There are two primary aims of an operating systems, i.e., to manage resources (e.g. CPU time, memory) and to control users and software. This class introduces the basic facilities provided in modern operating systems. The course divides into three major sections. The first part of the course discusses concurrency: how to manage multiple tasks that execute at the same time and share resources. Topics in this section include processes and threads, context switching, synchronization, scheduling, and deadlock. The second part of the course addresses the problem of memory management; it will cover topics such as linking, dynamic memory allocation, dynamic address translation, virtual memory, and demand paging. The third major part of the course concerns file systems, including topics such as storage devices, disk management and scheduling, directories, protection, and crash recovery. After these three major topics, the class will conclude with a few smaller topics such as virtual machines.

(b) Course Objectives

The aims of the course are:

• To introduce students to concurrency where students will learn how to manage multiple tasks that execute at the same time and share resources
• To introduce students to the problem of memory management
• To introduce students to file systems and issues of storage management.

(c) Learning outcomes

By the end of the course, the student should be able to:

• Explain the role of the operating system as a high level interface to the hardware.
• Use OS as a resource manager that supports multiprogramming
• Explain the implementation of CPU dispatch.
• Explain the implementation of memory management.
• Explain the performance trade-offs inherent in OS implementation

(d) Detailed Course Content

• Module 1: Introduction to Operating Systems (4 CH) Evolution of operating systems, The future of operating systems, Characteristics of current OSes, Categories of operating systems functionalities
• Module 2: Processes, Threads and Dispatching, Concurrent Threads (8 CH) Threads and processes, dispatching, Process creation, Independent and cooperating threads
• Module 3: Locks and Condition Variables (8 CH) Discuss basic lock operations, condition variables and monitors
• Module 4: CPU Scheduling (4 CH) Discuss simple scheduling alogorithms: FCFS, STCF. Priority-based scheduling, multiprocessor scheduling: processor affinity, gang scheduling.
• Module 5: Implementing Locks and Deadlocks (4 CH) Introduction to deadlocks and lock implementation
• Module 6: Linkers and Dynamic Linking (4 CH) Introduction to linkers and dynamic linking
• Module 7: Dynamic Storage Management (4 CH) Stack allocation, heap allocation, storage reclamation.
• Module 8: Memory Management, Virtual Memory, Flash Memory (8 CH) Dynamic memory relocation, base and bound relocation, multiple segments, paging, types of flash memory
• Module 9: Storage Devices (4 CH) Magnetic disk (hard disk), Reel-to-Reel magnetic tape, communicating with I/O devices: device registers, programmed I/O, interrupts, direct memory access.
• Module 10: File Systems (8 CH) File access patterns, file descriptors, Block cache, free space management, block sizes, disk scheduling.
• Module 11: Virtual Machines (4 CH) Introduction to virtual machines, history and usage of virtual machines, implementing virtual machine monitors

(e) Mode of Delivery

The teaching pattern is by blended learning including lectures, tutorials, practical lab work, group discussion and class presentations.

(f) Mode of Assesment

The assessment will be done by mini-projects, tests and assignments (40%) and final examination (60%)

(g) Reading List

1. Operating systems: Principles and Practice, Anderson, Thomas and Dahlin, Michael, 2nd Edition, Recursive books, 2014
2. Operating System Concepts Silberschatz, A., Galvin P.B. and Gagne, G., 9th Ed, Wiley Publishing, 2012

(a) Description

This course presents the processes, methods, techniques and tools that organizations use to build new information systems for their businesses, with particular focus on how computer-based technologies can most effectively contribute to the way business is organized. The course presents a systematic methodology for analyzing a business problem or opportunity, determining what role, if any, computer based technologies can play in addressing the business need. It articulates business requirements for the technology solution, specifies alternative approaches to acquiring the technology capabilities needed to address business requirements, and specifying the requirements for the information systems solution in particular. In- house development, development from thirdparty providers, or purchased commercial-off-the-shelf (COTS) packages are presented.

(b) Course Objectives

Introduce students to the different contemporary approaches used for the analysis and design of information systems

• Equip students with the skills for initiating, specifying, and prioritizing information systems and Information Technology projects and for determining various levels of feasibility for these projects
• Equip students with the different techniques commonly used to collect, analyze and document business requirements, in order to cause productive change in a way a business operates
• Equip students with the different methodologies for designing and communicating a business solution to a problem using formal techniques, and contemporary case tools in process and data modeling
• Equip students with the skills needed to manage information systems projects using formal project management methods
• Introduce students to various systems acquisition alternatives, including packaged systems, and outsourced design and development resources.

(c) Learning outcomes

By the end of the course, the student should be able to:

• Explain the different approaches used for the analysis and design of information systems
• Demonstrate understanding of the initiation, specification and prioritization of information systems and Information Technology projects and there different levels of feasibility
• Use contemporary techniques to collect, analyze and document business requirements, in order to cause productive change in a way a business operates
• Use contemporary techniques for designing and communicating a business solution to a problem using formal techniques, and contemporary case tools for data modeling
• Demonstrate understanding of the managing information systems projects using formal project management methods.
• Explain the various systems acquisition alternatives, including packaged systems, and outsourced design and development resources

(d) Detailed Course Content

• Introduction to Systems Analysis and Design - 12 hours
  • The different Analysis & Design Philosophies and Approaches for Information Systems: structured SDLC, UML, agile methods, soft systems methodology
  • Managing Systems Projects.
  • Configuration and Change Management
• Planning IT based projects - 4 hours
  • Analysis of project feasibility
  • Identification of opportunities for IT-enabled organizational change
• Analysis and Specification of System Requirements - 12 hours
  • Analyzing of business requirements
  • Requirements Gathering
  • Requirements Modelling
  • Data and Process Modelling
• High-level System Design of Information systems - 12 hours
  • User Interface Design
  • Data Design
  • System Architecture
• System Deployment and Implementation - 8 hours
• System Verification and Validation - 8 hours
• Managing Systems Support and Security - 4 hours

(e) Mode of Delivery Blended learning including

• Lectures
• Practicals using CASE Tools
• Online learning management systems
• Class discussions and presentations
• Group project-based assignments

(f) Mode of Assesment

Assessment will be by continuous assessment through practical exercises and Coursework (40%) and final exam (60%)

(g) Reading List

1. Systems analysis and design Shelly, Gary B and Rosenblatt, Harry J, Cengage Learning, 2011
2. Knowledge management: organizing knowledge based enterprises Hawryszkiewycz, Igor Macmillan International Higher Education, 2009
3. Information systems analysis and design Wang, Shouhong and Wang, Hai Universal-Publishers, 2012

(a) Course Description

This is a practical course designed to equip students with essential practical skills in a selected Computer Science area. The courses builds upon the foundation knowledge gained in the first and second semesters. The exact area of focus will be determined by the Course Lecturer. The students are to be supervised by staff. The practical sessions can be run in collaboration with the industry or government organisations dependent on the topic. For example, for teaching of student to integrate mobile money payment services in software systems can be done in collaboration with the telecom companies.

(b) Course Objectives

The aims of the course are to provide the student with:

• ability to study a real word challenge and formulate a relevant digital solution
• Knowledge and techniques on how to implement a working hardware orand software system
• Knowledge in project planning and development, team management, organization as applied to a complete system development.
• Skills to document and present a digital product to potential users.

(c) Learning Outcomes

Students who successfully complete this course of study will be able to:

• understanding of the process involved in the translation of a real world problem into a digital solution
• demonstrate ability to implementing a working digital solution.
• document and present a digital product to the target audience.
• work as part of computer science project execution team
• demonstrate an understanding of the tools for implementation of a working digital solution.

(d) Detailed Course Content

The content of course is not fixed. The project will be determined by the current industry needs and challenges. The broad themes of practical skills development include but not limited to:

• Mobile applications development
• Application and use RESTful APIs for example use of Mobile Money open API.
• Internet of Things and Embedded systems applications
• Robotics applications
• Block chain applications
• Implementation of projects
• Network and system administration
• Hardware maintenance
• Computer assembly. This can be done within the School of Computing and Informatics Technology or any other unit in Makerere University. Students will write a report at the end of the course.

(e) Mode of Delivery

The mode study will be blended learning including tutorials, self-study research and development, group and team discussions.

(f) Mode of Assessment

The assessment will be done by technical project documents, progress review meetings (40%) and project report, poster presentation, project demo and presentation (60%).

(g) Reading List

The reading materials will vary dependent on the selected thematic area and project.

In this course, students will cover the course content of the CCNA international

(a) Course Description

This course trains the student in theoretical and practical aspects of modeling and developing database applications. The course covers the rationale for databases in the current systems development environment, the modeling, implementation and testing/tuning database driven applications. It also covers advanced topics tile indexing hashing, security and performance.

(b) Aims

The aims of the course are to:

• Carry out system and data requirement analysis for database driven applications
• Model, map and tune database schema
• Implement typical database driven applications
• Monitor and tune operational database systems
• Handle some security and performance issues related to databases

(c) Learning outcomes

By the end of the course, the student should be able to:

• ADevelop a system and data requirements document for an application
• Carry out conceptual, logical and physical modeling of a typical database application
• Implement a database driven application
• Diagnose and fix performance related problems for operational systems
• Explain security, performance and integrity issues in database systems

(d) Detailed Course Content

• The Database Application Development Life-Cycle(15CH) The history of databases, basic definitions in DBMS, file based systems and database based systems, data models, players in the database field, changes in database applications trends. Description of the the DB application process, decisions to be made and their merits/demerits, common mistakes made in the development process.
• Database Applications Modeling (15 CH) System and data modeling process, documentation standards, The data modeling process, Conceptual modeling, Logical modeling, Physical modeling, enhanced conceptual modeling, normalisation and denormalisation, tuning operational systems, Data dictionary development.
• SQL, Scripting and Mini Project (20 CH) SQL statements, PL SQL, survey of a scripting language, Students will then be given a micro project in which they will develop and document the development of a small database driven application
• Advanced topics in DBMS(10 CH) Relational algebra, query processing, Indexing, Hashing, emerging architectures (parallel databases, distributed databases)

(e) Mode of Delivery

The study will be blended learning including a mix of student centered and learning teacher centered pedagogy. Specifically the student is expected to do some guided reading as well as practically implementing a database application.

(f) Assessment method

The assessment will be done by tests/assignment (40%) and final examination (60%)

(g) Reading List

1. Thomas Conolly and Carollyn Begg(2010) Database systems. A practical approach to design, implementation and management (Addison-Wesley)
2. Ramez Elmasri, and Shamkant B. Navathe,(2016) Fundamentals of Database Systems, (7th Edition)

(a) Description

This course examines principles, design, implementation, and performance of computer networks and data communication. The aim is to sharpen student’s understanding and skills in computer networking and data communication. Subjects for discussion include: Internet protocols and routing, local area networks, wide area networking, wireless communications and networking, performance analysis, congestion control, TCP, network address translation, multimedia over IP, switching and routing, mobile IP, peer-to-peer networking, network security, and other current research topics and technologies.

(b) Aims

The aims of the course unit are to:

• To provide a solid basis on the theoretical and practical understand of data communication networks
• To introduce students to standards and guidelines in computer and data communication networks
• To impact knowledge and skill relevant for the design, implementation and maintenance of modern computer communication networks
• To introduce students to emerging technologies in data communication

(c) Learning outcomes

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

• Describe theoretical concepts of computer communication systems, including standards, protocols, and infrastructure devices e.g. routers and switches.
• Design, install and manage a simple Local Area Network or A campus Wide Area Network
• Describe emerging communication technologies particularly wireless and fiber optic technologies
• Deploy sound network security practices and tools
• Describe emerging research directions in computer communication networks
• To write a few network scripts

(d) Detailed Course Content

• Module 1: Computer Networks and the Internet (4 CH) Introduction to computer networks and the internet, network edge and core, delay, loss and throughput in a packet, protocol layers and their service models.
• Module 2: Application Layer (8 CH) Network services and applications: DNS, HTTP, SMTP, FTP, peer-to-peer systems
• Module 3: Transport Layer (8 CH) Network transport architectures, Demultiplexing & Multiplexing, TCP, UDP, TCP congestion control and Reliable data transfer
• Module 4: Network Layer (8 CH) Routing and forwarding, intra-domain, inter-domain routing algorithms, Mobile IP, Queuing and scheduling.
• Module 5: The Link Layer and Multimedia Networking (12 CH) Link layers and local area networks, Ethernet, WiFi, and mobility/Wireless and Mobile Networks. Multimedia communications and quality of service
• Module 6: Network Security and Management (12 CH) Network measurement, inference, and management, Network security (ACL, IPSec, SSL etc), Network programming, Network experimentation and performance analysis and Protocol verification.
• Module 7: Emerging Trends (8 CH) Emerging trends in computer networks e.g Internet of Things, IPV6, 5G etc

(e) Mode of Delivery

The teaching pattern is by blended learning including lectures, tutorials, practical lab work, group discussion and class presentations

(e) Assessment method

Assessment will be in terms of tests and Assignment (40%) and final examination (60%)

(g) Reading List

1. James F. Kurose and Keith W. Ross. Computer Networking - A Top Down Approach Featuring the Internet, 3rd edition, Addison-Wesley, 2004, ISBN 0-321-22735-2.
2. L. Peterson and B. Davie, Computer Networks: A Systems Approach. Morgan Kaufmann Publishers, 1999.

(a) Course Description

Embedded and real-time systems are becoming an integral part of modern Computer Science applications. Most computers today’s come in the form of embedded and real-time systems in cars, refrigerators, coffee machines, security systems, etc. This courses aims to equip students with the understanding of embedded and real-time systems and skills to design, analyse and develop practical applications of embedded systems with real-time constraints in the areas of agriculture, health, entertainment, security, traffic control and management, automotive systems, energy, environment, robotics, smart cities, and others. The course topics include

(b) Aims

The aims of the course are:

• ensure that students gain an understanding of the principles, concepts and technologies for embedded and real-time systems
• ensure that students understand the programming, operating systems and software tools for developing software for embedded and real-time systems
• provide students with the skills to design and develop embedded systems
• introduce students to basic electronics, different types of micro-controllers and common architectures.

(c) Learning outcomes

By the end of the course, the student should be able to:

• design and develop embedded systems that address a practical challenge
• demonstrate an understanding of the software tools used for modern embedded systems development
• be able to build embedded systems with real-time constraints
• demonstrate an understanding of micro-controllers and common architectures. capabilities.

(d) Mode of Delivery

This course will delivered by blended learning including class lectures and practical sessions. Students will be introduced real life examples and illustrations to enable them apply the acquired knowledge with the business context. Students will be required to analyse business domain problem and come up with communications to address the needs as part of their coursework assessment

(e) Detailed Course Content

• Module 1 Introduction to Embedded and Real time systems (10 Hours): Properties of embedded systems, design, practical applications. Embedded systems architecture. Basic electronics and circuit for embedded systems.
• Module 2: Micro-controllers and Microprocessors (10 Hours). Basic structure of a Micro-controller. Examples of micro-controllers (ATTiny85, PICx, 32-Bit) and Development Boards.
• Module 3: Embedded systems with Arduino (15 Hours). Introduction to Arduino boards, The Arduino IDE, Arduino Board models, Shields, Arduino programming and Arduino C Library, Arduino example projects.
• Module 4: I/O Interfaces (10 Hours). Switches, analog inputs, high-power digital outputs, sound interfaces, serial interfaces, actuators, sensors, level conversation and LED display interfaces.
• Module 5: Schematic design and simulation programs (09 Hours). Circuit schematics and simulator programs, PCB designs.
• Module 6: Real-time Scheduling and Real-time Operating Systems (06 Hours). Scheduling, priorities, re-entrancy, programming multi-core based embedded systems.

(f) Assessment method

Assessment will be in terms of tests and practical assignments (40%) and final written examination (60%)

(g) Reading List

1. E. A. Lee and S. A. Seshia, Introduction to Embedded Systems - A CyberPhysical Systems Approach, LeeSeshia.org, 2011.
2. Elecia White Making Embedded Systems, O’Reilly Media, Inc., 2011
3. Paul Scherz and Simon Monk Practical Electronics for Inventors 4th Edition, Mc Graw Hill Education, 2016.
4. Heath, Steve. Embedded systems design, Oxford: Newnes, 2003
5. Wolf, Marilyn. TComputers as components: principles of embedded computing system design, Cambridge, MA: Morgan Kaufmann, 2017.
6. dawsonera. Computers as components: principles of embedded computing system design, London: Elsevier/Morgan Kaufmann.s, 2008

(a) Description

Artificial intelligence (AI) is a research field that studies how to encode the intelligent human behaviors onto a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Artificial Intelligence has been studied for of over half a century but there is not computer has been made that is as intelligent as a human being in all aspects. However, there are successful applications such as computers playing chess, manufacturing and in some cases, a computer equipped with artificial intelligence technology can be even more intelligent than humans. The Deep Blue system that defeated the world chess champion is a well-know example. The course focuses on the theory and practice of Artificial Intelligence. It will concentrate on the study of modern techniques for computers to represent taskrelevant information and make intelligent decisions towards the achievement of goals. The search and problem solving methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. The course investigates questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world, how to learn from experience, and how to learn decision rules from data.

(b) Aims and Objectives

• To provide students with comprehensive and in-depth knowledge of AI principles and techniques by introducing AIs fundamental problems, and the state-of-the-art models and algorithms used to undertake these problems.
• To expose students to the frontiers of AI-intensive computing while providing a sufficiently strong foundation to encourage further research.

(c) Learning outcomes

On successful completion of this course students will be able to:

• Explain what constitutes ”Artificial” Intelligence and how to identify systems with Artificial Intelligence.
• Compare AI with human intelligence, and discuss its strengths and limitations and its application to complex and human-centered problems.
• Discuss the core concepts and algorithms of AI, including intelligent agents; search algorithms; adversarial search; Constraint satisfaction problems; Utility theory, Uncertainty and probability theory, probabilistic reasoning in AI; Bayesian networks, Nearest neighbour methods;clustering.
• Apply the basic principles, models, and algorithms of AI to recognize, model, and solve real world problems that require intelligence.
• Analyze the structures and algorithms of a selection of techniques related to searching, reasoning, machine learning, language processing, computer vision, image processing and robotics.

(d) Detailed Course Content

• Module 1: Introduction to Artificial intelligence (AI). 4 hours The module provides a broad introduction to AI. We discuss what Artificial intelligence is and its Applications We also discuss intelligent agents, Environment types and types of intelligent Agents.
• Module 2: Planning- uninformed search. 8 hours The module discusses intelligent agents that plan. We discuss the methods used to find a plan for solving a problem using only problem definition information. We will discuss Depth first search, Breadth first search, iterative deepening search, and uniform cost search strategies for finding a solution in such situations. We will also discuss the advantage and disadvantages of using each of the strategies
• Module 3: Planning - A∗ search and Heuristics search. 8 hours We discuss methods used to find a plan for solving when we have some prior information to guide the plan. We will discuss what a heuristic is, space graph search, A∗ search, greedy search, optimally of A∗ graph search, admissible and consistent heuristics, generating admissible and consistent heuristics. We will also discuss how to generate the heuristics.
• Module 4 - Constraint Satisfaction Problems (CSPs) (12 hours) The module introduces a way of generating plans that satisfy some constraints. We discuss the basic concepts of a constraint satisfaction problems: What is a CSP? Types of CSPs, Constraint graph, Applications of CSPs, CSP formulation and solving a CSP by Backtracking search. We will also discusses various ways of speeding up the backtracking search: filtering, Arc Consistency, ordering, exploring the constraint graph structure, Iterative Algorithms for CSPs such as minimum conflict algorithm.
• Module 5: Adversarial Search (8 hours) We discuss a way of finding a plan where one or more agents a participating. We will discuss game playing, minmax algorithm, Alpha-Beta Pruning technique, Uncertainty and Utilities, Expectimax Search, utilities, multiagent utilities and Maximum expected utility (MEU).
• Module 6: Non-Deterministic Search and Reinforcement learning. 10 hours The modules provides an introduction to Markov Decision Processes and Policies as well as an introduction Reinforcement learning.
• Module 7: Bayesian Network (10 hours) The module introduces the Bayesian network, Representation , Independence and Inference

(e) Mode of Delivery

Teaching and learning is implemented through blended learning including Lectures, Tutorials sessions and Assignments/Quizzes. Lectures will introduce and motivate the basic concepts of each topic. Significant discussions and twoway communication are also expected during lectures to enrich the learning experience. Tutorials provide opportunities for obtaining feedback. The assignments/quizzes willreinforce theoretical concepts by their application to problem solving. Assignments will be done via programming work using Python programming language. Students will be expected to make presentations of their assignments for discussion in class.

(g) Assessment method

Assessment will be by continuous assessment through practical exercises and Coursework (40%) and final exam (60%)

(h) Reading List

1. Russell Stuart Jonathan and Norvig Peter. Artificial Intelligence: A Modern Approach. 4th Edition, Prentice Hall, 2021.
2. Philip C Jackson. Introduction to Artificial Intelligence. Second enlarged Edition.

(a) Description

The course applies mathematics to finite or discontinuous quantities in order to master the process of problem- solving, communication, reasoning, and modeling. It gives a basic understanding of mathematical structures that are fundamentally discrete. Objects studied in discrete mathematics are largely countable sets such as integers, finite graphs, and formal languages. Applications of such concepts to computer science are to be studied. Concepts and notations from discrete mathematics are useful in studying and describing objects and problems in computer algorithms and programming languages.

(b) Aims

At the end of this course, students should demonstrate the ability to:

• use mathematical logic to solve problems
• use iterative and recursive processes to prove properties of integers
• use sets to codify mathematical objects
• use discrete functions and relations to solve problems
• use a problem-solving approach in applying counting techniques in order to determine probabilities

(c) Learning outcomes

By the end of the course unit, the student should be able to:

• use correctly the classical notions of logic : implications, equivalence, negation, proof by contradiction, proof by induction, quatificators
• use set theory: union, intersection, complementary, maps, bijection, injection, surjection
• know the main formulas in combinatorics: enumerations of subsets, enumerations of injections, surjections, bijections
• know the main definitions, some classical theorems on graphs and apply graphs in concrete situations

(d) Mode of Delivery

Teaching and learning will be by blended learning including lectures, tutorials and practical sessions.

(e) Detailed Course Content

• Module 1 (6 hours) - Logic: Propositional and First Order Logic
• Module 2 (6 hours) - Functions and The Set Theory. Basic set operations, one-to-one, onto, inverses, composition and graphs.
• Module 3 (6 hours) - Relations and the Number Theory
• Module 4 (09 hours) - Methods of Proof: Direct Proofs, Implications, Contraposition, Contradiction, Proof by Cases, Induction
• Module 5 (06 hours) - Counting: Sum Rule, Product Rule, Permutations, Combinations, Combinatorial Proofs,
• Module 6 (3 hours) - Recursion: recursive definitions, recursive algorithms. Solving recurrences.
• Module 7 (9 Hours) - Graphs and Trees: Terminology, Representing Graphs, Isomorphism, Eulerian and Hamiltonian Graphs, Bipartite Graphs and Matchings, The Stable Marriage Problem, Graph Colorings.

(f) Assessment method

Assessment will be by assignments and/or tests (40%) and written examination (60%)

(g) Reading List

1. Discrete Mathematics and Its Applications by Kenneth H. Rosen, 7th Edition, McGraw Hill, 2012
2. Discrete Mathematics, by Kenneth A. Ross and Charles R. B. Wright, Fourth Edition
3. Discrete and Combinational Mathematics: An Applied Introduction, by Ralph Grimaldi, 4th Edition
4. Discrete Mathematics with Applications by Susanna S. Epp

(a) Course Description

Research is essential in nearly all aspects of life. This course unit enables students to learn and apply principles of conducting scientific research. It caters for the rationale of doing research, the research process, findings presentation as well as validation of findings. The course also prepares the students on the process of conducting the final year project

(b) The objectives of the course are to equip students with skills to:

• Formulate a research problem
• Document and justify the research aims
• Select appropriate methods of solving a research problem
• Correctly correct and analyze data
• Document and present findings

(c) Learning outcomes

A Student who has undertaken this course unit will be able to learn skills that will enable him/her to successfully undertake a research project. He/she will be able to:

• Identify a relevant or significant research problem.
• Identify the aims of a research project that can solve a given problem
• Select appropriate research methods to be used in solving a given project
• Select appropriate data collection techniques that can be used to gather data required to solve a given project.
• Select appropriate data analysis techniques and use them to process collected data, interpret data analysis results.

(d) Mode of Delivery Blended learning including:

• Lectures
• Online learning management systems
• Class discussions and presentations
• Group project-based assignments

(e) Detailed Course Content

• Introductory concepts of research : what is research, understanding the research process and fundamental concepts of research, how to formulate a research problem, research objectives, research questions, how to define scope, how to conduct literature review in a given study etc. (12 Hours)
• Research methods: Student learns the various quantitative and qualitative research methods. Student also learns the data gathering techniques. (12 Hours)
• Research evaluation: The evaluation phase of a research project, testing and validation of developed tool or research results. E.g. How to evaluate, testing, and validate research results; what are the evaluation criteria? What are the validation methods? What is one testing in the testing phase? (12 Hours)
• Research application: This covers the practical application of concepts learned from parts I to III. The lecturer suggests one or several class projects that require practical application of concepts learned from parts I to III.(12 hours)
• Report writing: How to prepare a research report, how to report research results/findings, how to present feedback or findings from evaluating the developed tool or research results. (12 Hours)

(g) Assessment method

The assessment will constitute Practical assignments on at least 5 chapters of the course and written course work (40%) and written Exam (60%)

(h) Reading List

1. Zikmund, W.G. Thomson South Western Business Research Methods (8th Ed.)(2010)
2. Leedy, P.D., & Ormrod, J.Practical Research: Planning and Design Paperback (2004)
3. Sounders, M., Lewis, P. & Thornhill, A. Research Methods for Students. 3rd edition(2003) UK, Financial times, Prentice hall
4. Graziano, A.M, Michael, L. Research Methods: A process of Inquiry Raulin, Hardcover, Prentice Hall(2006)

(a) Course Description

Machine learning is the science of getting computers to act without being explicitly programmed. Over the past years, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is being used in many applications now days and many researchers think it is the best way to make progress towards human-level artificial intelligence. The machine learning course aims at introducing the fundamental concepts of machine learning. In the course, students learn about the most effective machine learning techniques, and gain practice in implementing them and making them work. More to that students will learn about the theoretical underpinnings of learning and practical know-how needed to quickly and effectively apply these techniques to new problems. The course introduces models and algorithms for regression, classification and clustering. Students taking the course will need to be familiar with linear algebra, probability theory, and programming specifically in Python.

(b) aim:

• To introduce methods for learning from data.
• To provide the necessary mathematical background to enable students to understand how the methods work.
• To provide an understanding on how to evaluate the performance a machine learning models and how to get the best performance from them.

(c) Learning outcomes

On successful completion of this course students will be able to:

• develop an appreciation for what is involved in learning from data.
• understand a wide variety of learning algorithms
• understand how to apply a variety of learning algorithms to data
• understand how to perform evaluation of learning algorithms and model selection.

(d) Mode of Learning

• Teaching and learning will employ blended approaches implemented through online or face to face Lectures, Tutorials sessions and Quizzes. Lectures will introduce and motivate the basic concepts of each topic. Significant discussions and two-way communication are also expected during lectures to enrich the learning experience. Tutorials provide opportunities for obtaining feedback. Quizzes will reinforce theoretical concepts by their application to problem solving
• Home works and Assignments: The best way to learn about a machine learning method is to program it yourself and experiment with it. So the assignments will generally involve implementing machine learning algorithms, and experimentation to test your algorithms on some data. The students may asked to summarize their work, and analyze the results, in brief (3-4 page) write ups. The implementations will be done in Python.

(e) Detailed Course Content

• Module 1: Introduction to Machine Learning (6 CH) This module introduces the students to the major concepts of machine learning. It introduces the broad concepts of learning. Concepts like: supervised learning, unsupervised learning, data representation, and overfitting will be discussed in this module.
• Module 2: Machine Learning Basics (10 CH) The module will introduce the basis concepts and distributions used in building machine learning models. In the module we discuss the Bayesian Theorem, Gaussian Distribution, Gaussian Mixture Models and Maximum Likelihood Estimation technique
• Module 3: Feature selection and Evaluation metrics (4 CH) In this module we discuss methods of feature selection and the methods for evaluating the models.
• Module 4: Supervised learning Models (20 CH) The module introduces various supervised learning models in detail. In the module we discuss Linear Regression, Logistic Regression, Naive Bayes Classifier, Decision Tree, Support Vector Machine (SVM), and K-nearest Neighbour classifier. We also discuss Regularisation and Gradient Descent.
• Module 5: Unsupervised Learning (8 CH) This module introduces the idea of learning from data by clustering. We discus the K-Means and Expectation Maximisation methods.
• Module 6: Introduction to Deep Learning (12 CH) The module provides an introduction to Deep learning. Concepts concerning Multilayer Perceptron, Convolutional Neural Network and Recurrent Neural Network will be discussed.

(f) Assessment method

Assessment will be by continuous assessment through programming assignments (20%) , written assignments and quizzes (20%) and Final exam (Practical exam (30) and written (30%)).

(g) Reading List

1. Bishop, C., M. Pattern Recognition and Machine Learning. Springer 2006
2. Tom Mitchell. Machine Learning McGraw Hill.
3. Andreas Mueller. Scikit-learn Tutorial: Statistical-Learning for Scientific Data Processing.
4. Shai Shalev-Shwartz and Shai Ben-David Understanding Machine Learning: From Theory to Algorithms.
5. Stephen Marsland. Machine Learning: An Algorithmic Perspective
6. Willi Richert and Luis Pedro Coelho. Building Machine Learning Systems with Python

(a) Description

Robotics as an application combines many fields and allows automation of products such as cars, vacuum cleaners, and factories. The course aims at equipping the students with the fundamental concepts used in automating robotics; specifically the students will be introduced to the basic computation concepts used in robotics. The focus will be mainly on mobile robotics, and will cover the basic issues in this dynamic field via lectures and a large practical element where students work in groups. Topics include will Introduction to Robotics, Locomotion Kinematics and planning, perception and sensing, Planing, localization and navigation. The mathematical basis of each area will be emphasized, and concepts will be motivated by using common robotics applications and programming.

(b) Course Objectives:

• To understand the basic concepts and theory governing the programming of robots that perform autonomous tasks such as navigation and manipulation.

(c) Learning outcomes

By the end of the subject, students should be able to:

• Understand the defining properties of a robot: sensing and action, linked by processing
• Understand an overview of the practical issues of modern-day mobile robotics.
• Understand Robot locomotion methods, particularly wheel configurations and uncertainty in motions.
• Understand the use of simple sensors in reactive, behavioural programming.
• Understand and implement localization and mapping algorithms using different sensor modalities.
• Understand the concepts of different approaches for motion planning such as roadmaps, feedback control and sampling based methods
• Apply the tools learned in the class to physical robots.

(d) Detailed Course Content

• Module 1: Introduction to Robotics (4 CH) What is robotics, applications and challenges in robotics, categories of robotics, robots and types, mobiles robots: state of the art.
• Module 2: Locomotion and Kinematics (8 CH, 20 PH) Basic 3D geometry and notation, Introduction kinematics, Open and Closed Kinematics, Coordinate systems and relationship, Homogeneous Transforms, Direct Vs inverse kinematics, Kinematics- dynamics and control, the robotics locomotion scheme, locomotion concepts, characteristics of locomotion, legged locomotion, locomotion of wheel, Standard Wheel Configuration, locomotion Notion, mobile robot locomotion, Mapping Wheel rotation Speed to Velocity, motion and State on a 2D Plane,Position-Based Path Planning,
• Module 3: Perception and Sensing (9 CH, 20 PH) What is perception? what are the challenges to perceptions? mobile robot control system, sensors for mobile robot, perception for Mobile robots and it’s challenges, evolution of robotic sensors, classification of sensors, sensor measurements, sensor outlines: single and multiple value sensors, Servoing, combining: sensing/ Action Loops, combining Sensors: World Model Approach.
• Module 4: Localization and Navigation (9 CH, 20 PH) What is localization?, Challenges to localization, Localization-based navigation versus programmed solutions, Belief representation, Map representation, Probabilistic Map-Based Localization, Other Example s of Localization Systems, Autonomous Map Building.

(e) Mode of Delivery

Blended learning including:

• Teaching and Learning is implemented through online or face to face lectures, tutorial sessions, Assignments/Quizzes, Research papers, group discussion and class presentations. Lectures will introduce and motivate the basic concepts of each topic
• Significant discussions and two-way communication are also expected during lectures to enrich the learning experience. Tutorials provide opportunities for obtaining feedback. The assignments/quizzes will reinforce theoretical concepts by their application to problem solving
• Project programming work: The best way to learn about a Robotics is to program it yourself and experiment with it. The project implementation will using the Raspberry Pi boards computer and Python using BrickPi interface boards and Lego NXT motors and sensors.

(g) Assessment method

Assessment will be by continuous assessment (40%) distributed as follows: Assignments/Quizzes and group research (10%), Tests (10% and projects work (20%)) and Final exam (60%) distributed as follows Written (40%) and practical exam (20%).

(h) Reading List

1. Robin R. Murphy. Introduction to AI Robotics MIT Press, 2000
2. Maja J. Mataric. The Robotics Primer MIT Press, 2007.
3. Steven M. LaValle. Planning Algorithms. Cambridge University Press, 2006.
4. John J. Craig. Introduction to Robotics. Mechanics and Control. Third Edition, 2005
5. Kevin M. Lynch and Frank C. Park Modern Robotics. Mechanics, Planning and Control. 2017.
6. Mark W. Spong and Seth Hutchinson, and M. Vidyasagar Robot Modeling and Control. First Edition

(a) Course Description

This is a course that puts emphasis on the concepts and practices that reduce software cost while increasing reliability and modifiability. The quality of software systems developed is a major concern in today’s world of automation. This course focuses on how to build quality in both the software product and process. Software verification ensures that the product is being built according to the requirements and design specifications

(b) Course Objectives:

The objectives of this course are to provide the students with:

• Knowledge of factors that affect the quality of a software product.
• The techniques to produce high-quality and reliable software, regardless of the software system’s level of complexity

(c) Learning outcomes

By the end of the subject, students should be able to:

• Understand software testing and quality assurance as a fundamental component of the software life cycle.
• Define the scope of software quality assurance and testing projects
• Efficiently perform quality assurance and testing activities using modern software tools
• Describe the components of configuration management.

(d) Detailed Course Content

• Module 1: Introduction (6 CH) what is software quality and its importance; Software Quality characteristics;General Quality views; User versus producer expectations; Quality frameworks, standards and models; Software quality management and documentation; Quality Dimensions, The Software Quality Dilemma, Achieving Software Quality
• Module 2: Software Quality Assurance (8 CH) Components; Correctness and Defects; Defects and Quality; Defect prevention, reduction and containment; QA in software development and maintenance processes; Verification and Validation concepts;
• Module 3: Quality Engineering (6 CH) activities and processes; Quality engineering in software processes; context and cost. Software Quality Control and why it is needed;
• Module 4: Software Quality metrics (8 CH) purpose; types of software audits; Software Quality Indicators; How can we measure software quality?
• Module 5: Software Testing (12 CH) the software testing process, planning and testing strategies, Test management and automation; Test case design; Alpha and Beta Testing; testing and quality assurance; functional versus structural testing; Testing and the Software Life Cycle; Static testing: techniques and types; Dynamic testing
• Module 6: Software configuration management (8 CH) items and management; tasks and organization; baseline and intermediate software configuration versions;
• Module 7: Formal Verification (12 CH) Basic Concepts: Formal Verification and Formal Specification; Axioms; pre/post conditions; Weakest pre-conditions; Software inspection and walkthroughs - informal code reviews, formal inspection;

(e) Mode of Delivery

The teaching pattern is by blended learning including including lectures and tutorials, practical lab work, group discussions and class presentations

(g) Assessment method

Assessment will be in terms of Assignments and tests (40%) and final written examination (60%)

(h) Reading List

1. Stephen H. Kan. Metrics and Models in Software Quality Engineering (2nd Edition), 2001.
2. Gopalaswamy Ramesh and Srinivasan Desikan. Software Testing: Principles and Practices, 2007.
3. Tom Badgett, Corey Sandler, Glenford J. Myers. The Art of Software Testing, 3rd Edition, 2011.
4. Jeff Tian. Software Quality Engineering, 2005.

(a) Course Description

Cloud computing is a computing paradigm, where a large pool of systems are connected in private or public networks, to provide dynamically scalable infrastructure for application, data and file storage. With the advent of this technology, the cost of computation, application hosting, content storage and delivery is reduced significantly. This course gives an introduction to cloud computing technologies, applications as well emerging paradigms such as Cloud Native Computing.

(b) Course Objectives:

The aims of this course unit are to provide students with:

• Knowledge of cloud computing models and services
• An understanding of public and private clouds.
• Techniques to set up, manage and protect data in the cloud
• An understanding of emerging cloud native paradigm and DevOps concepts, methodologies and tools.

(c) Learning outcomes

By the end of the subject, students should be able to:

• Analyze the trade-offs between deploying applications in the cloud and over the local infrastructure
• Compare the advantages and disadvantages of various cloud computing platforms.
• Deploy applications over cloud computing infrastructures such as Amazon Web Services, Windows Azure, and Google AppEngine.
• Identify security and privacy issues in cloud computing.
• Work with modern cloud native and DevOps tools such as Kubernetes and Docker

(d) Detailed Course Content

• Module 1: Overview of Cloud computing (6 CH) Cloud architecture and benefits, enabling technologies, evolution and case studies for motivation, common cloud providers, cloud computing platforms, types and components of clouds, Deployment models i.e public, private and hybrid, Key characteristics, obstacles and opportunities to cloud computing.
• Module 2: Service Models (6 CH) Infrastructure as a service (IaaS), Platform as a service (PaaS), Software as a service (SaaS)
• Module 3: Cloud enabling technologies (12 CH) Using and managing virtual machines in the cloud (Virtualization). Data centers, broadband, containers. Gain experience with Git, VMWare (or any other hypervisor), Docker.
• Module 4: Cloud Security (8 CH) Cloud security challenges, security approaches. Securing the cloud and data. Establishing identity and presence. Data security and control.
• Module 5: Migrating into the cloud (8 CH) A case study project with any of the existing services such as Amazon, Google, Microsoft Azure and others.
• Module 6: Cloud Native Computing and Microservices architectures (5 CH). Introduction to cloud native computing, Micro-services, Desining Cloud Native applications, Serverless computing, Containers and Kubernetes, DevOps, continuous integration and delivery.
• Module 7: Project (15 CH) Students work in small groups to create an application and be able to host it in the cloud using technologies learned and available cloud platforms such as AWS, GCP, Azure and the local ones such as Crane Cloud.Module 7: Project (15 CH) Students work in small groups to create an application and be able to host it in the cloud using technologies learned and available cloud platforms such as AWS, GCP, Azure and the local ones such as Crane Cloud.

(e) Mode of Delivery

The teaching pattern is by blended learning including lectures, lab sessions and group projects.

(g) Mode of Assesment

Assessment will constitute practical assignments, tests and a project done in groups. A project assessment will include both a written report and oral presentation (40%) and written Exam (60%).

(h) Reading List

1. Rajkumar Buyya and James Broberg and Andrzej Goscinski Cloud Computing: Principles and Paradigms, Willey 2011.
2. Ray J Rafaels Cloud Computing : From beginning to end.
3. Thomas Erl and Zaigham Mahmood and Ricardo Puttini Cloud Computing: Concepts, Technology & Architecture, Prentice Hall 2013
4. Boris Scholl, Trent Swanson, Peter Jausovec Cloud Native: Using Containers, Functions, and Data to Build Next-generation Applications, O’Reilly Media, Inc Hall, 2019.
5. Engineer Bainomugisha and Alex Mwotil Crane Cloud: A Resilient MultiCloud Service Layer for Resource Constrained Settings, 2021.

(a) Course Description

The course covers an introduction to the classical and contemporary theory of computation including automata, formal languages, Turing machines, recursive functions, computability and in computability, complexity, and the classes of P and NP. It examines the classes of problems that can and cannot be solved by various kinds of machines. Native Computing.

(b) Course Objectives:

The aims of this course unit are to provide students with:

• The concepts, notations, and techniques of the theories of Automata, Formal Languages, and Turing machines
• Classification of computational problems given to them, in terms of their computational complexity
• Deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem.

(c) Learning outcomes

By the end of the subject, students should be able to:

• know different descriptions of various classes of formal languages
• prove regularity or context-freeness of languages and non regularity or non context freeness of languages. platforms.
• define automata, pushdown automata and Turing machines which perform prescribed tasks.
• know basic unsolvable problems and will be able to prove unsolvability by means of reductions.
• know the basics of the complexity classes P and NP, and of NP1 complete problems.

(d) Detailed Course Content

• Module 1: Introduction (4 CH) definitions, why study automata, complexity and computability, applicability and relevance, computation theory, complexity theory, automata theory, languages and strings.
• Module 2: Finite state machines and regular languages (15 CH) finite automata, regular expressions, applications of regular expressions, deterministic and nondeterministic finite automata, transition diagrams, equivalence of NFAs and DFAs, conversion from a regular expression to a DFA, conversion of finite automata to regular expression, equivalence and minimization of automata.
• Module 3: Context Free Grammars (10 CH) definition of CFG, examples, derivations, sentential forms, parse trees, applications of CFGs, ambiguity in grammars and languages
• Module 4: Push-down automata (6 CH) definition and examples, equivalence of PDAs and context-free grammars i.e A PDA from a Given and A CFG from a Given PDA, deterministic PDAs, Top-down and bottomup parsing
• Module 5: Computability (5 CH) Turing machines, Church-Turing Thesis, Undecidable Problems
• Module 6: Complexity (5 CH) The classes P and NP, NP-Computable problems, time complexity - big O and small O notations, decision trees. Desining Cloud Native applications, Serverless computing, Containers and Kubernetes, DevOps, continuous integration and delivery.

(e) Mode of Delivery

Teaching will be in form of class blended learning including lectures, tutorials and group assignments.

(g) Mode of Assesment

Assessment will be in terms of Assignments and tests (40%) and final written examination (60%)

(h) Reading List

1. Elaine Rich. Automata, Computability and Complexity: Theory and Applications.
2. Michael Sipser. Introduction to the Theory of Computation, Third Edition, 2013.
3. John E. Hopcroft, Rajeev Motwani, and Jeffrey D. Ullman. Introduction to Automata Theory, Languages, and Computation. Addison Wesley, 2000.
4. Daniel I. A. Cohen. Introduction to Computer Theory. John Wiley & Sons, Inc., 1986.

(a) Course Description

The course introduces the fundamental processes, principles and techniques of software development. It focuses on development of software that is safe from bugs, easy to understand and maintainable. Topics include tools and techniques for software construction, unit and integration testing, debugging and qualities of software systems such as correctness, clarity, reliability, efficiency and portability. The course provides a hands on experience with a project at the end of the course

(b) Course Objectives:

The aims of this course unit are to provide students with:

• Fundamental principles of software development.
• Software testing and debugging.
• Object-oriented design with UML tools

(c) Learning outcomes

By the end of the subject, students should be able to:

• be conversant with program design patterns over the local infrastructure
• demonstrate a working knowledge of the object-oriented paradigm for software design and implementation. platforms.
• be able to apply methods of systematic testing
• demonstrate knowledge of agile programming
• have a broader and deeper knowledge of building software systems Docker
• be knowledgeable in version control and use of frameworks for software development

(d) Detailed Course Content

• Module 1: Preliminaries (6 CH) Definition and fundamentals, Software Construction activities, why study software construction, standards, planning for software construction. Software development process models including the waterfall model, incremental development and Reuseoriented software engineering. Code documentation and Code Review - purpose, smelly code, style standards, fail fast.
• Module 2: Configuration management and Version control (8 CH) incremental development, repositories and working copies, distributed versus centralized, features and best practices, Git and open source online repositories, team version control, Git in IDEs, Software development tools (IDES), Frameworks and developing graphical user interfaces.
• Module 3: Debugging and Testing (8 CH) planning and testing strategies, finding and fixing bugs, techniques for avoiding debugging, strategies for debugging, debugging tools; Testing: unit and integration testing, test case generation; Blackbox and Whitebox Testing. Testing procedures and tools; Error-Handling Techniques; Defensive programming. the software testing process, Test management and automation; Alpha and Beta Testing; testing and quality assurance; functional versus structural testing; Testing and the Software Life Cycle; Static testing: techniques and types; Dynamic testing
• Module 4: Object-oriented design (10 CH) Introduction to UML, use cases and scenarios, use case diagrams, UML diagrams: sequence, object and class, deployment diagrams, using UML tools. Designing good APIs; API development.
• Module 5: Code Design (8 CH) the design process - inputs, activities and outputs. Minimizing and managing complexity: the software crisis, separation of concerns. Desirable design characteristics, levels of software design, design techniques, design approaches including iteration, divide and conquer, prototyping, collaborative design
• Module 6: Design patterns for Object-oriented programming (10 CH) when (not) to use patterns; Behavioral Patterns e.g observer, visitor patterns; Structural patterns such as adapter and decorator patterns; Creational patterns such as factory, prototype patterns
• Module 7: Agile software development (5 CH) agile methods, extreme programming; project management.
• Module 8: Formal Verification (5 CH) Basic Concepts: Formal Verification and Formal Specification; Axioms; Assertions and correctness proofs: pre- and post-conditions; Weakest pre-conditions; Software inspection and walkthroughs - informal code reviews, formal inspection;

(e) Mode of Delivery

The teaching pattern is by blended learning including lectures, tutorials, practical lab work, group discussion and class presentations.

(g) Mode of Assesment

A programming project will be given that covers all topics learned in the course. The assessment will be done by weekly programming assignments, quizzes and tests (40%) and final examination (60%).

(h) Reading List

1. Steve McConnell. Code Complete: A Practical Handbook of Software Construction, Second Edition. Microsoft Press, 2004.
2. Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition. Neural Computation, 74(9):1429–1435, April 2011.
3. Brian W. Kernighan and Rob Pike The Practice of Programming, 1999

(a) Description

This course provides students with a mentorship opportunity in a real work environment. Students are given an opportunity to go out in the field to put whatever they have learnt into practice. It is a 2-3 months program and during this period students are attached to various organizations where they carry out training from.

(b) Course Objectives

The aims of the course are:

• To equip students with practical skills in all aspects of computer science such as programming, databases, software engineering, artificial Intelligence and general hardware maintenance.
• To provide students with mentor ship in writing professional software products.
• To give students an opportunity to build team-work, communication, appearance and leadership skills needed in the workforce

(c) Learning Outcomes

Upon succesful outcome, students will;

• Have gained work experience that allows them to sample professional environments in which they might seek careers.
• Have experience that will help prepare them for careers and research ahead.

(d) Detailed Course Content

Not specified

(e) Mode of Delivery

The student is to be under the supervision of one of the workers (Field Supervisor) in the organization. The student is assigned duties in line with the operations of the organization. Staff from Makerere University (Academic Staff) will make visits to get the students view of the organization as well as the organizations view about the student.

(f) Mode of Assesment

• The two supervisors (both Academic and Field Supervisor) will be given forms to evaluate the students. On the side of the student, he/she is expected to send a weekly report to the academic supervisor on the various activities carried out and at the end of the internship provide a final report.
• The two reports will be used to evaluate the student. The final report carries 40 %, the Academic supervisor assessment carries 40% and Field supervisor assessment carries 20%.

(g) Reading List

There is no particular reference material for internship, except whatever may be recommended by the student supervisors.

(a) Description

This course teaches how to recognize a business opportunity and develop the opportunity into a business. The controlling functionsaccounting, finance, marketing, managementas well as legal and economic considerations are applied. In this course Student responsibility and initiative are encouraged as business strategies are created, planned, and presented as a final producta business plan for an actual business venture. Through the process of developing the business plan, students acquire skills necessary to operate a successful business.

(b) aims:

The course aims at providing the skills listed below to students:

• Understand entrepreneurial behavior with reference to business
• Acquire skills involved in creating, planning, developing and managing the entrepreneurial business
• Recognize the importance of entrepreneurship and identify the profile of entrepreneurs and their role in economic growth
• Designing various kinds of UI, in particular graphical user interfaces (GUIs) , computer and mobile and web sites;
• Evaluating UIs
• Identify an attractive market that can be reached economically

(c) Learning outcomes

By the end of the subject, students should be able to:

• Describe entrepreneurial behavior with reference to business
• Demonstrate knowledge involved in creating, planning, developing and managing the entrepreneurial business
• Articulate the importance of entrepreneurship and identify the profile of entrepreneurs and their role in economic growth
• Exhibit the ability to find an attractive market that can be reached economically

(d) Detailed Course Content

• Module 1: Introduction to Business (6 Hours)
Module 2: Introduction to Entrepreneurship (6 Hours)
• Module 3: Business and the entrepreneurial environment (3 Hours)
• Module 4: Creating the entrepreneurial business (6 Hours)
• Module 5: Business planning (6 Hours)
• Module 6: Successful business strategies (6 Hours)
• Module 7: Forming and protecting a business Managing the Entrepreneurial business (3 Hours)
• Module 8: Management of Resources in the entrepreneurial business (3 Hours)
• Module x: 9. Marketing in an entrepreneurial business (3 Hours)
• Module 10: Managing Money in an entrepreneurial business (3 Hours)

(e) Mode of Delivery

Text books, conference and journal publications, and online resources.

1. Lectures
2. Take home assignments
3. E-learning
4. Discussions and presentations
5. Problem-based/ Case studies

(f) Assessment method

Assessment will be in form of assignments and tests (40%), practical Exam (30%) and final written exam (30%)

(g) Reading List

1. Stokes, D.R. & Wilson, N.. Small Business Management and Entrepreneurship, 6th Edition. Publisher: Carnegie Learning EMEA, 2010.
2. Storey, D.J. & Greene, F.J. Small Business and Entrepreneurship. Illustrated Edition. Publisher: Financial Times Prentice Hall., 2010
3. Beaver, G. Small Business, Entrepreneurship & Enterprise Development.. Publisher: Financial Times Prent. Int., 2002.

(a) Description

This course introduces the fundamentals of business process management (BPM) that will be used to systematically analyze, improve and automate business processes. Students will learn the techniques and tools that will be used for process identification, process discovery, process analysis, process redesign and improvement, process automation and monitoring. Students will learn how to apply these techniques and tools to a wide range of examples and case studies that will show the power of BPM in practice

(b) Course Objectives:

• Equip students with skills needed to identify business processes within an organization
• Equip students with skills necessary to document business processes using different business process modeling techniques.
• Make students understand qualitative and quantitative techniques useful for the analysis of the performance of business processes.
• Equip students with skills necessary to identify and analyze business improvement opportunities based on business process models
• Make students understand how IT can effectively be used to build process automation solutions that realise the benefits of process improvement.

(c) Learning outcomes

On completion of this course unit, the students will be able to:

• Identify business processes within an organization, study their boundaries and interrelations, and prioritize their management using different criteria
• Document business processes at different levels of detail using contemporary business process modeling techniques.
• Apply qualitative and quantitative techniques to analyze the performance of business processes and to assess the impact of business process changes
• Identify and analyze business improvement opportunities based on business process models.
• Use IT effectively to build process automation solutions that realise the benefits of process improvement.

(d) Detailed Course Content

• Module 1: Introduction to Business Process Management (5 CH) Introduction to the definition of business process and BPM. Provide a historical overview of the BPM discipline. Finally, the BPM lifecycle will be discussed.
• Module 2: Process Identification (5 CH) Discuss the context of process identification. Present a method based on process architecture definition and process selection with listing initial set of processes and their architecture. Discuss process selection which considers criteria for defining priorities of processes using a portfolio.
• Module 3: Essential Business Process Modelling (10 CH) Discuss business process modelling based on the Business Process Modelling Notation (BPMN). Discuss branching and merging, business objects, and resources. Cover process decomposition and process model reuse.
• Module 4: Process Discovery (10 CH) Discuss the setting of process discovery and process discovery methods. Process Modelling method and Process Model Quality Assurance
• Module 5: Qualitative Process Analysis (5 CH) Discuss value added analysis and waste analysis. Stakeholder Analysis & Issue Documentation. Root-Cause Analysis.
• Module 6: Quantitative Process Analysis (15 CH) Cover topics on Flow Analysis, Queuing Analysis and Process Simulation.
• Module 7: Process Redesign (5 CH) Discuss the essence of process redesign. Transactional Methods to process redesign and Transformational Methods.
• Module 8: Process-Aware Information Systems (5 CH) Types of Process-Aware Information Systems, Advantages of Introducing a BPMS, Challenges of Introducing a BPMS

(e) Teaching and Learning Pattern

The teaching and learning approaches will combine classroom lectures, discussions and group activities, quizzes and take home assignments. A group project shall form part of the coursework. The material presented in class will overlap that of the text but will contain additions and variations.

(f) Mode of Delivery

• Lectures
• Practical sessions in computer Laboratories
• Online learning mnagement systems
• Class discussions and presentations
• Problem-based/case studies
• Project-based assesments that can be done in groups

(g) Assessment method

Assessment will be in terms of tests and Individual assignments (40 %) and a final examination (60%)

(h) Reading List

1. Dumas, M. La Rosa, M., Mendling J., & Reijers, H. A. Fundamentals of Business Process Management (2nd Ed) Springer., 2018.
2. Weske, M. Business Process Management: Concepts, Languages, Architecture. (3rd Ed) Springer, 2019

(a) Description

This course provides a foundation for further studies in information security. It introduces students to the exciting fields of cryptology and coding theory. Fundamentally, it deals with the mathematics that underlies modern cryptology. Cryptology combines the studies of cryptography, the creating of masked messages, and cryptanalysis, the unraveling of masked messages. Coding theory is the study of coding schemes used to detect and correct errors that occur during the data transmission. Basic problems of cryptography and coding are discussed. Topics include classical ciphers, public key cryptosystems (RSA, Diffie-Hellman key exchange, ElGamal), digital signatures, codes, linear codes, perfect codes and cyclic codes.

(b) Aims:

• To understand the building blocks of crypto systems and error correction
• To gain historical understanding of the evolution of crypto systems
• To develop tools necessary to crypto analyze crypto systems
• To gain insights in the practical application of cryptology and error correction in the modern information age.
• To understand the goals and trade-offs associated with encryption and error-control coding systems.

(c) Learning outcomes

By the end of the course, the student should be able to:

• Deploy sound cryptographic practices and tools
• Discuss the goals and trade-offs associated with encryption and error

(d) Mode of Delivery

The course will be delivered by blended learning including lectures, tutorials, and group assignments.

(e) Detailed Course content

• Module 1: Overview (5 CH) History of cryptology and coding theory; Classical Cryptography and cryptosystems
• Module 2: Encryption (10 CH) Symmetric and asymmetric; Private-Key and Public-Key Cryptography; Key management - Diffie-Hellman Key Exchange, Elliptic curve cryptography; Digital Signatures - RSA algorithm; Steganography
• Module 3: Block Ciphers (8 CH) Data Encryption Standard (DES); Advanced Encryption Standard (AES); Secure Communication and Message Integrity; Message Authentication Codes and hash functions. Stream Ciphers.
• Module 4: Security Protocols (10 CH) Authentication protocols - mutual authentication and one-way authentication; IP security; Secure Sockets Layer (SSL), SSL/TLS; Email security; Security Facilities in the TCP/IP Protocol Stack.
• Module 5: Coding theory (12 CH) Coding theory fundamentals; Error detection, correction and decoding, Error-correcting codes: Hamming distance, Linear codes; Hamming codes, Reed-Muller codes; Cyclic codes; Applications in computer science

(g) Assessment method

Assessment will be by tests and assignments (40%) and final written examination (60%)

(h) Reading List

1. A. Menezes, P. van Oorschot, and S. Vanstone. Handbook of Applied Cryptography. CRC Press, 1996.
2. Trappe and Washington. Introduction to Cryptography with Coding Theory, 2nd edition. Prentice Hall, 2006.
3. Simon Singh. The Code Book. Doubleday, 1999
4. William Stallings. Cryptography and network security: principles and practice (7th edition). Pearson, 2017.

(a) Description

This course highlights programming practices that are vital in the day today work of a programming Professional. While many systems are described by functionalities, some important aspects like security, robustness, and maintainability are ignored. Students are to get an in depth understanding of these concepts as well as exploring the current trends in the programming environment. In the first half of the course, a framework like Django, CodeIgniter, Ruby on Rails, Laravel (or Symfony), MonoRail, or Bootstrap is to be used to demonstrate the MVC (Model-View-Controller) architecture. In the other half, the course should also provide mobile applications development skills to the learners.

(b) Aims:

The aims of the course are:

The aim of the course is to concretize the student’s past programming experience as well as highlighting critical practices like efficient code reuse and parallel development in programming that are necessary for a professional programmer

(c) Learning outcomes

By the end of the course, students will be able to:

• Able to develop well documented and well- structured software that can easily be maintained
• Able to implement a complete application using the MVC architecture
• Knowledgeable in other programming practices like mobile applications development
• Aware of newer programming paradigms like service oriented and cloud computing

(d) Mode of delivery

There will be blended (online or face-to-face) sessions, tutorials, and/or practical sessions.

(e) Detailed Course Content

• Module 1 (8 hours): The MVC architecture
• Module 2 (8 hours): Regression test programming
• Module 3 (12 hours): Trends in programming paradigms
• Module 4 (16 hours): Visualisation
• Module 5 (16 hours): Mobile applications development

(f) Assessment method

The assessment will be done by tests and take home practical assignments (40%), practical examination (30%) and written examination (30%)

(g) Reading List

1. Advanced Programming in the UNIX Environment by W. Richard Stevens and Stephen A. Rago Addison Wesley 2013
2. Test-Driven Development: A Practical Guide, by David Astels
3. Beautiful Code: Leading Programmers Explain How They Think, by Andy Oram & Greg Wilson
4. Refactoring: Improving the Design of Existing Code, by Martin Fowler
5. Refactoring to Patterns, Joshua Kerievsky

(a) Description

This course introduces to the student the concepts of operations research (OR), enable the student explore the different manifestations of operations research and go deeper into two such manifestations: Linear systems and graphical systems. The students will learn how to use popular OR packages such as GAMS, AMPL, and OPL.

(b) Aims:

The aims of the course are:

• What Operations Research is and its use in general life
• How to formulate and solve simple linear programming problems
• How to analyse effects of variations in LP problems including modifications of the initial formulation
• How to solve simple graphical problems
• How to apply graphical approaches (like matching, traversal and spanning trees) in real life /Computing scenarios and use of OR programming packages.

(c) Learning outcomes

By the end of the subject, students should be able to:

• Explain what OR is, its variants and its role in real life
• Formulate LP problems given the rubric of the problem
• Solve LP problems and be able to deduce special cases (like in existent solutions, unbounded solutions, degeneracy and alternate optima)
• Be able solve basic graphical problems
• Be able to interpret graphical problems in relation to real life and use popular OR computing packages/libraries.

(d) Mode of Delivery

The mode of study will be by blended learning including a mixture of student and teacher centered. The teacher will explain the concepts in class but students will also make extensive discussions and guided reading

(e) Detailed Course Content

• Concepts of Operations Research: (10 CH) Introduction to concepts of operations research, a classical example of a real life problem that can be solved in an Operations Research way at minimal resources (time, space, money, materials, etc). Discussion of 2-3 typical generic operations research problems, how they apply in real world and instances of solutions to them. The generic problems can be any of the likes of (i) Knapsack problem (ii) traveling salesman problem (iii) Chinese postman problem (iv) Celebrity problem (v) Stable marriage problem etc
• Introduction to Linear Programming: (10 CH) Formulation of LP problems, graphical solutions to LP problems, simplex approach, artificial variables, big M and duality
• Further Linear Programming: (10 CH) Algebraic LP solutions, Properties of LP problems/solutions, Post optimality Analysis, sensitivity analysis (and their implication in real life)
• Graph Related Systems: (15 CH) Graph definition, representation, properties, types, graph traversal, (minimum) spanning trees, matching and matching problems, graph coloring. The applications to the world of computer science will be included. Solving OR problems in computing packages such as GAMS, AMPL, OPL, available for programming environments such as Python.

(g) Assessment method

Assessment will be in form of tests and assignments (40%) and final examination (60%)

(h) Reading List

1. Hamdy A. Taha Operations Research : An Introduction, 2015
2. Frederick Hillier Introduction to Operations Research . 10th Edition

(a) Description

The Computer Science Project 1 is a 5 credit unit course that aims at providing a platform for students to combine knowledge and skills acquired from all course modules taught throughout the course in the production of a suitable Computer science project. The project is not a taught module but a major design and development exercise for the student carried out under supervision.

The students in this course will select a project topic from an area of computer Science that interest them, conduct a semester worth of individual/group study of that topic, resulting into a substantial written proposal and software model or solution demonstrating initial preliminary results from the study. The project should be of a problem solving nature, typically providing a software solution or a model to a practical computing problem

The project should also be able to produce an end product for users. Further, theoretical essay, literature search, or a descriptive evaluation would not be acceptable.

(b) Aims:

The aims of the course are:

• To give experience of undertaking a significant individual/group design and development exercise from conception through to design, implementation and delivery.
• To allow students to conduct a substantial piece of research into unfamiliar area of computing
• To introduce research methods and skills relevant for conducting such a project.
• To provide an opportunity for the students to synthesize what they have learned from other modules
• To teach students planning, scientific writing and problem solving skills.
• To give students experience of working independently.
• To give students experience in writing up and presenting work in a scholarly fashion.

(c) Learning outcomes

By the end of the subject, students should be able to:

• A general understanding of the state of the art in the student’s chosen research area of specialization.
• Have an in-depth knowledge and understanding of the student’s chosen area of specialization
• have an understanding of the research methods required in conducting a research based project.
• Plan and carry out research appropriated to a project
• Demonstrate project planning, scientific writing and problem solving skills.
• Manage personal learning and exercise critical self-evaluation.
• Have effective communication of general and specialized Computing Science concepts

(d) Mode of Delivery

The course is a self-study/group-study research exercise for the students carried out under supervision.

(e) Detailed Course content

Since the project is not a taught module but a major design and development exercise for the student carried out under supervision; students are expected to select a research project from the topic of interest. The following are some of the topics students can choose from

– – – – – Artificial Intelligence – – Image Processing – Bioinformatics – Operating systems – Multimedia computing – Computer vision – Algorithms & complexity

• Internet of Things (IoT)
• Big Data/Data science
• Semantic Web
• Machine Learning
• Artificial Intelligence
• Data Mining
• Image Processing
• BioInformatics
• Operating Systems
• Multimedia Computing
• Computer Vision
• Algorithms and Complexity

(f) Assessment method

A detailed project proposal is submitted to the supervisor in the 14th week of the first semester for 40% (combined with student participation). A poster presentation of preliminary results of the project and a well written project proposal are presented to a panel of examiners for 60%.

(g) Reading List

1. Hossein Hassani. How to do the Final Year Projects. A Practical Guideline for Computer Science and IT Students.
2. Ranjit Kumar. Research Methodology a step-by-step guide for beginners 3 rd Edition, 2014
3. C.R. Kothari. Research Methodology methods and & techniques Second Edition, 2004.

(a) Description

The course introduces the principles of user interface development, focusing on design, implementation and evaluation.

(b) Aims:

The aim of the course is to give students experience in

• Training students in order for them to create efficient, flexible, usable and interactive User Interfaces (UI)
• Providing students with the skills so that they are able identify system users, the tasks they want to carry out and the environment in which they will be working.
• Exposing students to standard conceptual designing
• Instructing students in the art of evaluating UIs;
• Enable students create useful User interfaces, in particular graphical user interfaces.

(c) Learning outcomes

By the end of the subject, students should be able to:

• Develop efficient, flexible, usable and interactive User Interfaces(UI)
• Identify system users, the tasks they want to carry out and the environment in which they will be working;
• Create standard conceptual designs
• Create useful User interfaces, in particular graphical user interfaces.
• Evaluate UIs

(d) Mode of Delivery

The study mode will by blended learning including Lectures, Lab sessions, Online learning management systems, Class discussions and presentations, and Group project-based assignments

(e) Detailed Course Content

• Usability, Design Principles and User-Centered Design 6 Hours
• UI Software Architecture, Human Capabilities 6 Hours
• Output Models, Input Models, Conceptual Models and Metaphors 6 Hours
• Designing for collaboration and communication 6 Hours
• Emotional interaction 6 Hours
• The process of interaction design 6 Hours
• Design, prototyping and construction 6 Hours
• User Testing, Experiment Design and Analysis 6 Hours
• Graphic design
• Ui evaluation

(f) Assessment method

Students will submit the signed Proposal to the department. The department will appoint a supervisor who will work with the students as they come up with their Proposal. Students will be required to present their work and answer any questions from the supervisor.

(g) Reading List

1. Rogers, Y., Sharp, H., & Preece, J. Interaction design: beyond human computer interaction, 2011
2. Norman, D. A. The design of everyday things: Revised and expanded edition. Basic books, 2013.

(a) Description

The course provides an introduction to the theory and practice of computer graphics; specifically it introduces the basics of 2- and 3-dimensional computer graphics where both the theoretical principles and methods will be taught and how they can be applied using graphical software libraries. More to that students will learn about the theoretical underpinnings of computer graphics and practical know-how needed to quickly and effectively apply these techniques to new problems

Topics will include: Introduction to Computer Graphics and OpenGL, Basic Programming Techniques using OpenGL, graphics systems, graphics devices and their control, 2D graphics, Color Systems and Shading,geometric object, Transformation and Viewing, Objects Modeling and Visible Surface Detection, clipping and hidden surface removal, Lighting, Surface Rendering, Basic Ray Tracing Algorithms, Applying Ray Tracing Techniques,

(b) Aims:

• To Identify and explain the core concepts of computer graphics
• To Apply graphics programming techniques to design, and create computer graphics scenes.
• To create effective OpenGL programs to solve graphics programming issues, including 3D transformation, objects modeling, colour modeling, lighting, textures, and ray tracing.

(c) Learning outcomes

On successful completion of this course students will be able to.

• derive and apply geometric view and projection models and transformations of homogeneous coordinates in computer graphics, like transformations of 3D objects, transformations between object-world-camera coordinate systems, and perspective and parallel projections.
• describe the data flow in a graphics rendering system
• describe how lines, surfaces and in some cases curves can be represented by polygons and parametric curves, as well as be able to derive the definition and use these representations
• derive and apply basic rendering techniques and algorithms in polygonoriented computer graphics such as lightning models, line and polygon cutting algorithms, and how to treat hidden surfaces.
• describe and relate various visual effects such as antialiasing, texture mapping, bump mapping, and displacement mapping.
• demonstrate their ability to use modern 3D computer graphics techniques, models, and algorithms to solve graphics problems.

(d) Mode of Delivery

• Teaching will be conducted using blended learning approaches including lectures, tutorials, quizzes,
• Assignments and project work: The best way to learn about a computer graphics method is to program it yourself and experiment with it. So the assignments will generally involve implementing computer graphics algorithms, and experimentation to test your algorithms on some data. The students may asked to summarize their work, and analyze the results, in brief (3-4 page) write ups. The implementations will be done in OpenGL.

(e) Detailed Course content

• Module 1: Introduction to computer graphics, problem domain and application. (3 hours)
• Module 2: Coordinate systems and Transformations. (9 hours) The module discusses how various graphics can be changed into some thing else by applying a set of rules or transforms. The module will discuss 2D and 3D transforms, geometric operations and hierarchical 3D transformations.
• Module 3: Building and representing. (09 hours) This module will discuss various ways of representing 3D objects or surfaces in computer graphics. We will discuss data sources and acquisition, modeling software and representation schemes.
• Module 4: The Computer graphics pipeline and the OpenGl API for 3D computer graphics. (3 hours)
• Module 5: Rendering . (9 hours) We discuss various ways of rendering objects. These will include incremental shading algorithm, Hidden Surface removal algorithms, the Z-buffer and the Ant-Anti-aliasing algorithms .
• Module 6: Simulating 3D Reality. (9 hrs) This module discuss different ways of simulating real-world objects in computer graphics. We will discuss light object interaction, texture and shadows.
• Module 7: Introduction to Computer Animation. (3 hours)

(f) Assessment method

Assessment will be by continuous assessment through programming assignments (20%) and written assignments and quizzes (20%) and final exam (60%).

(g) Reading List

1. Peter Shirley and Steve Marschner. Fundamentals of Computer Graphics. Third Edition, 2009.
2. Edward Angel and Dave Shreiner . Interactive Computer Graphics A top-down Approach with shader-based OpenGL. 6th edition, 2012.
3. J. F. Hughes and A. van Dam and M. McGuire and D. F. Sklar and J. D. Foley and S. K. Feiner and K. Akeley Computer Graphics Principles and Practice. 3rd Edition 2014.

(a) Description

This course provides an introduction to system modeling using both computer simulation and mathematical techniques. A range of case studies are examined in the lectures and project exercises. Students will get hands-on training. The application areas considered are wide-ranging, although the emphasis is on the analysis of a variety of modeling paradigms such as queuing and dynamic systems. A simulation language will be utilized and an applied project carried out.

(b) Aims:

• Acquire basic knowledge about simulation and modeling
• Develop critical thinking and analytical skills graphics scenes.
• To be able to solve real world problems which cannot be solved strictly by mathematical approaches using Modeling and Simulation.

(c) Learning outcomes

On successful completion of this course students will be able to.

• Demonstrate basic knowledge about simulation and modeling
• Exhibit critical thinking and analytical skills
• Display skills of building simulation models and how to model and simulate a variety of management-related problems.
• Manifest competence in solving real world problems which cannot be solved strictly by mathematical approaches using Modeling and Simulation.

(d) Mode of Delivery

Blended learning including Lectures, lab practicals, online learning management systems, class discussions and presentations

(e) Detailed Course content

• Introduction to simulation concepts : simulation, models, benefits of using simulation, application areas, model classifications, types of simulations, benefits and drawbacks of simulation, Pitfalls to successful completion of simulations.(3 Hours)
• Module 2: Coordinate systems and Transformations. (9 hours) The module discusses how various graphics can be changed into some thing else by applying a set of rules or transforms. The module will discuss 2D and 3D transforms, geometric operations and hierarchical 3D transformations.
• Introduction to systems- static and dynamic; discrete and continuous, Introduction to Monte Carlo simulation, generation of randomm numbers using Excel RAND () as well as various distributions such as, Bernoulli, Exponential. (6 hours).
• Discrete Event Simulation (DES): components of DES, overview of Queuing Theory, queuing systems, queuing models, queuing discipline (LIFO, FIFO, SIRO, priority queue), analysis and performance measures of queuing systems, overview of probability distributions (such as Markovian, deterministic) used in queuing models. (6 Hours)
• Hand simulation exercises employing single/multi-channel, single/multiphase server systems (6 Hours)
• Simulation Development Life Cycle: problem formulation, system investigation, model formulation, model representation, programming, design of experiments, experimentation, presentation of simulation results, verification and validation. (6 Hours)
• Graphical Integration: basic principles and application of graphical integration, constant rates, inflows and outflows, step functions. (6 Hours)
• Introduction to dynamic simulation models: Stock and Flow diagrams, STELLA modeling environment. Exercises involving modeling with STELLA, interpretation of graphs, creating user interfaces and them to the models. (6 Hours)
• Undertake DES and dynamic simulation field projects. (6 Hours)

(f) Assessment method

Course work (tests, assignments) 20 % , Projects 20 % and Final written exam: 60%

(g) Reading List

1. Principles of Modeling and Simulation: A Multidisciplinary Approach Sokolowski, J.A. & Banks, C.M. (2011)). John Wiley & Sons. ISBN: 978- 0-470-28943-3.
2. Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains. Sokolowski, J.A. & Banks, C.M. (2010). Wiley & Sons. ISBN: 978-0-470-48674-0.
3. Theory of Modeling and Simulation, (2nd ed) Zeigler, B.P., Praehofer, H., & Kim, T.G. (2000)
4. Simulation Modeling and Analysis Law, A.M & Kelton, W.D. (2000). , (3rd ed)
5. Simulation With Arena Kelton, W.D., Sadowski, R.P., & Sturrock, D.T. (2006) (4th ed) Mc-Graw Hill

(a) Pre-requisites: Computer Networks

(b) Course Description

This is a theoretical course that covers the fundamentals of data communication, Formatting and transmission of digital information over various media. The topics covered include digital versus Analog transmission, transmission media, data encoding, integrated Services Digital Networks (ISDN), Network Access Protocols, LAN standards among others.

(c) Aims:

The aims of the course are:

• To provide a solid basis on the theoretical and practical understand of data communication networks
• To get practical experience of the physical phenomenon that can be used to transmit digital information
• To familiarize the students with the basic taxonomy and terminology of the data communications area
• To impact knowledge and skill relevant for the design, implementation and maintenance of modern computer communication networks
• To introduce students to emerging technologies in data communication.

(d) Learning outcomes

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

• Learn the design principles of communication networks.
• Understand the physical phenomenon that can be used to transmit digital information.
• Describe theoretical concepts of computer communication systems, including standards, protocols, and infrastructure devices e.g. routers and switches.
• Design, install and manage a simple Local Area Network or A campus Wide Area Network
• Describe emerging communication technologies particularly wireless and fiber optic technologies
• Describe emerging research directions in computer communication networks
• Work professionally in the area of computer networks and related data communication fields

(e) Mode of Delivery

This course will be delivered through class lectures, discussions. Simulations will also be used to explain abstract concepts.

(e) Detailed Course Content

• Module 1: Introduction to Data Communications: (4 CH) What is communication, uses of communication; General block diagram of communication system, types of communication, Data communications, Applications of data communications Data Communications and Networking for Today’s Enterprise, A Communications Model, Networks, Internet.
• Module 2: Data Transmission:(8 CH) Data Transmission: Fourier analysis, Band limited signals, The communication channel, Maximum data rate of a channel, Electromagnetic spectrum, electromagnetic waves, frequency and wave length, bandwidth, bandwidth and channel capacity, Modulation, types of Modulation, Concepts and Terminology, Analog and Digital Data Transmission, Transmission Impairments, Channel Capacity
• Module 3: Transmission Media:(8 CH) Transmission Media: Guided Transmission Media, Wireless Transmission, Wireless Propagation, Line-of-Sight Transmission, Optical Fibre -Physics & velocity of propagation of light, Advantages & disadvantages, unguided media: Electromagnetic polarization, attenuation and absorption, optical properties of radio waves, terrestrial propagation of electromagnetic waves, skip distance free-space path loss, microwave, infrared & satellite communication system.
• Module 4: Signal Encoding and Data Communication Techniques: (20 CH) Digital Data, Digital Signals; Digital Data, Analog Signals; Analog Data, Digital Signals; Analog Data, Analog Signals. Digital Data Communication Techniques: Digital communication, advantages of digital communication, Nyquist theorem ,Sampling Theory, Analog to digital conversion -Pulse Code Modulation (PCM), Delta modulation (DM); encoding of digital signals, Multiplexing and Modulation of Digital Signals, digital radio, digital amplitude modulation, frequency shift keying (FSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), band width efficiency, carrier recovery, differential phase shift keying,(DPSK), clock recovery, probability of error & bit error rate, trellis encoding, Asynchronous and Synchronous Transmission.
• Module 5: Spread Spectrum: (8 CH) The Concept of Spread Spectrum, Frequency Hopping Spread Spectrum, Direct Sequence Spread Spectrum, Multiple Access- Random Access, AlohaCarrier Sense Multiple Access (CSMA)- Carrier Sense Multiple Access with Collision Detection (CSMA)- Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), - Frequency- Division Multiple Access (FDMA), Time - Division Multiple Access (TDMA), - Code - Division Multiple Access (CDMA).
• Module 6: Local Area Network Overview and ISDN:(12 CH) Background, Topologies and Transmission Media, LAN Protocol Architecture, Bridges, Hubs and Switches, Virtual LANs, integrated services digital network (ISDN), Network Access Protocols; Passive versus dynamic allocation. Ethernet: Traditional Ethernet, High-Speed Ethernet, IEEE 802.1Q VLAN Standard. LAN standards:802.3 (Ethernet),802.4 (token bus), 802.5 (token ring); Wireless LANs: Overview, Wireless LAN Technology, IEEE 802.11 Architecture and Services, IEEE 802.11 Medium Access Control, IEEE 802.11Physical Layer, IEEE 802.11 Security Considerations.

(f) Assessment method

(Assignments, Tests, Group Research course work) 40% Final Examination 60%

(g) Reading List

1. Behrouz A Forouzan, Data Communications and Networking, 5th Edition (2012)
2. Kurose, JF & Ross, Computer Networking: A Top Down Approach, 4th edn, Addison-Wesley, 2007
3. William Stallings, Data and Computer Communications, 7th Edition (2003).
4. David Stamper et al, Business Data Communications, 6th Edition, 2003, Prentice Hall.

(a) Description

Computer security is a branch of technology concerned with digital security or information security applied to computers. Since the largest part of the computer that users interact with is software, computer security pays big attention to development of secure software. The course gives tools for achievingparticularsecuritygoals, attacks, and countermeasures, an overview of security issues for software, and provides programming methods for the development of secure applications

(b) Aims:

• the need for computer security.
• threats faced by computers in the connected digital world.
• techniques that are used to protect computers against various threats.

(c) Learning outcomes

By the end of the subject, students should be able to:

• Describe Access control mechanisms
• Describe digital security principles
• Apply computing forensic techniques using open-source tools

(d) Mode of Delivery

The teaching pattern is by blended learning including lectures, lab sessions and group projects.

(e) Detailed Course Content

• Module 1: Overview of Computer Security (4 CH) Digital security principles - Confidentiality, Integrity, Availability; threats; goals of security; social engineering, Usability; Security engineering; Security architecture.
• Module 2: Security policies (8 CH) Confidentiality policies - Bell-LaPadula Model; Integrity Policies - Biba Integrity Model, Clark-Wilson Integrity Model; Hybrid Policies - Chinese Wall Model; Multilevel security;
• Module 3: Authentication and Access Control (6 CH) Basics, Identity representation; Passwords; Challenge-Response; biometrics; location; access control mechanisms and types; access control lists; access control matrix; capabilities.
• Module 4: Software Security (8 CH) Security by design - Design Principles; Secure coding; Malicious logic; Proof carrying code; confinement problem - virtual machines and sandboxes; Building Secure and trusted systems; Buffer overflow exploits; Securing web applications
• Module 6: Risk Management (6 CH) Risk and threat modeling; vulnerability analysis. Compliance: Audits reviews and inspection, Vulnerability scanners, Penetration testing
• Module 7: Network and Cloud Security (5 CH) Intrusion detection; mobile phones; database security; web security; attacks and prevention.
• Module 8: Computer Forensics (8 CH) File system analysis; malware analysis; mobile and Internet forensics. Have a practical session on this.

(f) Assessment method

Assessment will be by assignments and/or tests (40%) and written examination (60%)

(g) Reading List

1. Ross J. Anderson. Security Engineering: A Guide to Building Dependable Distributed Systems, Willey 2001.
2. Dan Farmer and Wietse Venema Forensic Discovery, Addison-Wesley, 2005.
3. K. M. Jackson and J. Hruska and Donn B. Parker. Computer security reference book, 1992
4. Matt Bishop Introduction to Computer Security. Addison-Wesley, 2005

(a) Description

In this course unit, students shall understand the complete process of translating a program in a high-level language to machine language. The course gives an introduction to the design and implementation of a compiler with emphasis on principles and techniques for program analysis and translation. It also gives an overview of the tools for compiler construction. Topics covered include lexical and syntactic analysis, semantic analysis, type-checking, program analysis, code generation and optimization, memory management, and runtime organization.

(b) Aims:

The aim of the course is to give students experience in:

• Expressing a programming language formally
• The analysis and synthesis of a compiler
• The different phases of a compiler
• How to optimize the machine code generated by the compiler to make it faster and more efficient
• The basic skills needed to design and implement a compiler of a given language
• Utilizing compiler construction tools in the process of building a compiler

(c) Learning outcomes

By the end of the subject, students should be able to:

• Describe the complete process of translating a program in a high level language to machine language
• Describe the design and implementation of a compiler
• Describe the principles and techniques for program analysis and translation
• Identify the tools for compiler construction

(d) Teaching and Learning Pattern

The course consists of a Blended learning including lectures, projects, and tutorials. The lecture component introduces the basic concepts of compiler writing. The project component will involve students in writing a compiler for a specified programming language.

(e) Detailed Course Content

• Module 1: Introduction (3 CH) Why study compiler design, Language translators, Compilers versus interpreters, and the application of compilation techniques in other Computer Science fields. They will also have an overview of the phases of the compiler from analysis to synthesis phases
• Module 2: Lexical analysis (4 CH) The lexical analysis of the compiler will be studied including lexical errors, scanner implementation, regular expressions, finite automata review (nondeterministic and deterministic FA, conversions), lexical analyzer generators.
• Module 3: Syntax Analysis (10 CH) Basics of syntax analysis, review of context free grammars, top-down parsers (recursive descent parsers, predictive parsers, LL parsers), FIRST and FOLLOW sets, parsing tables. Bottom-up parsers (LR parsers, Conflicts in LR grammars and how to resolve them).
• Module 4: Semantic analysis (10 CH) Basics of semantics analysis, semantic errors, syntax directed translations, formal semantics, attribute grammars (attributes and their computation), type checking (static versus dynamic) and type inference, type equivalence (name versus structural), scope, inherited attributes and symbol tables. Overload resolution, polymorphism and dynamic dispatching
• Module 5: Run-time organization and Intermediate code generation (4 CH) Storage allocation(Static Versus Dynamic), Stack Allocation of Space, parameter passing mechanisms, heap management, garbage collection. ThreeAddress Code, control flow, register allocation.
• Module 6: Code optimization (4 CH) Classification, scope of optimization, machine independent versus machine dependent optimization, Principal Sources of Optimization, local versus global optimisation
• Module 7: Compiler Project (10 CH) Hands on implementation group project of a simple compiler illustrating the different phases of the compilation process. There will be presentations of this project thereafter.

(f) Assessment method

Assessment will be by quizzes, assignments and/or tests (40%) and written examination (60%)

(g) Reading List

1. Alfred V. Abo, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman Compilers: Principles, Techniques, and Tools (2nd Edition) 2007.
2. William .M. Waite and Gerhard Goos Compiler Construction. Springer, 1984.
3. Kenneth C. Louden Compiler Construction: Principles and Practice 1st Edition. Course Technology, 1997
4. Y.N. Srikant and Priti Shankar, The Compiler Design Handbook: Optimizations and Machine Code Generation, Second Edition, 2007 CRC Press, 2007.
5. Keith D. Cooper and Linda Torczon Rice Engineering a Compiler, Second Edition, 2011

(a) Description

The Computer Science Project 2 is a continuation of Computer science project 1 which also aim at providing a platform for students to combine knowledge and skills acquired from Computer Science modules studied in the course in the production of a suitable Computer science project. The course is not a taught module but a major design and development exercise for the student carried out under supervision.

In the course, the students continue developing their project topics they selected in Computer Science Project 1, resulting into a substantial well written project report/dissertation and fully operational/working software solution or model that can be used by the end product users.

(b) Aims:

The aims of the course are:

• To give experience of undertaking a significant individual/group design and development exercise from conception through to design, implementation and delivery.
• To allow students to conduct a substantial piece of research into unfamiliar area of computing.
• To introduce research methods and skills relevant for conducting such a project.
• To provide an opportunity for the students to synthesize what they have learned from other modules.
• To teach students planning, scientific writing and problem solving skills
• To give students experience of working independently
• To give students experience in writing up and presenting work in a scholarly fashion.

(c) Learning outcomes

By the end of the subject, students should be able to:

• A general understanding of the state of the art in the student’s chosen research area of specialization
• Have an in-depth knowledge and understanding of the student’s chosen area of specialization.
• have an understanding of the research methods required in conducting a research based project.
• Plan and carry out research appropriated to a project.
• Demonstrate project planning, scientific writing and problem solving skills.
• Manage personal learning and exercise critical self-evaluation.
• Have effective communication of general and specialized Computing Science concepts

(d) Mode of Delivery

The course is a self-study/group-study or research exercise for the carried out under supervision.

(e) Detailed Course Content

Since the course is not a taught module but a major design and development exercise for the student carried out under supervision; and students are expected to select a research project from the topic of interest. The following a some the topics students can choose from

• Data Warehousing
• Internet of things
• Big data/ Data Science
• Security architecture
• Semantic Web
• Machine Learning
• Operating Systems
• Multimedia Computing
• Artificial Intelligence
• Data Mining
• Image Processing
• Bioinformatics
• Computer Vision
• Algorithms and complexity

(f) Assessment method

A detailed project report/dissertation is submitted to the supervisor in the 14th week of the second semester for 40% (combined with student participation). A presentation of the results of the project is and well written report/dissertation are assessed by a panel of examiners for the remaining 60%

(g) Reading List

1. Hossein Hassani. How to do the Final Year Projects. A Practical Guideline for Computer Science and IT Students.
2. Ranjit Kumar. Research Methodology a step-by-step guide for beginners 3 rd Edition, 2014.
3. C.R. Kothari. Research Methodology methods and & techniques Second Edition, 2004.

(a) Description

The course is to expose provide students with an opportunity to search for knowledge in an area of interest. It is to allow a student do lightweight research and explore the current trends in a certain computer science area

(b) Aims:

The aims of the course are:

• aid a student get an in depth understanding of the developments in at least one area of computer science
• improve the students research skills
• develop confidence in the students on the ability to search for knowledge with little guidance
• improve students ability to present and report findings from self study on new technologies and trends

(c) Learning outcomes

By the end of the course, the student should be able to:

• Discuss the various areas in computer science that are potential areas of research
• Demonstrate skills acquired from topics of self study.

(d) Mode of Delivery

The study will be blended learning including a mix of student centered and learning teacher centered pedagogy. Students will be grouped in theme areas which will be covered by a set of staff. Staff will guide students on the specific themes/topics in the area as well as where to search for information. Staff will also address experiences and hardships found.

(e) Detailed Course Content

The content is not specific but will be dependent on the area the student chooses to pursue

(f) Assessment method

Assessment will be include:

1. Class presentations and take home projects will constitute progressive assessment (40%)
2. Final examination that will constitute 60%. The examination will include student presentation, reports, demonstrations about the selected study area

(g) Reading List

1. This will be decided by the lecturer in consultation with the Course leader and dependent on the selected study area/topic.

(a) Description

This course gives students theoretical and practical skills on development of distributed systems and applications. This includes distributed-system specific challenges like reliability and robustness. Protocols like Remote Mothod Invocation and Remote Procedure Calls should be covered using languages like Java and C respectively

(b) Aims:

The aim of the course is to equip students with skills of developing distributed systems

(c) Learning outcomes

By the end of the course, the student should be able to:

• Describe Event driven Architectures
• Describe the development, documentation and testing of distributed applications
• Describe the techniques of reusable, extensible and efficient software systems
• Discuss maintainability and concurrence in distributed systems
• Describe abstraction based on patterns and object-oriented techniques

(d) Mode of Delivery

Teaching will be in form of class lectures, tutorials, lab demonstrations as well as class presentations.

(e) Detailed Course Content

• Module 1 (16 hours): Distributed Objects
  • Event-driven software architectures
  • Distributed object computing (RPC and RMI)
• Module 2 (24 hours): Distributed Applications
  • Development, documentation and testing of distributed applications
  • Techniques for reusable, extensible and efficient software systems
  • Practical project(s)
• Module 3 (20 hours): Advanced techniques
  • Maintainability and concurrence in distributed systems
  • Information Security in Network Based Applications

(f) Assessment method

• To assess whether students have met the learning outcomes, there will be course work that will contribute 40% to final grade and a final exam that will constitute 60% to the final grade
• The coursework will comprise at least one (practical) assignment and at least one (written) test.
• The Final exam will be done at the end of the semester and will take a duration of three (3) hours.

(g) Reading List

1. Distributed Systems Concepts and Design by George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair, Fifth Edition by Addison Wesley, May 2011, ISBN: 0-13-214301-1
2. S. Tanenbaum and M. V. Steen, Distributed Systems: Principles and Paradigms, Second Edition, Prentice Hall, 2006, ISBN: 0132392275
3. G. Coulouris, J. Dollimore, and T. Kindberg, Distributed Systems: Concepts and Design, 3rd Edition, Addison-Wesley, 2000, ISBN: 0201619180
4. R. Anderson, Security Engineering: A Guide to Building Dependable Distributed Systems, John Wiley & Sons, 2001, ISBN: 0471389226

(a) Description

This course provides candidates with practical and theoretical skills in data and information management technologies so as to provide decision support capabilities. The course provides skills on building of data warehouses, managing and querying data warehouses and utilization of business intelligence for decision-making. Modeling techniques for futuristic prediction is also covered.

(b) Aims:

The aims of the course are:

• Make students understand the role and operation of data warehouses
• Equip students with skills of developing data warehouses
• Equip students with skills of maintaining existing data warehouses
• Equip students with skills of manipulating data warehouses to generate information for business decision making

(c) Learning outcomes

By the end of the subject, students should be able to:

• Explain how data warehousing combined with good business intelligence can increase a company’s bottom line and the different forms of business intelligence. Distinguish between on-line transaction processing (OLTP) and online analytical processing (OLAP); describe how structured, semistructured, and unstructured data are all essential elements of enterprise information and knowledge management.
• Create a simple data warehouse (“data mart”) and develop dimensional models from which key data for critical decision-making can be extracted
• Load extracted and transformed data into the data warehouse
• Understand the different kinds of data mining algorithms and sketch out the process for extracting data from disparate databases and data sources.

(d) Mode of Delivery

There will be blended learning including Lectures, Lab practicals, Online learning management systems, Class discussions and presentations, and Group projectbased assignments

(e) Detailed Course Content

• Module 1 (16 hours): Basic concepts
  • Data warehouse concepts: Partitioning, Granularity, Record of source, and Metadata
  • Building viable decision support environments
• Module 2 (24 hours): Data manipulation
  • Architect development
  • Use of operational data stores, and transactional systems
  • Data migration and integration
• Module 3 (20 hours): Online analytics
  • Online analytic processing
  • ROLAP vs MOLAP
  • Data Mining

(f) Assessment method

The assessment will be done by tests (20%), take home practical assignments (20%), and a final written examination (60%)

(g) Reading List

1. Ponniah, P. (2011). Data warehousing fundamentals for IT Professionals, 2nd Edition. Publisher: John Wiley & Sons.
2. Haertzen, D. (2012). The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics. Publisher: Technics Publications Llc.
3. Khan, A. (2011). Business Intelligence and Data Warehousing Simplified: 500 Questions, Answers, & Tips. Publisher: International Pub Marketing.
4. Taniar, D. (2009). Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics. Illustrated Edition. Publisher: IGI Global Snippet.
5. Roland Bouman, R. (2009) Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL.