GR101EL Self Learning Skills
(3) Credit Hours
This is a general course that aims only to help students to know
more about university life. It also introduces the four academic
skills students use in their everyday studies
Course Code |
GR101EL |
Course Title |
Self Learning Skills |
Pre-requisite |
|
Credit Hours |
3 |
Course Description |
This is a general course that aims only to help students to know
more about university life. It also introduces the four academic
skills students use in their everyday studies |
Course Objectives |
GR101EL aims to help students manage their own success. It does this by:
- Preparing students for what to expect from higher education (university or college)
- Encouraging students to think about the skills they already have acquired, and which they will need now as students and later in their professional life
- Providing resources to help university students evaluate, reflect upon and manage their own learning
- Making suggestions on how to develop positive approaches and good study habits
- Helping students to understand more about how learning, intelligence and memory work, and how to develop critical and analytical thinking styles
- Encouraging university students to understand that success is not only being clever and getting good marks; but it is also about using the skills they will always learn throughout life.
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Course Outcomes |
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GR101 Self-Learning Skills
(3) Credit Hours
عرض هذا المقرر سبل تطوير مهارات التعلم الذاتي ويهيئ الطلبة للدراسة الجامعية عن طريق التركيز على المهارات المطلوبة من إدارة الوقت و العادات الدراسية الجيدة. وتشتمل تدريبات المقرر على نماذج نقدية وتحليلية وعرض لأنماط التعلم المختلفة.
Course Code |
GR101 |
Course Title |
Self-Learning Skills |
Pre-requisite |
|
Credit Hours |
3 |
Course Description |
عرض هذا المقرر سبل تطوير مهارات التعلم الذاتي ويهيئ الطلبة للدراسة الجامعية عن طريق التركيز على المهارات المطلوبة من إدارة الوقت و العادات الدراسية الجيدة. وتشتمل تدريبات المقرر على نماذج نقدية وتحليلية وعرض لأنماط التعلم المختلفة. |
Course Objectives |
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Course Outcomes |
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M109 .NET Programming
(3) Credit Hours
This module is intended to introduce and present the fundamental skills that are required to design and develop object-oriented programs and applications in .NET Framework
Course Code |
M109 |
Course Title |
.NET Programming |
Pre-requisite |
- |
Credit Hours |
3 |
Course Description |
This module is intended to introduce and present the fundamental skills that are required to design and develop object-oriented programs and applications in .NET Framework |
Course Objectives |
- To understand the .NET framework architecture.
- To provide students with a range of skills to analyze a problem and construct a .NET program that solves it.
- To provide the principles of object oriented programming.
- To implement object-oriented concepts in .NET environment.
- To understand the Visual Studio Integrated Development Environment
- To develop .NET applications using the selected programming language.
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Course Outcomes |
A. Knowledge and understanding
- Explain .NET Platform.
- Describe data types, variables, constants, operators and built-in functions in the selected .NET programming language.
- Discuss decision-making and looping statements.
- Explain object oriented concepts such as classes, objects and methods.
- Describe the features of object oriented programming such as Inheritance and Polymorphism.
- Explain the concept of arrays.
- Identify errors and different types of exceptions in a .NET program.
B. Cognitive skills
Upon completing this module, students will be able to: - Develop appropriate programs in .NET framework.
- Apply object oriented concepts in .NET framework.
- Test and debug a .NET program
C. Practical and professional skills
Upon completing this module, students will be able to: - Develop programming skills in .NET platform.
- Use variables, constants, operators, built-in functions, methods and arrays in a .NET program.
- Write codes in a .NET programming language that make use of structured programming constructs of sequence, selection and repetition.
- Apply classes, objects and other object oriented concepts such as inheritance and polymorphism in a .NET program.
- Test and debug .NET programs.
- Use the Visual Studio IDE to build .NET applications using the selected .NET programming language.
D. Key transferable skills
Upon completing this module, students will be able to: - Collaborate effectively within a group using electronic conferencing techniques.
- Facilitate discussions in a conference.
- Develop self- learning and performance.
- Discuss about testing strategies, design, and code.
- Use electronic media (the web and electronic conferencing) for information retrieval and communication.
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M110 Python Programming
(8) Credit Hours
This module is an introductory level programming module that is meant to provide foundation in computer programming to students. Students will learn how to develop solutions (algorithms) using pseudocode to solve problems. Thereafter, students will learn how to implement these solutions using a programming language (Python). This module serves as foundation for higher level modules.
Course Code |
M110 |
Course Title |
Python Programming |
Pre-requisite |
EL111 |
Credit Hours |
8 |
Course Description |
This module is an introductory level programming module that is meant to provide foundation in computer programming to students. Students will learn how to develop solutions (algorithms) using pseudocode to solve problems. Thereafter, students will learn how to implement these solutions using a programming language (Python). This module serves as foundation for higher level modules. |
Course Objectives |
This module aims to: - Help student to develop their understanding of the available techniques of designing / solving different problems using pseudocode.
- Explore a variety of algorithmic thinking and problem-solving skills using examples from everyday life.
- Enhance student’s knowledge about implementing solutions to problems in a visual programming using Python.
- Provide the students with the required skills to possess the programming skills.
- Prepare the student for further academic study by helping him develop his study skills.
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Course Outcomes |
A. Knowledge and understanding | After studying the module, learners will be able to: - Understand the design and programming processes.
- Know how to implement solutions to problems using Python programming language.
- Understand the techniques used in developing a medium Python application.
- Understand of the basic data structures.
- Appreciate the implications of object-oriented software analysis and design.
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B. Cognitive skills | At the end of the module, learners will be able to: - Describe and apply key concepts and techniques in software design and development.
- Design and formulate an appropriate solution to a problem and evaluate it.
- Deal professionally with the basic data structures.
- Carry out a project in computing and IT that applies and extends student's knowledge and understanding, and critically reflect on the processes involved and the outcomes of learner's work
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C. Practical and professional skills | At the end of the module, learners will be able to: - Assemble, program, develop, and evaluate software systems.
- Use software tools for good design and programming practice.
- Use appropriate numerical and mathematical skills to carry out calculations and analyze data.
- Work independently, planning, monitoring, reflecting on and improving learner's own learning.
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D Key transferable skills | At the end of the module, learners will be able to: - Evaluate computing and IT systems, using appropriate simulation and modelling tools where appropriate
- Use a range of resources to help him develop as an independent learner.
- Use information literacy skills, computers, and software packages appropriate to the workplace.
- Use appropriate numerical, mathematical and abstraction skills.
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M251 Object Oriented Programming using Java
(8) Credit Hours
This module is intended to provide students a good understanding of object-oriented principles, including inheritance, polymorphism, class libraries, interacting objects, and the unified modelling language (UML). It uses the JAVA language to illustrate theses principles.
Course Code |
M251 |
Course Title |
Object Oriented Programming using Java |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
This module is intended to provide students a good understanding of object-oriented principles, including inheritance, polymorphism, class libraries, interacting objects, and the unified modelling language (UML). It uses the JAVA language to illustrate theses principles. |
Course Objectives |
- Introduce all aspects of object-oriented principles
- Identifying and implementing class relationships using abstract classes, interfaces and inheritance
- Provide knowledge in using simple UML class diagrams
- Describe how these concepts are implemented in java
- Provide knowledge in how to explore the JAVA API and to develop your own
- Provide the knowledge necessary to construct java programs
- Describe a number of the advanced facilities of java including exceptions
- Show how java can be used in developing non-trivial programs
- Introduce good design and programming practice
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Course Outcomes |
A. Knowledge and understanding
After studying the module, the student will be able to demonstrate: - An understanding of the object-oriented principles
- Some knowledge of the main constructs and mechanisms in Java
- An appreciation of the implications of object oriented software analysis and design
- An understanding of the techniques used in developing a large Java program
B. Cognitive skills
After studying the module, the student will be able to: - Describe and apply key concepts and techniques in software design and development
- Analyze and abstract away from the details of a problem
- Design and formulate an appropriate solution to a problem and evaluate it
C. Practical and professional skills
After studying the module, the student will be able to: - Assemble, program, develop, debug, test and evaluate software systems
- Use software tools such as a Java IDE
- Use good design and programming practice
- Develop and implement class relationships
D. Key transferable skills
After studying the module, the student will be able to: - Find information from a range of sources to support a task
- Plan complex tasks
- Use new Java libraries
- Use appropriate numerical, mathematical and abstraction skills
- Progress to more advanced level studies
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M269 Algorithms, Data structures and Computability.
(8) Credit Hours
One of the basic pillars of advanced computing projects consists of the set of proper algorithms used to solve not only traditional but also unconventional IT problems. With the huge amount of data embedding the new data science, being skilled in setting proper data structure, managing and understanding computability techniques become a must nowadays. M269 is one of the most important modules for information technologies and computing related majors and tracks. The underlying concepts of this module are implemented using the python programming language.
Course Code |
M269 |
Course Title |
Algorithms, Data structures and Computability. |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
One of the basic pillars of advanced computing projects consists of the set of proper algorithms used to solve not only traditional but also unconventional IT problems. With the huge amount of data embedding the new data science, being skilled in setting proper data structure, managing and understanding computability techniques become a must nowadays. M269 is one of the most important modules for information technologies and computing related majors and tracks. The underlying concepts of this module are implemented using the python programming language. |
Course Objectives |
- Provide the students with the required skills to possess the computational thinking. These skills start by proper understanding and analyzing the problems to be solved and end by providing computer programs that solve these problems.
- One of the important aspects of this module is to provide the students with the awareness of the limits of computation and the ability to decide which problems can and which cannot be solved efficiently with computers.
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Course Outcomes |
A. Knowledge and understanding
Upon completing this module, students will be able to: - Identify and define the sets, functions and logic, and their application in the design, implementation and analysis of computer-based systems.
- Define and recognize Data structure and computational problematic.
B. Cognitive skills
Upon completing this module, students will be able to: - Explain, construct and use algorithms and data structures to solve computational problems.
- Describe and assess the difficulty of computational problems.
- Analyse algorithms and computational problems making use of several informal proof techniques
C. Practical and professional skills
Upon completing this module, students will be able to: - Use the Python programming language to implement algorithms.
- Write a short report which is based on one or more sources and which has a well-argued conclusion.
D. Key transferable skills
Upon completing this module, students will be able to: - Apply appropriate computational problem-solving techniques to a range of problems;
- Apply computational thinking skills to solve problems across a range of application areas.
- Discuss the questions 'What is computation?' and 'What are its limits?', and explain how the answers to these questions have important implications for the practical use of computer-based systems.
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MS102 Physics
(3) Credit Hours
An understanding of the physical phenomena underlying the operation of devices involved in information processing and transmission can lead to better understanding of those devices. In addition, software developers of computer games frequently require knowledge of the behavior of physical objects in order to produce realistic games. Finally, as a fundamental science, a good understanding of physics and its techniques will help students develop a better understanding of nature and how to approach studying it. The module has implicit links to computer communication and software development modules, in addition to the final year project.
Course Code |
MS102 |
Course Title |
Physics |
Pre-requisite |
- |
Credit Hours |
3 |
Course Description |
An understanding of the physical phenomena underlying the operation of devices involved in information processing and transmission can lead to better understanding of those devices. In addition, software developers of computer games frequently require knowledge of the behavior of physical objects in order to produce realistic games. Finally, as a fundamental science, a good understanding of physics and its techniques will help students develop a better understanding of nature and how to approach studying it. The module has implicit links to computer communication and software development modules, in addition to the final year project. |
Course Objectives |
- To impart knowledge and understanding of fundamental concepts of physics likely to be needed by the students for later modules and future careers.
- To develop an appreciation of physics' tools and techniques for understanding the real world.
- To develop transferrable problem-solving skills that can be applied in other areas.
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Course Outcomes |
A. Knowledge and understanding Upon completing this module, students will be able to: - Explain the various important units of physics and the concept of dimensional analysis and the representation and manipulation of physical quantities
- Outline the laws of classical mechanics
- Contrast and differentiate among the different types of waves and summarize their properties
- Explain electric forces and fields and summarize their properties
- Illustrate and explain basic passive electric circuits
B. Cognitive skills
Upon completing this module, students will be able to:
- Identify concepts and quantities in physics precisely beyond what is used in everyday language.
- Apply strategies for solving problems in physics in different situations.
- Use vector algebra to the study of mechanics in two dimensions.
- Analyze passive electric circuits.
- Analyze wave propagation in different materials.
C. Practical and professional skills
Upon completing this module, students will be able to: - Use and interpret different types of graphs to display the relationship between variables
- Analyze the forces of static and dynamic bodies in simple mechanical systems
- Calculate the velocity and acceleration of bodies in different types of plane motion
- Determine basic parameters of waves propagating in different materials
- Calculate voltages and currents in passive electric circuits
D. Key transferable skills
Use the learning Management System (LMS) effectively to improve own learning performance. - Demonstrate active participation and contribution to classroom discussions.
- Improve own learning and performance through self-reflection.
- Demonstrate effective communicate about technical matters.
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MT129 Calculus and Probability
(4) Credit Hours
This module introduces the concepts of differentiation and integration as well as some applications of differential and integral calculus. Moreover, the module offers a clear and comprehensive survey of the of data sampling, measurements of central tendency and spread, organizing and visualizing categorical and numerical data. It also includes topics in the basic probability such as events, simple probability, conditional probability, and Bayes’ rule. Finally, it provides an introduction to fundamental basis and concepts of statistical inferences, normal distribution. The module has direct links to computing, programming and communication modules, in addition to the numerical analysis module.
Course Code |
MT129 |
Course Title |
Calculus and Probability |
Pre-requisite |
- |
Credit Hours |
4 |
Course Description |
This module introduces the concepts of differentiation and integration as well as some applications of differential and integral calculus. Moreover, the module offers a clear and comprehensive survey of the of data sampling, measurements of central tendency and spread, organizing and visualizing categorical and numerical data. It also includes topics in the basic probability such as events, simple probability, conditional probability, and Bayes’ rule. Finally, it provides an introduction to fundamental basis and concepts of statistical inferences, normal distribution. The module has direct links to computing, programming and communication modules, in addition to the numerical analysis module. |
Course Objectives |
The module aims to: - Apply the knowledge of elementary functions to calculus concepts.
- To compute the derivative of polynomials, rational, radical, trigonometric, exponential, and logarithmic functions.
- Evaluate the integrals of polynomials, rational, radical, trigonometric, exponential, and logarithmic functions.
- Introduce the terms and concept of probability, and the idea of discrete and continuous random variables.
- Ensure the understanding of mathematical expectations and moment generating functions concepts.
- Equip students with some important discrete and continuous probability distributions in technology and communication modules.
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Course Outcomes |
A. Knowledge and understanding
Students will be able to: - Use derivative rule to find derivatives of power, exponential, logarithmic and trigonometric functions.
- Solve simple definite and indefinite integrals.
- Use applications of differentiation and integration in sketching graphs, obtain area between curves and average value of functions.
- Define and identify random variables for any well- defined probability problems.
- Realize mathematical expectations and variances for different continuous and discrete distribution
B. Cognitive skills
Students should be able to: - Produce descriptions and explanations of the different types of elementary functions and apply their understanding of the studied functions to information systems.
- Display deep knowledge gained from the course and use it to solve optimization problems.
- Utilize knowledge gained from the course to help them to understand new unfamiliar probability distributions.
C. Practical and professional skills
Students will be able to: - Apply the practical skills gained from differential and integral calculus ITC problems.
- Cultivate the capacity to be leaders in their professional and personal communities.
- Develop some technical statistical materials; effectively present and objectively evaluate them.
- Deal with statistical computer applications such as spread sheets and MATLAB statistics toolbox.
D. Key transferable skills
Students will be able to: - Be aware of the implications of information technology in daily lives and on society as a whole, and the ability to utilize IT to communicate and solve problems.
- Use information, reasoning, and creative processes to solve problems and achieve goals.
- Implement global issues gained from module and their implications on their daily lives.
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MT131 Discrete Mathematics
(4) Credit Hours
This is an elementary level module which introduces various topics in discrete mathematics. It offers a clear and comprehensive survey of logic operations, predicates, quantifiers, sets, functions, relations. Also, the module provides the concept of permutations, combinations and counting techniques which are needed as prerequisite in most of technology and communication modules. Moreover, the module gives some knowledge of relevant algorithmic ideas in number theory and cryptography that are widely used in data structure, data base, programming, data communication and in scientific research.
Course Code |
MT131 |
Course Title |
Discrete Mathematics |
Pre-requisite |
- |
Credit Hours |
4 |
Course Description |
This is an elementary level module which introduces various topics in discrete mathematics. It offers a clear and comprehensive survey of logic operations, predicates, quantifiers, sets, functions, relations. Also, the module provides the concept of permutations, combinations and counting techniques which are needed as prerequisite in most of technology and communication modules. Moreover, the module gives some knowledge of relevant algorithmic ideas in number theory and cryptography that are widely used in data structure, data base, programming, data communication and in scientific research. |
Course Objectives |
The course aims to:
- Introduce basic notations used in discrete Mathematics associated with information and communication technology
- Teach the rudiments of elementary mathematical reasoning.
- Prepare students for the theoretical parts of further courses in information technology.
- Explain logic from a mathematical perspective and relating it to computer applications.
- Introduce set theory, relations, functions, graphs, equivalence relations, and partial orderings.
- Provide concepts of permutation, combination and any other counting techniques.
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Course Outcomes |
A. Knowledge and understanding
Students will be able to: - Identify propositional logic, logical equivalence, predicates and quantifiers.
- Describe the Integers and division functions, prime number and prime factorization, least common multiple and highest common factors.
- Define sets, functions and binary relations, their properties and representations. Know the major types of binary relations on a set, equivalence relations and partial orderings.
- Use matrices to represent relations, graphs and trees.
- Recognize basic properties of counting techniques using permutation and combination properties.
B. Cognitive skills
Students will be able to: - Deal with mathematical and logical arguments and carry out mathematical and logical manipulations.
- Acquire a good understanding of the concepts and methods of discrete mathematics described in detail in the syllabus.
- Be familiar with mathematical notations related to computer science.
C. Practical and professional skills
Students will be able to: - Prove any simple mathematical theory using logic laws
- Use any or all of the previous tools in a significant information and communication technology application such as cryptography.
- Apply combinatorial principles and discrete mathematical structures that are central to mathematics and information technology.
D. Key transferable skills
Students will be able to: - Demonstrate study skills at a level appropriate to higher education, such as timetabling study; read critically for meaning and take effective notes; and use study aids such as dictionaries and glossaries;
- Present and communicate basic mathematical and logical arguments; communicate appropriately with their tutor and other students using email and online conferences;
- Locate information on a given subject from the World Wide Web
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MT132 Linear Algebra
(4) Credit Hours
The course introduces a range of ideas concerning matrices and its applications, matrix operations that are widely used in data structure, programming, data communication, digital signal processing and in scientific research. The course shows algorithmic method to solve systems of linear equations. Moreover, it includes concept of vector spaces and subspace that are used to construct algebraic codes. Also, it introduces the meaning of basis and dimension of a subspace the vector space Rn. The concept of linear transformation between two vector spaces together with null space and rank are also included. Finally, the course introduce the idea of characteristic values/vectors and diagonalization.
Course Code |
MT132 |
Course Title |
Linear Algebra |
Pre-requisite |
- |
Credit Hours |
4 |
Course Description |
The course introduces a range of ideas concerning matrices and its applications, matrix operations that are widely used in data structure, programming, data communication, digital signal processing and in scientific research. The course shows algorithmic method to solve systems of linear equations. Moreover, it includes concept of vector spaces and subspace that are used to construct algebraic codes. Also, it introduces the meaning of basis and dimension of a subspace the vector space Rn. The concept of linear transformation between two vector spaces together with null space and rank are also included. Finally, the course introduce the idea of characteristic values/vectors and diagonalization. |
Course Objectives |
The course aims to: - Extend the students' basic mathematical awareness and skills in matrices and matrix operations.
- Give the study skills necessary for students to be able to solve system of linear equations.
- Provide a range of useful ideas such as linear combinations and linear independence.
- Present some important mathematical terms such as span, basis and dimensions.
- Upgrade the concept of linear transformation necessary for other compulsory technology and communication modules.
- Give a feeling for the mathematical approach to the study of computer science.
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Course Outcomes |
A. Knowledge and understanding
Student will be able to: - Define and classify type of matrices and perform matrix operations.
- Solve problems in information systems and communication using matrix techniques.
- Use and apply linear algebra knowledge and concepts to information technologies and computing.
- Be familiar with different terminologies in linear algebra and matrix transformation.
- Acquire technical material, effectively present it and objectively evaluate other technical materials in linear algebra.
B. Cognitive skills
Students should be able to demonstrate that they can: - Produce descriptions and explanations of the different types of matrices and linear operations.
- Apply their understanding of the studied ideas in linear algebra to coding problems, encryption and decryption.
- Use knowledge gained from the module to help them to understand new unfamiliar matrix operations.
C. Practical and professional skills
Students should be able to: - Communicate effectively in English and Arabic in a variety of contexts and media.
- Analyze a mass of information and carry out an appropriate analysis of the problem material.
- Express a problem in mathematical terms and carry out an appropriate analysis.
- Reason critically and interpret information in a manner that can be communicated effectively.
- Integrate and link information across course components.
D. Key transferable skills
Students should be able to demonstrate that they can: - Communicate complex information, arguments and ideas effectively and without plagiarism on a range of topics relating to linear operations.
- Perform calculations to find inverse of a matrix, use and manipulate simple algebraic calculations to solve linear system of equations.
- Use technology to find a span and a basis for a vector space.
- Enhance existing numerical ability.
- Work effectively as part of a group in solving any complicated mathematical problems.
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MT141 Introduction to Probability and Statistics
(4) Credit Hours
MT141 is an introductory module in probability and statistics. It is designed to provide Artificial Intelligence students with the basic concepts of data analysis and statistical computing. Topics covered include basic descriptive measures, measures of association, probability theory, measurement error, random variables, probability distributions, confidence intervals, and hypothesis testing. To study this module, students should have a sound knowledge of level 1 mathematics modules MST129, MT131 and MT132.
Course Code |
MT141 |
Course Title |
Introduction to Probability and Statistics |
Pre-requisite |
EL111 |
Credit Hours |
4 |
Course Description |
MT141 is an introductory module in probability and statistics. It is designed to provide Artificial Intelligence students with the basic concepts of data analysis and statistical computing. Topics covered include basic descriptive measures, measures of association, probability theory, measurement error, random variables, probability distributions, confidence intervals, and hypothesis testing. To study this module, students should have a sound knowledge of level 1 mathematics modules MST129, MT131 and MT132. |
Course Objectives |
The module aims to: - Provide students with a framework that will help them choose the appropriate descriptive methods in various statistical data analysis situations.
- Make acquainted with concepts of random variables and finding appropriate distribution for interpreting data specific to an experiment.
- Impart knowledge and understanding of discrete and continuous probability distributions.
- Develop the understanding of mathematical expectations and moment generating functions.
- Ensure the understanding of testing hypotheses, and estimation.
- Enable students to understand the role of statistics in doing the research.
- Help students to read and understand the statistical concepts from reports and papers.
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Course Outcomes |
A. Knowledge and understanding | At the end of the module, learners will be expected to: - Use a variety of methods for exploring, summarizing and presenting data.
- Have knowledge and understanding of basic probability models and their use for modelling discrete and continuous data.
- Realize the different probability distributions types, their structures and standards.
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B. Cognitive skills | At the end of the module learners will be expected to: - Apply mathematical and statistical manipulation and calculation on choices of model and analyses resulting from them.
- Solve practical problems by formulating problems in a statistical framework and applying appropriate statistical techniques.
- Be able to interpret the results of a statistical data analysis and evaluate statistical evidence.
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C. Practical and professional skills | At the end of the module, learners will be expected to: - Apply mathematical and statistical concepts and principles.
- Analyse and evaluate criteria and specifications appropriate to specific problems and plan strategies for their solutions.
- Use information technology with confidence to acquire and present mathematical and statistical knowledge and data, to model and solve problems and to develop mathematical and statistical insight.
- Demonstrate their understanding of descriptive statistics by practical application of quantitative reasoning and data visualization.
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D. Key transferable skills | At the end of the module, learners will be expected to: - Apply statistical modelling and analysis techniques to a wide range of practical problems.
- Analyse and to evaluate practical problems involving statistical data and plan strategies for their solution.
- Communicate in writing about statistical and probability modelling, communicate relevant information accurately and effectively, using a form, structure and style that suits the purpose.
- Use mathematical and statistical software with confidence.
- Acquire further knowledge with little guidance or support.
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MT390 Image Processing
(3) Credit Hours
Image Processing is an important field of study and MT390 is meant to provide students with the basic knowledge of this field. Along with the importance of Image Processing in traditional areas such as Medical Diagnosis, Industrial Inspections, Security Systems, Robotics etc., the pervasiveness of smart phones equipped with powerful cameras has increased the need for Image Processing due to the availability of large amount of image data. This module is intended to provide students the opportunity to study the basics of the important field of Image Processing.
Course Code |
MT390 |
Course Title |
Image Processing |
Pre-requisite |
- |
Credit Hours |
3 |
Course Description |
Image Processing is an important field of study and MT390 is meant to provide students with the basic knowledge of this field. Along with the importance of Image Processing in traditional areas such as Medical Diagnosis, Industrial Inspections, Security Systems, Robotics etc., the pervasiveness of smart phones equipped with powerful cameras has increased the need for Image Processing due to the availability of large amount of image data. This module is intended to provide students the opportunity to study the basics of the important field of Image Processing. |
Course Objectives |
The aims of this module are to:
- Introduce students to the important field of Image Processing.
- Teach students the fundamental concepts related to image Representations and Enhancements.
- Impart to the students knowledge about Intensity Transformations and Spatial Domain Filtering.
- Introduce students to the concepts of 2-D Fourier Transform and the basics of Frequency Domain Filtering.
- Introduce students to the topics of Image Segmentation, Image Coding and their related techniques.
- Enable students to implement basic image processing algorithms using the Matlab Programming environment.
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Course Outcomes |
A. Knowledge and understanding
On successful completion of this course, the student will be able to demonstrate knowledge and understanding of: - Basic image representation concepts.
- Spatial domain image processing techniques of intensity transformation and filtering.
- Frequency domain image processing techniques of filtering and masking.
- Data reduction and image coding methods.
- Basic image segmentation concepts and techniques.
B. Cognitive skills
On successful completion of this course, the student will be able to:
- Critically evaluate and suggest spatial domain processing techniques for image enhancement purposes.
- Analyse and suggest appropriate frequency domain filtering techniques suitable for image processing tasks.
- Critically interpret histogram data of images and suggest appropriate image processing techniques for image enhancement.
- Analyze various image coding techniques and select the appropriate one for a particular task.
- Evaluate and interpret image segmentation results.
C. Practical and professional skills
On successful completion of this course, the student will be able to: - Apply skills and concepts from the course to develop practical image processing projects.
- Develop, Interpret and Implement image enhancement techniques both in the spatial and frequency domains.
- Perform Matlab simulations of practical image processing algorithms including image enhancement, coding and segmentation.
D. Key transferable skills
On successful completion of this course, the student will be able to: - Apply the mathematical and algorithmic skills acquired in this course to other areas of study and work.
- Carry out independent learning on topics related to image processing and computing.
- Communicate ideas and concepts about image processing techniques effectively both in writing as well as in any group discussion or environment.
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T215A Communication and Information Technologies A
(8) Credit Hours
Students will begin with Communication and information technologies (T215) – learning about the core principles upon which new technologies are built. They will gain an understanding of the ways in which data is stored, manipulated and transmitted; and discover how new processes and services are transforming our lives.
Digital communication and information technologies have become fundamental to the operation of modern societies. New products and services are rapidly transforming our lives, both at work and at play.
This module will help students learn more about these developments, and will equip them with the understanding and skills to continue learning about new developments in the future. Students will study the core principles on which the technologies are built and, through a range of online and offline activities, investigate new topics and technologies.
After studying this module, students will be in a better position to appreciate the potential of developments in communication and information technologies.
Course Code |
T215A |
Course Title |
Communication and Information Technologies A |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
Students will begin with Communication and information technologies (T215) – learning about the core principles upon which new technologies are built. They will gain an understanding of the ways in which data is stored, manipulated and transmitted; and discover how new processes and services are transforming our lives.
Digital communication and information technologies have become fundamental to the operation of modern societies. New products and services are rapidly transforming our lives, both at work and at play.
This module will help students learn more about these developments, and will equip them with the understanding and skills to continue learning about new developments in the future. Students will study the core principles on which the technologies are built and, through a range of online and offline activities, investigate new topics and technologies.
After studying this module, students will be in a better position to appreciate the potential of developments in communication and information technologies.
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Course Objectives |
- To introduce students to modern topics in ICTs.
- To develop student's skills in managing technologies of data storage and computer networks.
- To develop students skills in the technologies of mobile communication systems with an emphasis on mobile telephony.
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Course Outcomes |
A. Knowledge and understanding
Upon completing this module, students should be able to: - Describe key principles and concepts of digital communication and information systems and their component devices, including such topics as LANs, WLANs, mobile communication networks, encoding, modulation, multiplexing, routing, and standards.
- Explain key principles and concepts relating to digital data including the storage, manipulation and transmission of digital data.
- Identify major trends in communication and information technologies.
- Enhance their scientific reading and writing skills for writing short reports.
B. Cognitive skills
Upon completing this module, students should be able to: - Produce descriptions and explanations of the communication and information systems that feature in the module and of their underlying technologies and component devices
- Apply their understanding of the communication and information systems that feature in the module, their underlying technologies and component devices in specified contexts, updating themselves about the systems, technologies and devices as necessary.
- Use knowledge gained from the module to help them to figure out new or unfamiliar communication and information systems in specified situations; describe and explain such systems and their technologies and devices; apply their understanding in specified contexts.
- Analyze and discuss some of the technological, social, legal, ethical and personal issues that relate to communication and information systems, technologies and devices.
- Realize an overview of the way in which mobile telephone systems have developed from its first generation till LTE stage.
C. Practical and professional skills
Upon completing this module, students should be able to: - Critique draft materials in order to improve them
- Use standard office and communication software effectively to support their work
D. Key transferable skills
Upon completing this module, students should be able to: - Communicate complex information, arguments and ideas effectively and without plagiarism on a range of topics relating to communication and information systems through a variety of different media, using styles, language and images appropriate to purpose, audience and medium
- Perform simple calculations relating to communication and information systems, use and manipulate simple algebraic equations and interpret and produce graphical and tabular data
- Use information technology to find information from various sources and evaluate that information
- Develop a range of skills as an independent learner to support them in learning through the module materials and through other resources that they seek out for themselves.
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T215B Communication and Information Technologies B
(8) Credit Hours
Digital communication and information technologies have become fundamental to the operation of modern societies. New products and services are rapidly transforming our lives, both at work and at play. This module will help you to learn more about these developments through studying the core principles on which the technologies are built and, through a range of online and offline activities, investigate new topics and technologies.
This module will also help you to raise students’ awareness of some of the technologies and issues associated with safeguarding the privacy of digital information and the people who are affected by its use – hence the themes ‘protecting’ and ‘prying’.
These themes are explored through case studies and practical examples. A recurring approach is the use of an analytical framework that uses five themes to examine the technologies and issues: convenience, identity, reliability, acceptability and consequences.
Course Code |
T215B |
Course Title |
Communication and Information Technologies B |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
Digital communication and information technologies have become fundamental to the operation of modern societies. New products and services are rapidly transforming our lives, both at work and at play. This module will help you to learn more about these developments through studying the core principles on which the technologies are built and, through a range of online and offline activities, investigate new topics and technologies.
This module will also help you to raise students’ awareness of some of the technologies and issues associated with safeguarding the privacy of digital information and the people who are affected by its use – hence the themes ‘protecting’ and ‘prying’.
These themes are explored through case studies and practical examples. A recurring approach is the use of an analytical framework that uses five themes to examine the technologies and issues: convenience, identity, reliability, acceptability and consequences.
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Course Objectives |
- Increase the knowledge of the basic principles of communication and information systems and technologies, and the issues relating to their use
- Develop the ability to apply the understanding of communication and information technologies to learn about new or unfamiliar systems and technologies
- Enable students to explore how personal and private data can be protected.
- Help students develop an understanding of audio and video encoding and editing.
- Develop a variety of skills appropriate to a practitioner in communication and information technologies.
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Course Outcomes |
A. Knowledge and understanding
Upon completing this module, students should be able to: - Describe key principles and concepts relating to digital data including the availability of, mechanisms for protecting digital personal data, and the associated privacy and security issues related to it.
- Explain major trends of the fastest expanding areas of ICT, that of audio and video production and its potential for entertaining us.
- Understand key concepts, issues and technologies associated with online communication.
- Enhance the scientific reading and writing skills for writing long reports.
B. Cognitive skills
Upon completing this module, students should be able to: - Produce descriptions and explanations of the fundamental building block of all modern security systems which is encryption.
- Apply their understanding of the themes of security framework for communication and information systems that feature in the module, their underlying technologies and component devices for applying biometrics as a measurement of human beings used to identify them in the context of authentication.
- Use knowledge gained from the module to help them to figure out new or unfamiliar topics; conveying information in audio and visual format, introduction for some tools that will assist in obtaining a simple digital video from a number of digital still images.
- Describe and discuss some of the technological, social, legal, ethical and personal issues that relate to securing personal data like preventing unauthorized people from having access to private information.
- Evaluate or compare communication and information systems suggested for a particular need and give a justified recommendation on their appropriateness
C. Practical and professional skills
- Upon completing this module, students should be able to:
- Critique draft materials in order to improve them
- Experiment with some fingerprint recognition tools and evaluate the system using the given data set.
- Use specialised software tools as AviSynth script language to provide the students with basic skills required to produce video from still images.
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TM103 Computer Organization and Architecture
(4) Credit Hours
This module offers a clear and comprehensive survey about computer organization and architecture. It introduces the inner workings of a modern digital computer through an integrated presentation of fundamental concepts and principles
Course Code |
TM103 |
Course Title |
Computer Organization and Architecture |
Pre-requisite |
- |
Credit Hours |
4 |
Course Description |
This module offers a clear and comprehensive survey about computer organization and architecture. It introduces the inner workings of a modern digital computer through an integrated presentation of fundamental concepts and principles |
Course Objectives |
To emphasize on the concept of computer organization.
To emphasize on the concept computer architecture. To comprehend the different core concepts behind the hardware layer of a computer system. To recognize the mathematical concepts of the low level computer structure (circuits and gates). To know the processor's instruction sets architecture and implementation. To recognize the memory organization concept and methods
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Course Outcomes |
A. Knowledge and understanding
The module provides student with an understanding of: - Historical developments of computers.
- The Von-Neumann Model.
- Data representation and arithmetic in Computer Systems.
- Boolean Algebra and Digital Logic.
- Assembly language of an intuitive architecture (MARIE).
- Memory organization and addressing modes.
- Cache memory mapping Schemes.
B. Cognitive skills
To be able to
- Identify the different parts of any computer system and understand their roles.
- Understand the instruction set of any modern computer system.
- Evaluate the performance of modern computer systems.
C. Practical and professional skills
To be able to
- Have an awareness of the process of designing, writing and testing MARIE assembly programs.
- Use low level programming skills appropriate to a task.
- Ability to use the MARIE and data path simulator software.
D. Key transferable skills
To be able to - Interact effectively within a group using electronic conferencing techniques.
- Contribute to discussions on a conference.
- Improve own learning and performance.
- Communicate effectively about testing strategies, design and low level codes.
- Use electronic media (the web and electronic conferencing) for information retrieval and communication.
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TM105 Introduction to Programming
(4) Credit Hours
This module is an introductory level programming module and it is meant to provide basic foundation in computer programming to students. Students will learn how to develop solutions (algorithms) using pseudocode to solve simple problems. Thereafter, they will learn how to implement these solutions using a programming language (Java). This module serves as foundation for second level programming modules.
Course Code |
TM105 |
Course Title |
Introduction to Programming |
Pre-requisite |
- |
Credit Hours |
4 |
Course Description |
This module is an introductory level programming module and it is meant to provide basic foundation in computer programming to students. Students will learn how to develop solutions (algorithms) using pseudocode to solve simple problems. Thereafter, they will learn how to implement these solutions using a programming language (Java). This module serves as foundation for second level programming modules. |
Course Objectives |
The module aims to: - Introduce the technique of solving simple problems using pseudocode.
- Introduce Java programming via writing, compiling and executing simple programs.
- Present how to store and deal with data including variables, constants, and expressions.
- Cover deeply the concepts of program control structure and illustrate each concept with a diagrammatic notation using UML.
- Present how these concepts are implemented in Java.
- Introduce the concept of modularization and how to write Java methods.
- Present how to deal with basic data structures like strings, arrays and two dimensional arrays.
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Course Outcomes |
A. Knowledge and understanding
After studying the module, the student will be able to: - Understanding of the design and programming processes
- Knowledge of the main constructs and mechanisms in programming using Java language.
- Understanding of the techniques used in developing a medium Java application.
- Understanding of the basic data structures like strings, arrays and two dimensional arrays.
B. Cognitive skills
After studying the module, the student should be able to: - Describe and apply key concepts and techniques in software design and development.
- Analyse and abstract away from the details of a problem.
- Design and formulate an appropriate solution to a problem and evaluate it.
- Deal professionally with the basic data structures.
C. Practical and professional skills
After studying the module, the student should be able to: - Create, develop and trace Java programs.
- Use software tools such as a Java IDE and an On-line Java compiler.
- Use appropriate programming skills.
- Traverse data in the basic data structures in a professional way.
D. Key transferable skills
After studying the module, the student should be able to: - Find information from a range of sources to support a task.
- Plan medium tasks.
- Use Java libraries.
- Use appropriate numerical, mathematical and abstraction skills.
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TM111 Introduction to Computing and Information Technology 1
(8) Credit Hours
This is an introductory level 1 module, which provides students with a broad introduction to Computing and Information Technology concepts, principles and theories.
Course Code |
TM111 |
Course Title |
Introduction to Computing and Information Technology 1 |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
This is an introductory level 1 module, which provides students with a broad introduction to Computing and Information Technology concepts, principles and theories. |
Course Objectives |
- Help students to develop their understanding about the significant role of computers in our lives.
- Explore some processes by which sound and images in the real world are captured and stored and may be shared with peers and the wider world through social networking sites.
- Introduce students to algorithmic thinking and problem-solving skills using examples from everyday life.
- Enhance student's knowledge about implementing solutions to simple problems in a visual programming.
- Introduce students to the key concepts and technologies underpinning the communication networks.
- Prepare the student for further academic study by helping him develop his study skills.
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Course Outcomes |
A. Knowledge and understanding
- Understand the fundamental principles, concepts and techniques underlying Computing and IT.
- Explore various situations in which Computing and IT systems are used, the ways in which people interact with them, and the possibilities and limitations of such systems
- Be aware of the ethical, social and legal issues that can be associated with the development and deployment of Computing & IT systems.
- Demonstrate an understanding of algorithmic thinking and problem-solving skills using examples from everyday life.
- Understand the general principles, roles of various components, and the challenges involved in sending data across communication networks.
- Know how to find, rank and reference information; how to build your information literacy skills and how to interpret data in different forms.
B. Cognitive skills
- Evaluate key computing and IT concepts in a range of contexts.
- Apply appropriate techniques and tools for abstracting, modelling, problem solving, designing and testing computing and IT systems.
- Compare, contrast, critically analyze and refine specifications and implementations of software systems and/or simple hardware systems.
- Identify situations in which different network technologies may be used.
C. Practical and professional skills
- Communicate information, arguments, ideas and issues clearly and in appropriate ways, bearing in mind the audience for and the purpose of your communication.
- Use appropriate numerical and mathematical skills to carry out calculations and analyze data.
- Work independently, planning, monitoring, reflecting on and improving your own learning
- Demonstrate study skills at a level appropriate to higher education, such as study planning, learning from feedback and reading actively
D Key transferable skills
- Evaluate computing and IT systems, using appropriate simulation and modelling tools where appropriate
- Use a range of resources to help you develop as an independent learner.
- Use information literacy skills, computers and software packages appropriate to the workplace.
- Communicate appropriately with your tutor and other students using email, online conferences and forums.
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TM112 Introduction to Computing and Information Technology 2
(8) Credit Hours
This module will further develop and extend the skills and knowledge that students will have built up by studying its partner module TM111. The overall focus of TM112 is on developing the students’ problem solving skills.
Course Code |
TM112 |
Course Title |
Introduction to Computing and Information Technology 2 |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
This module will further develop and extend the skills and knowledge that students will have built up by studying its partner module TM111. The overall focus of TM112 is on developing the students’ problem solving skills. |
Course Objectives |
- Help students to practice the use of computing and information technologies to solve problems.
- Explore a variety of information technologies, from basic computer architecture, cloud computing, mobile/wireless and location-based computing Introduces the students to algorithmic thinking and problem-solving skills using examples from everyday life.
- Enhance student's knowledge about implementing solutions to simple problems in a visual programming.
- Focus on how to examine computing and information technology problems and solutions in their real-world context, with a focus on information security
- Develop numeracy skills (including algebra) in the context of information technologies and programming activities
- Prepare the student for further academic study by helping him develop his study skills
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Course Outcomes |
A. Knowledge and understanding
- Understand the fundamental principles, concepts and techniques underlying Computing and IT.
- Identify a range of models to support the analysis and design of Computing and IT systems
- Know how to implement solutions to simple problems using Python programming language.
- Be aware of the of the range of situations in which Computing and IT systems are used, the ways in which people interact with them, and the possibilities and limitations of such systems
- Understand the ethical, social and legal issues that can be associated with the development and deployment of Computing & IT systems
- Describe major trends in Computing and IT and of the implications of these trends
B. Cognitive skills
- Evaluate key computing and IT concepts in a range of contexts.
- Apply appropriate techniques and tools for abstracting, modelling, problem solving, designing and testing computing and IT systems.
- Compare, contrast, critically analyze and refine specifications and implementations of software systems and/or simple hardware systems.
- Carry out a project in computing and IT that applies and extends student's knowledge and understanding, and critically reflect on the processes involved and the outcomes of student's work.
C. Practical and professional skills
- Communicate information, arguments, ideas and issues clearly and in appropriate ways, bearing in mind the audience for and the purpose of your communication.
- Use appropriate numerical and mathematical skills to carry out calculations and analyze data.
- Work independently, planning, monitoring, reflecting on and improving your own learning
- Demonstrate study skills at a level appropriate to higher education, such as study planning, learning from feedback and reading actively
D. Key transferable skills
- Evaluate computing and IT systems, using appropriate simulation and modelling tools where appropriate
- Use a range of resources to help you develop as an independent learner.
- Use information literacy skills, computers and software packages appropriate to the workplace.
- Communicate appropriately with your tutor and other students using email, online conferences and forums.
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TM260 Security, ethics and privacy in IT and Computing
(4) Credit Hours
The ITC specialists must conduct ethically by adhering to the ITC code of conduct and understand the social, professional and legal context of IT and computing,
Course Code |
TM260 |
Course Title |
Security, ethics and privacy in IT and Computing |
Pre-requisite |
- |
Credit Hours |
4 |
Course Description |
The ITC specialists must conduct ethically by adhering to the ITC code of conduct and understand the social, professional and legal context of IT and computing, |
Course Objectives |
The module aims to: increase students awareness of the ethical, professional and legal issues of IT and computing and the responsible use of ITC.
Upon the successful completion of this module students will be able to: - Consider the ethical issues related to ITC systems.
- Act ethically while making any profession related decisions.
- Apply all legal principles to intellectual property and ITC related situation.
- understand the emerging issues related to ethics in cyberspace
- Develop a sound methodology in resolving ethical conflicts and crisis.
- Understand the social and ethical issues in the professional practice of computing and technology and their impact on the society..
- Look up relevant ethical standards as developed by the ACM.
- State several examples of important ethical principles as they apply to computer science related situations.
- Identify the ethical issues that relate to computer science in real situations they may encounter and decide whether a given action is ethical as regards computer science professional ethics, and justify that decision.
- Research and write a professional-quality paper about a topic relating to social, legal, and ethical implications of computer science.
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Course Outcomes |
A. Knowledge and understanding
After completing this module, students will be able to: - Understand how ITC could raise social issues and ethical dilemmas
- Understand the historical background of some social, legal, philosophical, political, constitutional and economical issues related to ITC
- Describe current social and legal developments related to computers and computer crime
- Recognize the existence of computer abuse cases , laws pertaining to them
- Appreciate the value of technology and identify the ethical and moral situations that must be faced and dealt with.
- Deepen their understanding of technology and its effects on society.
B. Cognitive skills
After completing this module, students will be able to: - Evaluate the legal and professional impact of ITC in real life contexts
- Analyse the effect of ethical issues on IT industry and society
C. Practical and professional skills
After completing this module, students will be able to: - Effectively identify and analyze professional and legal issues;
- Promote an ethics of computing in practice;
- Resolve dilemmas related to ethical, professional and legal ITC issues
D. Key transferable skills
After completing this module, students will be able to: - Communicate effectively in writing about ethical, legal and professional issues in the ITC context
- Become an independent learner.
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TM270 Artificial intelligence
(8) Credit Hours
Artificial intelligence and Machine learning skills are becoming more and more essential in the modern job market. Machine Learning Engineer was ranked as one of the top most demanded employees based on the incredible growth of job openings and the average base salary.
In this module the student will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI basic concepts and terms like symbolic AI, optimization and neural networks.
Course Code |
TM270 |
Course Title |
Artificial intelligence |
Pre-requisite |
MT141, TM112 |
Credit Hours |
8 |
Course Description |
Artificial intelligence and Machine learning skills are becoming more and more essential in the modern job market. Machine Learning Engineer was ranked as one of the top most demanded employees based on the incredible growth of job openings and the average base salary.
In this module the student will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI basic concepts and terms like symbolic AI, optimization and neural networks.
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Course Objectives |
This module aims to provide an introduction to the basic principles, techniques, and applications of Artificial Intelligence. Coverage includes symbolic AI, game playing, planning, optimization and neural networks basics.
Students will refine their programming skills developed during (TM110) using AI libraries as well as experiencing programming in AI language tools. Potential areas of further exploration include robotics, natural language processing, and computer vision.
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Course Outcomes |
A. Knowledge and understanding | Upon completion of this module the student will: - Define the aims and motivations for AI.
- Discover AI concepts and terms like machine learning, neural networks and deep learning.
- Recognize issues and concerns surrounding AI such as ethics and bias, & jobs.
- Recite key concepts and methods in evolutionary computation.
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B. Cognitive skills | Upon completion of this module the student will be able to: - Distinguish the different Cognitive Computing phases (Perception, Learning, Reasoning)
- Associate the different AI primitives to different AI applications.
- Select and use appropriate mathematical representations for a range of problem solving systems;
- Compare, contrast and evaluate competing approaches to computational problem solving and the simulation of intelligence;
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C. Practical and professional skills | Upon completion of this module the student will be able to: - Construct different pre-processing primitives for different AI applications
- Apply different methods for classification and regression using traditional AI methods
- Experiment with different tools for decision support and planning
- Choose among the different models hyper parameters according to the application and analysis of results.
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D Key transferable skills | Upon completion of this module the student will be able to: - Measure the different performance indicators for individual AI systems.
- Rank the different AI methods
- Adapt individual Method according to the problem in hand.
- Assemble different methods for creating appropriate AI pipeline.
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TM271 Machine Learning and Deep Learning
(8) Credit Hours
TM271 is a compulsory module in the BSc (Hons) Artificial Intelligence qualification. Machine Learning and Deep Learning techniques are essential for regression and classification tasks and for extracting meaningful insights from data. Students will also study the important techniques of Deep Learning and the strengths and limitations of Machine Learning and AI systems in this module.
Course Code |
TM271 |
Course Title |
Machine Learning and Deep Learning |
Pre-requisite |
TM270, MT141 |
Credit Hours |
8 |
Course Description |
TM271 is a compulsory module in the BSc (Hons) Artificial Intelligence qualification. Machine Learning and Deep Learning techniques are essential for regression and classification tasks and for extracting meaningful insights from data. Students will also study the important techniques of Deep Learning and the strengths and limitations of Machine Learning and AI systems in this module. |
Course Objectives |
This module aims to provide students with an in-depth introduction to two main- areas of Machine Learning: supervised and unsupervised. That covers the basics of ML and the deep learning approaches. The module aims to learn the concepts behind several machine learning algorithms without going deeply into the mathematics and gain practical experience applying them. It will cover main models and algorithms for regression, classification, clustering and probabilistic classification. Topics such as linear and logistic regression, regularization, probabilistic (Bayesian) inference, SVMs and neural networks, clustering, and dimensionality reduction. The module will assume general familiarity with linear algebra, probability theory, statistics, and programming in Python.
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Course Outcomes |
A. Knowledge and understanding | At the end of the module, learners will be expected to: - Identify the basic mechanisms of machine/deep learning techniques, including their appropriate usage, limitations, and alternatives.
- Recognize the key elements and tools used to develop machine/deep learning-based systems, together with their strengths and weaknesses.
- Identify the social, professional, legal, and ethical issues associated with machine learning systems.
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B. Cognitive skills | At the end of the module learners will be expected to: - Explain the pros, cons, and limitations of machine/deep learning techniques.
- Justify why machine/deep learning tools and techniques are either suitable or not for a particular problem or domain.
- Choose appropriate techniques and tools for designing, implementing, and testing machine/deep learning-based systems, and be aware of their limitations.
- Critically evaluate machine/deep learning tools and techniques to solve real-world problems.
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C. Practical and professional skills | At the end of the module, learners will be expected to: - Analyse and evaluate problems and plan strategies for their solution using the suitable machine/deep learning techniques if applicable.
- Implement a wide range of machine/deep learning techniques within the context of a given task and dataset, using the appropriate tools
- Assess the performance of the adopted machine/deep learning techniques including limits of applicability.
- Select and appropriately pre-process a dataset for machine learning and evaluate how biases inherent in the data will affect the reliability and fairness of the trained machine/deep learning-based system.
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D Key transferable skills | At the end of the module, learners will be expected to: - Relate the strengths, weaknesses, and limitations of machine/deep learning to wider social issues, including social justice, privacy and security, and access to resources and services.
- Communicate information, arguments, ideas, and issues clearly and in appropriate ways, considering the audience and purpose of the communication.
- Select and use accurately analytical techniques to solve problems.
- Develop skills to become an independent lifelong learner, as the field moves on.
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TM275 Parallel and Distributed Systems
(4) Credit Hours
This course introduces students to the fundamentals and techniques of parallel and distributed computing. Topics to be covered include: parallel and distributed computing, concurrency, distributed computing paradigms, inter-process communications, operating system support, client server model, and models of parallel machines.
This course has either a direct or indirect links with other courses, including: Introduction to computing and information technology (TM112), Computer Architecture and Organization (TM103), and Python Programming (M110). In addition, students will be capable to choose the proper environments of their final year projects based on the knowledge and skills they gain from this course. Understanding issues like processors capabilities and intercommunication between cores and processors are essential for building high performance systems.
Course Code |
TM275 |
Course Title |
Parallel and Distributed Systems |
Pre-requisite |
TM103 |
Credit Hours |
4 |
Course Description |
This course introduces students to the fundamentals and techniques of parallel and distributed computing. Topics to be covered include: parallel and distributed computing, concurrency, distributed computing paradigms, inter-process communications, operating system support, client server model, and models of parallel machines.
This course has either a direct or indirect links with other courses, including: Introduction to computing and information technology (TM112), Computer Architecture and Organization (TM103), and Python Programming (M110). In addition, students will be capable to choose the proper environments of their final year projects based on the knowledge and skills they gain from this course. Understanding issues like processors capabilities and intercommunication between cores and processors are essential for building high performance systems.
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Course Objectives |
On successful completion of this course students will be able to: - Provides students with an opportunity to sample some key areas in parallel and distributed computing.
- Develop critical skills for deployment in the field of computing and IT industry.
- Recognize the professional and ethical issues associated with the deployment of different parallel and distributed methodologies in digital systems.
- Use the application of fundamental Computer Science methods and algorithms in the development of parallel applications.
Facilitate the management of students' self-learning development in term of time management and self-organization skills.
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Course Outcomes |
A. Knowledge and understanding | After studying the module, learners will be able to: A1: Develop and apply knowledge of parallel and distributed computing techniques and methodologies A2: Understand the fundamental aspects of parallel and distributed processing, taxonomies of parallel systems, and performance measures for parallel systems. A3: Understand the theoretical limitations of parallel computing.
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B. Cognitive skills | After studying the module, learners will be able to: B1: Explain the design, testing, and performance analysis of a software system, and to be able to communicate that design to others. B2: Formulate and evaluate a hypothesis by proposing, implementing and testing a project. B3: Analyze and critically discuss research papers both in writing and in class
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C. Practical and professional skills | After studying the module, learners will be able to: C1: Apply design, development, and performance analysis of parallel and distributed applications. C2: be able to design and analyze parallel algorithms for a variety of problems and computational models, C3: implement parallel applications on modern parallel computing systems, and be able to measure, tune, and report on their performance.
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D Key transferable skills | After studying the module, learners will be able to: D1: Develop your skills in communicating information accurately and appropriately D2: Develop your skills in finding, selection and evaluation of different paradigms and techniques. D3: Develop your skills in reviewing and monitoring your own learning.
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TM276 Software Development Processes and Methodologies
(4) Credit Hours
Software is quickly becoming integral part of human life as we see more and more automation and technical advancements. Just like we expect car to work all the time and can't afford to break or reboot unexpectedly, software industry needs to continue to learn better way to build software if it were to become integral part of human life.
In this course, you will get an overview of how software teams work? What processes they use? What are some of the industry standard methodologies? What are pros and cons of each? You will learn enough to have meaningful conversation around software development processes.
Course Code |
TM276 |
Course Title |
Software Development Processes and Methodologies |
Pre-requisite |
TM112 |
Credit Hours |
4 |
Course Description |
Software is quickly becoming integral part of human life as we see more and more automation and technical advancements. Just like we expect car to work all the time and can't afford to break or reboot unexpectedly, software industry needs to continue to learn better way to build software if it were to become integral part of human life.
In this course, you will get an overview of how software teams work? What processes they use? What are some of the industry standard methodologies? What are pros and cons of each? You will learn enough to have meaningful conversation around software development processes.
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Course Objectives |
The module covers the importance of developing software in an iterative process by means of established methods. After completing this course, a learner will be able to - Apply core software engineering practices at conceptual level for a given problem.
- Compare and contrast traditional, agile, and lean development methodologies at high level. These include Waterfall, Rational Unified Process, V model, Incremental, Spiral models and overview of agile mind-set.
Propose a methodology best suited for a given situation.
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Course Outcomes |
A. Knowledge and understanding | Upon completing this module, learners will be able to: | A1. Account for different principles, methods, models and technologies for development of software systems and its included parts | A2. Describe the elements of a basic software development process and illustrate the variety of different life cycles. | A3. Acquire the knowledge of processes that are used to implement the software, verify and validate the software, deploy the software and maintain the software | A4. Identify a range of situations in which computer systems are used, the ways in which people interact with them, and the ethical, social and legal problems that computer software can create.
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B. Cognitive skills | Upon completing this module, learners will be able to: | B1. Reflect critically on the development process and its components to evaluate the results. | B2. Explain the part played by a system/software development method, and compare the approach/structure and appropriate application of a range of standard methods. | B3. Analyse problems, and design and apply core software engineering principles and practices at conceptual level for a given problem.
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C. Practical and professional skills | At the end of the module, learners will be able to: | C1. Practice and evaluate a variety of software engineering approaches to developing and evolving software. | C2. Propose a methodology best suited for a real world problem and justify the design decisions. | C3. Understand, though experience, the practical challenges associated with the development of a significant software system.
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D. Key skills | At the end of the module, learners will be able to: | D1. Show an understanding of the professional and legal duties software engineers owe to their employers, employees, customers and the wider public. | D2. Be able to draw on a wide variety of materials, and not just the block materials in order to progress your learning | D3. Organize their own learning and success to the point that they are ready to continue learning after they graduate.
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TM297 Compression Methods for Multimedia
(3) Credit Hours
Data compression aims at finding new ways of representing data so that it takes very little storage, while still being able to reconstruct the original data from the compressed version. Compression is applied namely when storage space is at a premium or when data needs to be transmitted and bandwidth is at a premium (which always is the case). The most important thing about compression is that it is not ``one size fits all'' approach: essentially, compression aims at specifying the characteristics of the data that needs to be compressed (mainly looking for patterns to be explored in order to achieve compact data representation). This module defines a variety of data modeling and representation techniques, which is at the heart of compression. Recently, the convergence in the field of communications, computing and entertainment industries enabled data compression to be a part of everyday life (e.g. MP3, DVD and Digital TV) and has created a number of exciting new opportunities for new applications of compression technologies.
Course Code |
TM297 |
Course Title |
Compression Methods for Multimedia |
Pre-requisite |
- |
Credit Hours |
3 |
Course Description |
Data compression aims at finding new ways of representing data so that it takes very little storage, while still being able to reconstruct the original data from the compressed version. Compression is applied namely when storage space is at a premium or when data needs to be transmitted and bandwidth is at a premium (which always is the case). The most important thing about compression is that it is not ``one size fits all'' approach: essentially, compression aims at specifying the characteristics of the data that needs to be compressed (mainly looking for patterns to be explored in order to achieve compact data representation). This module defines a variety of data modeling and representation techniques, which is at the heart of compression. Recently, the convergence in the field of communications, computing and entertainment industries enabled data compression to be a part of everyday life (e.g. MP3, DVD and Digital TV) and has created a number of exciting new opportunities for new applications of compression technologies. |
Course Objectives |
The aims of this module are to illustrate methods for handling and compressing different kinds of data, such as text, images, audio and video data and show data compression techniques for multimedia and other applications, especially the once used in the Internet.
After finishing successfully this Module you should be able to: - Compute basic statistics of data.
- Apply nontrivial algorithms to real-world problems.
- Outline the principles of data compression.
- Discover different compression methods for text, image, audio, and video data.
- Extend different compression methods and their applications in different aspects of computing.
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Course Outcomes |
A. Knowledge and understanding
Upon completing this module, students will be able to: - Develop a well-founded knowledge in the field of study.
- Relate other disciplines to the field of study.
- Develop an international perspective on the field of study.
B. Cognitive skills
Upon completing this module, students will be able to:
- Analyse and explore information and ideas and to convey those ideas clearly and fluently, in both written and spoken forms.
- Experiment effectively with others in order to work towards a common outcome.
- Select and make use of appropriate level, style and means of communication.
- Experiment appropriately with information and communication technologies.
C. Practical and professional skills
Upon completing this module, students will be able to: - Apply different compression methods for text, image, audio, and video data
- Examine nontrivial algorithms to real-world problems
- Extend different compression methods and their applications in different aspects of computing.
D. Key transferable skills
Upon completing this module, students will be able to: - Analyse and conclude independently.
- Develop ideas and adapt innovatively to changing environments.
- Identify problems constructs solutions, innovate and improve current practices
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TM340 Natural Language Processing
(8) Credit Hours
Natural Language Processing (NLP) is a cognitive science sub-discipline drawing on linguistics, computer science, and psycholinguistics. Recent strides in this area have already begun to influence how human beings interact with computers. NLP is a field located at the intersection of data science and Artificial Intelligence (AI) that is all about teaching machines how to understand human languages and extract meaning from text. This is also why machine learning is often part of NLP projects.
Course Code |
TM340 |
Course Title |
Natural Language Processing |
Pre-requisite |
TM271 |
Credit Hours |
8 |
Course Description |
Natural Language Processing (NLP) is a cognitive science sub-discipline drawing on linguistics, computer science, and psycholinguistics. Recent strides in this area have already begun to influence how human beings interact with computers. NLP is a field located at the intersection of data science and Artificial Intelligence (AI) that is all about teaching machines how to understand human languages and extract meaning from text. This is also why machine learning is often part of NLP projects. |
Course Objectives |
The aims of this module are to: - gain an in-depth understanding of the computational properties of natural languages and the commonly used algorithms for processing linguistic information.
- study computing systems that can process, understand, or communicate in human language.
- Focus on understanding various NLP tasks, algorithms for effectively problems, and methods for evaluating their performance.
- NLP topics including regular expressions, text processing, language parsing, text classification, language modelling and sequence tagging, vector space models of semantics.
Understand distributed word representations, Relation extraction with distant supervision, natural language inference, supervised sentiment analysis semantic parsing.
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Course Outcomes |
A. Knowledge and understanding | At the end of the module, learners will be expected to: - Recognize algorithmic and linguistic basis for NLP techniques
- Identify algorithms commonly used for NLP problems such as information extraction, machine translation, text summarization and question answering.
- Outline key concepts, tools and approaches for handling textual data
- Recognize the potential and limitations of NLP techniques within application areas.
- Describe and discuss the different subareas of NLP
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B. Cognitive skills | At the end of the module learners will be expected to: - Evaluate key computing and IT concepts in a range of contexts
- Apply appropriate techniques and tools for abstracting, modelling, problem solving, designing and testing computing and IT systems
- Compare, contrast, critically analyse and refine specifications and implementations of software systems and/or simple hardware systems
- Carry out a project in computing and IT that applies and extends student's knowledge and understanding, and critically reflect on the processes involved and the outcomes of student's work.
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C. Practical and professional skills | At the end of the module, learners will be expected to: - Learn text processing fundamentals, including stemming and lemmatization.
- Develop NLP applications that can be gradually scaled and transformed to a more complex and automated AI models
- Assess the performance of the deployed NLP techniques
- Recognise the impact of an NLP-based AI solution.
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D Key transferable skills | At the end of the module, learners will be expected to: - Apply the mathematical and algorithmic skills acquired in this course to other areas of study and work.
- Evaluate NLP systems, using appropriate simulation and modelling tools where appropriate.
- Use a range of resources to help you develop as an independent learner.
- Use information literacy skills, NLP libraries, computers, and software packages appropriate to the workplace.
- Communicate information, arguments, and ideas clearly and in appropriate ways with different audiences.
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TM341 Computer Vision
(8) Credit Hours
This module provides an introduction to computer vision, including basic methods from image processing techniques and deep learning with neural networks to solve high level vision tasks. It helps to the student to develop the practical skills necessary to build computer vision applications.
Course Code |
TM341 |
Course Title |
Computer Vision |
Pre-requisite |
TM271 |
Credit Hours |
8 |
Course Description |
This module provides an introduction to computer vision, including basic methods from image processing techniques and deep learning with neural networks to solve high level vision tasks. It helps to the student to develop the practical skills necessary to build computer vision applications. |
Course Objectives |
The aim of this module is to provide students with understanding of the theories and techniques used in computer vision. Topics include fundamentals of computer vision based on image processing techniques as well as machine-learning based computer vision used by new-school vision.
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Course Outcomes |
A. Knowledge and understanding | At the end of the module, learners will be expected to: A1 : Understand the mathematical and theoretical foundations of image processing and computer vision. A2 : Understand the main algorithms for image processing and computer vision. A3 : Be familiar with the major technical approaches involved in computer vision and its application. A4 : Understand the strengths, weaknesses and limitations of image processing and computer vision algorithms.
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B. Cognitive skills | At the end of the module learners will be expected to: B1: Explain the strengths, weaknesses and limitations of image processing and computer vision algorithms. B2: Describe the methods used in different computer vision applications. B3: Analyse a problem and critically evaluate the different computer vision methods for solving it. B4: Design and conduct an experiment to validate and assess the performance of a computer vision algorithm.
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C. Practical and professional skills | At the end of the module, learners will be expected to: C1: Apply the computer vision methods to design a vision-based algorithm. C2: Use of the programming languages to implement a computer vision task. C3: Read, present and discuss a research work in computer vision.
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D Key transferable skills | At the end of the module, learners will be expected to: D1: Communicate information clearly and in appropriate ways, considering the audience and purpose of the communication. D2: Select and use appropriate approach to solve problems. D3: Develop skills to become an independent lifelong learner as the field rapidly grows.
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TM351 Data management and analysis
(8) Credit Hours
Data management and analysis (TM351) – an overview of the concepts, techniques, and tools of modern data management and analysis. The requirements of data management continually evolve. Recently those requirements have surpassed the capabilities of traditional data management. So, in order to better prepare our graduates for the new demands of the job market, it is necessary to address both the traditional concepts of data management as well as the increasingly important area of data analytics.
Course Code |
TM351 |
Course Title |
Data management and analysis |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
Data management and analysis (TM351) – an overview of the concepts, techniques, and tools of modern data management and analysis. The requirements of data management continually evolve. Recently those requirements have surpassed the capabilities of traditional data management. So, in order to better prepare our graduates for the new demands of the job market, it is necessary to address both the traditional concepts of data management as well as the increasingly important area of data analytics. |
Course Objectives |
This module aims to address some of the key concepts required for the traditionally important area of data management, and the increasingly important area of data analytics. The module will compare traditional relational databases with an alternate model (a NoSQL database), and will enable students to choose between the alternatives to select an appropriate means of storing and managing data, depending on the size and structure of a particular dataset and the use to which that data will be put. Students will be introduced to preliminary techniques in data analysis, starting from the position that data is used to answer a question, and introduced to a range of data visualisation and visual analysis techniques that will instil an understanding of how to start exploring a new data set. To ensure that students are comfortable with handling datasets, they will explore a range of openly licensed real-world datasets (either downloaded from their host websites, or provided as snapshots) to illustrate the key concepts in the course. Sources such as data.gov.uk, the World Bank, and a range of other national and international agencies will be used to provide appropriate data. The module will aim to divide approximately equally between issues in data management (technical and socio-legal issues in storing and maintaining datasets), and issues in data analytics (using data to answer questions). Students are not expected to have a background in statistics, but should be comfortable working with mathematical concepts and will need to be competent programmers. The module will be framed around a narrative that looks at how to manage and extract value and insight from a range of increasingly large data collections. At each stage, a comparison will be drawn between different ways of representing the data (for example, using different sorts of charts or geographical mapping techniques), and limitations of the mechanisms presented. To enable students to get a feel for the use of data, each stage will also include an overview of some data analysis techniques, including summary reporting and exploratory data visualisation. The module will be driven by Richard Hamming's famous quote: The purpose of computing is insight, not numbers. Some of the key ideas are: - Introducing data analysis. Starting with a text based data file such as comma separated variable (CSV) document, this unit will provide a brief introduction to some basic operations on simple data files. This will give an opportunity to provide an outline of the key ideas in the module, to ensure that the students have installed the module software correctly, and to begin to familiarise themselves with that software.
- Concepts in data management. The module will look at three key areas in data management: data architectures and data access (CRUD), data integrity, and transaction management (ACID). Each of these will be illustrated using a relational database, and one non-relational alternative, and the advantages and limitations of each model discussed.
- Legal and ethical issues. The module will consider the legal and ethical issues involved in managing data collections. Students will be required to obtain and read (parts of) the Data Protection Act and the Freedom of Information Act, and demonstrate how these apply to issues in data management. They will also consider privacy, ownership, intellectual property and licensing issues in data collection, management, retrieval and reuse.
Concepts in data analytics. These sections will focus on using data to answer a real question; the focus will be on exploratory techniques (such as visualisation) and formulating a question into a form which can realistically be answered using the data that is available. Issues in processing techniques for large and real-time streamed data collections will also be addressed along with techniques and technologies (such as mapreduce) for handling them. This part will use a statistical package such as the python scientific libraries and/or ggplot to visualise the data and carry out appropriate analyses. It is not anticipated that students will need to understand statistical methods in depth.
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Course Outcomes |
A. Knowledge and understanding
Upon completing this course, students will be able to: - Discuss and describe the similarities and differences between at least two different database models, and how they are used to manage data collections.
- Identify and explain the legal issues surrounding data collection, usage and retention.
- Explain the stages and process of database design
B. Cognitive skills
Upon completing this course, students will be able to: - Select an appropriate database model for a data collection.
- Use data to answer a practical question.
- Analyse a simple scenario to produce a conceptual model.
C. Practical and professional skills
Upon completing this course, students will be able to: - Use a query language to extract information from a database.
- Use a statistical package to explore a data set
- Present an analysis of a dataset to a variety of audiences.
D. Key transferable skills
Upon completing this course, students will be able to: - Write a report detailing a systematic approach to analysing a data set.
- Gain Active listening to the stakeholders regarding their data analysis needs
Communicate the results of data analysis to stakeholders at appropriate level
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TM354 Software Engineering
(8) Credit Hours
Software engineering (TM354) – the intellectual tools needed to design, build, and test software systems. This module aims to provide you with an understanding of software engineering concepts and a view of practical software development. It follows a disciplined approach to the development of software systems to meet specified requirements. You will become familiar with a wide range of techniques to support the dialogue between software engineers and an organisation’s stakeholders, and the work of the developers. You will also develop a good understanding of the different approaches to, and practices of, software development, including those followed by agile methods.
Course Code |
TM354 |
Course Title |
Software Engineering |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
Software engineering (TM354) – the intellectual tools needed to design, build, and test software systems. This module aims to provide you with an understanding of software engineering concepts and a view of practical software development. It follows a disciplined approach to the development of software systems to meet specified requirements. You will become familiar with a wide range of techniques to support the dialogue between software engineers and an organisation’s stakeholders, and the work of the developers. You will also develop a good understanding of the different approaches to, and practices of, software development, including those followed by agile methods. |
Course Objectives |
- To understand the business domain for a problem requiring a software solution or a change to an existing solution
- To acquire the tools and knowledge to analyse and design such a solution or change
- To understand how any chosen software architecture will impact on the satisfaction of all users requirements and expectations
- To apply and reuse design expertise from a set of design patterns
- To develop the skills for testing outputs of all activities throughout the development process.
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Course Outcomes |
A. Knowledge and understanding
Upon completing this module, students will be able to:
- Understand concepts of software development and maintenance, specialising in such topics as Web and Internet design and programming, advanced database techniques or human computer interaction
- Acquire the methods and tools used to develop a range of software systems
- Identify a range of situations in which computer systems are used, the ways in which people interact with them, and the ethical, social and legal problems that computer software can create.
B. Cognitive skills
Upon completing this module, students will be able to: - Explain advanced software development concepts and apply them to practical problems, including in an extended piece of work
- Analyse problems, and design and evaluate realistic solutions to them
- Compare and contrast a variety of computing methods and tools, identifying the best choices to apply to specific problems
- Explain the various roles, functions and interactions of members of a software development team.
C. Practical and professional skills
Upon completing this module, students will be able to:
- Work independently, planning, monitoring, reflecting on and improving your own learning and working practices
- Work in a group, communicating computing ideas effectively in speech and in writing
- Find, assess and apply information from a variety of sources, using information technology where necessary, in a number of assignments, including at least one significant piece of work
- Use numerical and analytical techniques confidently to solve complex problems.
D. Key transferable skills
Upon completing this module, students will be able to:
- Design, program, test and evaluate software systems
- Use modern software tools, both within and outside your workplace
- Identify and handle the ethical, social and legal issues that may arise during software development and use.
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TM355 Communications Technology
(8) Credit Hours
Communications technology (TM355) – looks at the underlying technologies of modern electronic communications, such as mobile data and telephony, broadband, Wi-Fi, and optical fiber. Electronic communication is ubiquitous in homes, offices and urban environments. This module gives students an insight into these and other questions, by looking at the fundamental principles of communications technologies. Through these principles students will gain an insight into the possibilities and constraints of modern communications technology. This module complements other modules relating to networking (e.g., T215A/B, T216A/B and T316).
Course Code |
TM355 |
Course Title |
Communications Technology |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
Communications technology (TM355) – looks at the underlying technologies of modern electronic communications, such as mobile data and telephony, broadband, Wi-Fi, and optical fiber. Electronic communication is ubiquitous in homes, offices and urban environments. This module gives students an insight into these and other questions, by looking at the fundamental principles of communications technologies. Through these principles students will gain an insight into the possibilities and constraints of modern communications technology. This module complements other modules relating to networking (e.g., T215A/B, T216A/B and T316). |
Course Objectives |
TM355 is framed fairly precisely by its areas of interest: layers 1 and 2 of the OSI seven-layer model, that is the Physical Layer (layer 1) and the Data Link Layer (layer 2); and the three access technologies of optical fibre, DSL broadband and wireless. Within this framing, TM355 is concerned to reveal and explore commonalities that cut across these technologies, such as Shannon's law, multiple access (which increasingly means orthogonal frequency division multiple access, or OFDMA), modulation techniques (in the digital world, almost synonymous with quadrature amplitude modulation, or QAM), error detection and correction, and coding. A thorough understanding of the principles of these common technologies will equip students to understand a range of communication technologies, and to understand their potential and limitations
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Course Outcomes |
A. Knowledge and
understanding
Upon completing this course, students will be able to: - Have a sound grasp of the essential vocabulary of communications technology, be able to deploy it appropriately, and be able to explain them
- Understand the main principles and constraints of digital communications technology at the physical and data link layers, and employ them to analyse and assess communication scenarios.
- Understand the essential limits and trade-offs that are inherent in practical communication systems
B. Cognitive skills
Upon completing this course, students will be able to: - Use relevant data related to a communication system to model its behaviour and assess performance and resource requirements.
- Explain how and why particular communications configurations and systems are used, discuss their potential and limitations.
C. Practical and
professional skills
Upon completing this course, students will be able to: - Write a short report or essay discussing applications of communications technology.
- Be able to use third-party material critically.
- Be able to incorporate copyrighted material appropriately
D Key transferable skills
Upon completing this course, students will be able to: - Assess and synthesise information from a range of sources in order to offer an informed judgement on applications of communication technology.
- Develop your own learning skills in topics related to communications technology.
- Be able to learn independently from third-party materials, in order to keep up to date in communications technology.
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TM471 The Telematics project ITC
(8) Credit Hours
TM471 is a final year projects course. Students are expected to select topics of their projects consistent with their track that is, directly related to the information technology and computing track, and also, make use of the skills they have learnt throughout their studies in lower level modules to plan a project, develop it and submit a report on completion of the project. They are expected to do a presentation and perform a working demonstration of their selected project.
Course Code |
TM471 |
Course Title |
The Telematics project ITC |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
TM471 is a final year projects course. Students are expected to select topics of their projects consistent with their track that is, directly related to the information technology and computing track, and also, make use of the skills they have learnt throughout their studies in lower level modules to plan a project, develop it and submit a report on completion of the project. They are expected to do a presentation and perform a working demonstration of their selected project. |
Course Objectives |
On successful completion of this course, students will be able to: - Undertake practical projects to solve problems in the area of ITC.
- Perform literature search on a selected topic of interest.
- Apply what they have learnt to plan a project and develop a deliverable.
- Produce project plans for successful undertaking of practical projects.
- Write a detailed project report and communicate their ideas clearly.
- Present their ideas and work formatively before an audience while progressing in their project.
- Present their findings, outcome and deliverable before an audience
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Course Outcomes |
A. Knowledge and understanding
Upon completing this course, students will be able to: - Demonstrate understanding of the fundamental technical concepts and principles relevant to their project
- Apply a systematic approach towards the practical implementation of their project
- Plan a project while preparing a detailed schedule of the project tasks and milestones for 8 months.
B. Cognitive skills
Upon completing this course, students will be able to: - Identify and refine the goals and content of their project
- Identify, list and justify the resources, skills and activities needed to carry out the project successfully
- Conduct a proper literature search. Gather, analyse and evaluate relevant information to complete the project successfully
- Critically review how they have tackled the project
C. Practical and professional skills
Upon completing this course, students will be able to:
- Plan and organize their project work appropriately, and keep systematic records of plans, progress and outcomes
- Identify and address the ethical, social and legal issues that may arise during the development and use of Computing and IT systems
- Analyse a practical problem and devise and implement a solution building on the knowledge and skills developed throughout their earlier OU studies and experience.
- Provide a tangible solution by accomplishing their deliverable according to their project requirements.
D. Key transferable skills
Upon completing this course, students will be able to: - Make effective use of a variety of information sources, including the internet, e-library and demonstrating awareness of the credibility of the source
- Communicate information, ideas, problems and solutions clearly
- Learn independently and reflect on what has been done, with a view to improving skills and knowledge
- Present their work in a professional manner while addressing the audience in the domain.
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TT284 Web technologies
(8) Credit Hours
This module is meant to introduce students to the foundations of web applications, including protocols, standards and content handling.
Course Code |
TT284 |
Course Title |
Web technologies |
Pre-requisite |
- |
Credit Hours |
8 |
Course Description |
This module is meant to introduce students to the foundations of web applications, including protocols, standards and content handling. |
Course Objectives |
- give students an insight into architectures, protocols, standards, languages, tools and techniques;
- give students an understanding of approaches to more dynamic and mobile content;
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Course Outcomes |
A. Knowledge and understanding
After completing this module, students will be able to: - Describe how the development of the Web has enabled the creation of new forms of information systems and impacted commerce and public services.
- Explain different architectural approaches to application design and contrast traditional approaches with the underlying client–server model of Web applications.
- Describe the roles of the range of protocols and standards associated with Web applications and their communications, for the development of web applications.
- Explain the operation and properties of service, distributed and mobile approaches to web architecture.
- Demonstrate knowledge of a range of different programming languages and explain their differing roles and properties for web applications.
- Discuss issues of web design including, accessibility, usability, localisation and globalisation and the nature of static and dynamic content and different content delivery approaches
- Explain a range of security issues including secure protocols, use of certificates, authentication, authorisation, and firewalls
B. Cognitive skills
After completing this module, students will be able to: - Analyse requirements to produce a design for a simple web application, applying an understanding of requirements for aspects such as usability and accessibility.
- Describe a suitable architecture, components and standards as the basis for implementation of a web application for a public or business organisation.
- Construct, using appropriate code, a simple web application selecting and reusing code etc where appropriate. , transforms content and integrates services to produce a mobile application
C. Practical and professional skills
After completing this module, students will be able to: - Outline the importance of standards and standardisation bodies.
- Maintain an up-to-date view of ongoing developments in web technology including standards and techniques.
- Produce and manage design and development plans for a specific technical solution to a challenge in Web application development.
D. Key transferable skills
After completing this module, students will be able to: - Find, select and use information from a range of sources to support analysis, design and implementation tasks.
- Plan and produce a well-structured and researched quality report as part of a project.
- Plan and manage effort and progress whilst undertaking a substantial project.
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TU170 Computing Essentials
(3) Credit Hours
This is an introductory course which introduces students to the essential concepts related to computing essentials. This is a fundamental course for all students enrolled in AOU.
Course Code |
TU170 |
Course Title |
Computing Essentials |
Pre-requisite |
- |
Credit Hours |
3 |
Course Description |
This is an introductory course which introduces students to the essential concepts related to computing essentials. This is a fundamental course for all students enrolled in AOU. |
Course Objectives |
- To develop basic skills of “Learning"
- To know e-Learning: meaning, accessibility, skills, and resources
- To familiarize with the basic concepts of Information Technology: Internet, Web, and Systems
- To familiarize with basic computer system applications: software and hardware
- To learn some practical skills for using computers
- To introduce the concepts of: Security and Ethics
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Course Outcomes |
A. Knowledge and understanding
After studying the course, the student will be able to:
- Understand terminologies related to IT and computer
- Understand the different learning styles
- Describe the difference between Conventional and blended-learning education systems
- Understand how to read and take notes in the process of learning
- Describe the social media types and facilities
- Have a background about the Web and Internet inventions
- Understand and explain what is information system and technology
- Know the types of applications
- Describe the e-commerce
- Explain different part in computer system (Hardware such as system unite, input and output, memory and processor)
- Explain different terms in communication such as network, connectivity, wireless, server, client)
- Understand clearly what is the difference between privacy and security)
- Describe what are computer ethics and computer crime
B. Cognitive skills
After studying the course, the student will be able to:
- Learn by himself
- Deal with computer problems
- Describe the difference between different learning styles
- Describe the web and search engines
C. Practical and professional skills
To be able to
- Operate the computer system properly
- Interact with applications and programs such as (MS office) confidently
- Communicate with others electronically (Email, instant messaging, blogs, micro-blogs and wikis)
- Read analytically and critically for the purpose of learning
- Avoid plagiarisms
- Initiate a transaction electronically (e-commerce) in a secure way
- Use the social media in the process of learning and communication with others.
- Connect and surf the internet
- Search using the search engines.
- Send and receive email, and share files in a secure way.
- Avoid computer crime
- Use computer ethically
D Key transferable skills
To be able to - Interact effectively within a group using social media and electronic conferencing techniques.
- Working in groups using the LMS system and course forum online.
- Contribute to discussions on a conference using instant messaging.
- Improve own learning and performance
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