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Faculty of Engineering & IT Student Handbook
MSc. In Informatics & MSc. In ITM September 2019
The Best of British Education in Dubai
P O Box 345015, Dubai, UAE. Tel: 971 4 279 1400 Fax: 971 4 279 1490 email: [email protected] web: www.buid.ac.ae
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LETTER FROM THE HEAD OF PROGRAMME Dear Student
elcome to your new Doctoral programme at the British University in Dubai (BUiD). We are very happy to have you join the programme and start your journey towards the highest academic qualification with us.
We pride ourselves on being able to offer a high-quality and flexible approach to post graduate education. We look forward to getting to know you and travelling with you till you graduate and receive your MSc Degree. I commend this to you as your goal; our goal is to keep you moving in the right direction so you will achieve your goal in a timely manner. An MSc degree in Informatics/Information Technology Management from the British University in Dubai will give you a deep knowledge in your chosen area of research and position you for new opportunities in academia or higher management. You will learn a broad spectrum of competencies in conducting rigorous and worthwhile research and how to apply the results of your endeavours in a myriad of contexts within the UAE, the Gulf region and more broadly at an international level. Your supervisors come with a wide range of experience and specialisms – you can focus your research in a particular industry or sector and in areas as diverse as Informatics Research Methods, Knowledge Representation & Reasoning, Introduction to Computational Linguistics, Data Mining and Exploration, Knowledge Engineering, Knowledge Management, Machine Learning, E-commerce, IT Entrepreneurship, Software Systems Design: Practical Object-Oriented Analysis and Design with UML, Systems Requirements Engineering Management Information Systems, Planning, Execution and Control, and People, Culture and Organisation. You need to choose either a dissertation or research project to complete your courses, each have different programme structure. For dissertation route on one hand, you will study four core modules, including one module on research methods, and two advanced specialised elective modules, you will engage in a major master-level research thesis of your own choosing – with guidance from your your academic supervisor. In addition, scholarly workshops are offered throughout the year and all students are expected to benefit from these. For the project-based route on the other hand you will study six core modules, including one module on research methods, and two advanced specialised elective modules, you will engage in a major master-level research project of your own choosing – with guidance from your academic supervisor. A further requirement for all students is to develop publications of their work with members of their supervisory team, leading to joint papers in high calibre academic journals and presentations at international conferences. In these first days and weeks, enjoy your first steps into this new world, get to know your fellow MSc scholars, your supervisors and module tutors, the administration staff and library staff – and, as a small university, you are sure to also have the chance to meet senior staff of the University. You will get student
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visitor status for the University of Edinburgh and, in due time, have your own University of Edinburgh Academic Advisor. Finally, remember your continuing education is only part of a balanced life. Please get to know your supervisor and feel free to chat with him/her about getting the work-study-life balance right for your own wellbeing, especially when your personal circumstances change. You cannot rush an MSc! Have a great PhD experience! Best wishes
Prof. Khaled Shaalan Head of Programme – MSc Informatics/Information Technology Management
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TABLE OF CONTENTS
1. Introduction ........................................................................................................................................ 6
2. Overview.............................................................................................................................................. 6
2.1 Informatics and the New Global Economy .......................................................... 6 2.2 Degrees Offered ................................................................................................... 6 2.3 The Faculty of Engineering & IT......................................................................... 6
3. Research and Teaching at the Faculty of Engineering & IT .......................................................... 7
3.1 Research ............................................................................................................... 7 3.2 Teaching and Modules ......................................................................................... 7
3.2.1 Masters in Informatics (Knowledge and Data Management) ....................... 7 3.2.2 Master in IT Management ........................................................................... 13
3.2.2.1 Premasters .............................................................................................. 16
3.2.3 Term-by-Term Plan ......................................................................................... 16 3.2.3.1 Postgraduate Diploma (PD Dip) ................................................................. 16
4. The Academics .................................................................................................................................. 17
5. Module Timetable ............................................................................................................................. 19
6. The Dissertation ................................................................................................................................ 19
6.1 Dissertation Guidelines ...................................................................................... 19
Appendix 1- Module Syllabi ..................................................................................................................... 22
Role in Context ................................................................................................. 26 Self-Development ............................................................................................. 26 Syllabus ............................................................................................................ 26
Syllabus ............................................................................................................ 29 Data Mining and Exploration........................................................................... 30
Syllabus ............................................................................................................ 32
Data mining and Exploration .............................................................................................................. 33
Coursework ........................................................................................................................................... 33
Grade 60% from total .......................................................................................................................... 33
Team assignments: 2-3 members ........................................................................................................ 33
1. Description: ...................................................................................................... 33 2. Milestones: ....................................................................................................... 34 3. Critical Survey ................................................................................................. 34
4. Final Report ..................................................................................................... 34
7. Important Notes ............................................................................................... 35
8. Late submission ............................................................................................... 35 Syllabus ............................................................................................................ 40
Syllabus ............................................................................................................... 49 Module Text ........................................................................................................ 50
Recommended Reading ..................................................................................... 50
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Syllabus ............................................................................................................... 52 Core Module Text .............................................................................................. 53 Recommended Reading ..................................................................................... 53 Syllabus ............................................................................................................... 56 Core Module Text .............................................................................................. 57
Recommended Reading ..................................................................................... 57
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1. Introduction
This handbook is intended to give you a guide about the Information Technology (IT) degrees at the Faculty of Engineering and IT. Please take the time to read it, as many frequently asked questions are answered in the handbook. In regards to the faculty policies and regulations, this handbook is intended as a guide only. Further information can be obtained by speaking to faculty members or support staff. Please take your time to read through to make sure you are familiar with the content. The handbook is posted on-line and will be up-dated when there are any changes to policy or information. It is a pleasure to welcome you to the Faculty of Engineering and IT. We hope you enjoy your time here.
2. Overview
2.1 Informatics and the New Global Economy
In the rapidly developing economy of the region, there is a great need for research based teaching, enabling students to contribute to the knowledge economy by exploiting cutting edge technologies to organise and manage information. The programmes in the Faculty of Engineering & IT aim to provide students with a comprehensive foundation in key techniques considered to be the state-of-the-art in informatics research and study. Applications are vast, and include several industry sectors ranging from the finance, medicine and travel industries to traditional manufacturing and service sectors.
2.2 Degrees Offered
The University is offering two full-time and part-time MSc programmes:
Master of Science (MSc) in Informatics (Knowledge and Data Management). The master in Informatics is to be run in collaboration with the Faculty of Engineering & IT at BUiD and the University of Edinburgh. The master of informatics is offered in either dissertation or research project route.
Master of Science (MSc) in IT Management. The master in IT management is to be run in collaboration with the Faculty of Engineering & IT at BUiD and the Universities of Edinburgh and Manchester. The master of IT management is offered in dissertation route.
2.3 The Faculty of Engineering & IT
The Faculty of Engineering & IT covers a basic need in the local Economy and in the Arab world in general, to have a research university in Engineering and Information Technology at international standards.
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Information Technology research has become one of the largest areas of research in the world, leading to applications in almost every industry we can think of. The academics at BUiD’s Faculty of Engineering and IT come from several backgrounds leading to a wide range of subjects offered, and several areas of research covered. The Faculty aims at collaborating with local research councils and local industries in order to provide research that could benefit the local economy in several ways. The role of IT research has become a central one in all parts of the world, and Dubai, hoping to be the knowledge centre of the region could greatly benefit from Informatics research and teaching at international levels.
3. Research and Teaching at the Faculty of Engineering & IT
3.1 Research
One of the basic aims of the British University in Dubai is to provide leading edge research in key disciplines, IT being one of them. In order to do that, BUiD has set up collaborations with several leading Institutes in the UK. Edinburgh’s Informatics institute is the largest one of its kind in the UK and the only one to be awarded a 5*A rating among UK universities. The University Of Manchester, UK, is also one of the UK's top rated research universities. It was recently awarded the top 5* rating. Several Edinburgh and Manchester academics visit BUiD regularly and give invited seminars and talks. BUiD staff and students are also encouraged to visit Edinburgh’s Informatics group and Manchester IT management's group.
3.2 Teaching and Modules
Teaching will be research based and one of the main aims of BUiD is to encourage students to have a thirst for knowledge and to enjoy being part of the research environment. In addition to acquiring novel technologies in the field of Information Technology, students will acquire several skills while attending their modules, such as teamwork, good presentation skills, and creative thinking.
3.2.1 Masters in Informatics (Knowledge and Data Management)
This programme aims to provide you with a comprehensive grounding in key techniques considered to be the state of the art in Informatics research and study. Topics covered include building of systems that capture and represent knowledge for people and businesses. The programme gives graduates a head start to enter multinational as well as specialist companies, or continue in academic research. Examples are knowledge management systems, natural language understanding, machine translation, supply chains and electronic markets, and automatic negotiation systems.
Applications are vast, and include several industry sectors ranging from education, architecture, finance, medicine, and travel industries to traditional manufacturing and service sectors. The BUiD MSc has accreditation from the UAE Ministry of Higher Education and Scientific Research.
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What can I do with an MSc in Informatics (Knowledge and Data Management)? The Masters in Informatics (Knowledge and Data Management) qualifies you to do a job that requires researching data, using it to design programs, and then implementing the programs in experimental stages. This can be done in a variety of industries such as pharmaceuticals, education, system engineering, manufacturing, communications, transportation, entertainment, defence, computer technology, and of course government (e.g. e-government). Company functions that you would be able to do can be equipment programming, product testing, executing technical projects from inception to completion, record keeping and documentation, research, engineering tasks, information and knowledge management, development and modification of software programs. Computer professionals with a Master’s degree are employed as programmers, engineers, analysts, language and speech experts, consultants, and managers. They could be employed in research and development departments, Informatics (Knowledge and Data Management) departments, or even strategic planning departments of business enterprises or government agencies. Another equally exciting career path would be the continuation of the research that has been started for the Masters dissertation. This will allow graduates to attain a PhD in an area of expertise that is relevant to knowledge development in this part of the world and even beyond since all research conducted at BUiD is of the highest international standing.
Dissertation or Research Project You need to choose either a dissertation or research project to complete your courses, each have different programme structure. Dissertation and research project topic must be related to the discipline of the degree sought. The dissertation is a substantial piece of research work in a specific area of project management. The dissertation is supervised individually and assessed on the basis of a final report of not more than 25,000 words in length. The research project will be based on a research or development/application topic of industrial and scientific relevance in the area of project management. The project will be carried out either in the university setting or at the work placement approved by the course director. MSc Informatics Programme Structure for the Dissertation Route Students study 4 core taught modules and 2 modules of 20 credits from the list of electives and complete a 60 credit research-based dissertation. The award of MSc IT is approved following the successful completion of 180 credits. The following is a summary of modules per stream, for more information on the modules please refer to appendix 1:
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Module Code Module Title Credits
Core: Complete all of the following modules
INF501 Informatics Research Methods 20
INF502 Knowledge Representation & Reasoning 20
INF503 Introduction to Computational Linguistics 20
INF504 Data Mining and Exploration 20
Electives: (Student will be required to take two out of the six modules)
INF505 Knowledge Engineering (pre-requisite INF502, Knowledge Representation & Reasoning)
20
INF506 Knowledge Management 20
INF513* Machine Learning (pre-requisite INF504, Data Mining & Exploration)
20
INF508 IT Project Management 20
INF509 E-commerce 20
INF510 IT Entrepreneurship 20
INF511 Software Systems Design: Practical Object-Oriented Analysis and Design with UML
20
INF512 Systems Requirements Engineering 20
INF514 Management Information Systems 20
Independent Research
RES506 Dissertation 60
Total Credits 180
*INF507 Learning from Data module name has been revised to Machine Learning.
Recommended Study Plans TERM 1 INF501 Informatics Research Methods (Core) INF503 Introduction to Computational Linguistics (Core) INF507 Learning from Data INF510 IT Entrepreneurship TERM 2 INF502 Knowledge Representation & Reasoning (Core) INF504 Data Mining and Exploration (Core) TERM 3 INF506 Knowledge Management INF508 IT Project Management INF509 E-commerce INF513 Machine Learning MSc Informatics Programme Structure for the Project-Based Route Students study 8 taught modules (6 core, 2 modules from Electives) and a project. Essentially the 60 credit dissertation in the existing structure is replaced with 2 modules of 20 credits each and a project of 20 credits.
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Module Code Module Title Credits
Core: Complete all of the following modules INF501 Informatics Research Methods 20
INF502 Knowledge Representation & Reasoning 20
INF503 Introduction to Computational Linguistics 20
INF504 Data Mining and Exploration 20
INF508 IT Project Management 20
INF506 Knowledge Management 20
INF520 MSc Project* 20
Electives SET 1: (Student will be required to take two out of these electives)
INF513* Machine Learning (pre-requisite INF504, Data Mining & Exploration)
20
INF505 Knowledge Engineering (pre-requisite INF502, Knowledge Representation & Reasoning)
20
INF509 E-commerce 20
INF510 IT Entrepreneurship 20
INF511 Software Systems Design: Practical Object-Oriented Analysis and Design with UML
INF512 Systems Requirements Engineering
INF514 Management Information Systems 20
Total Credits 180
*INF507 Learning from Data module name has been revised to Machine Learning.
Recommended Study Plans TERM 1 INF501 Informatics Research Methods (Core) INF503 Introduction to Computational Linguistics (Core) INF507 Learning from Data INF510 IT Entrepreneurship TERM 2 INF502 Knowledge Representation & Reasoning (Core) INF504 Data Mining and Exploration (Core) TERM 3 INF506 Knowledge Management (Core) INF508 IT Project Management (Core) INF513 Machine Learning INF509 E-commerce
Informatics Research Methods The aim of this module is to teach the methodologies of and the skills for conducting research in Informatics. It will focus on three main parts: (1) analytical methods, (2) empirical methods, (3) writing and evaluating research. The module will cover: the nature of Informatics and Informatics research; criteria for assessing Informatics research; different methodologies for Informatics research and how to combine them; analytical proof; algorithm and complexity analysis; the design of experiments and evaluations; practical advice on conducting research and numerous research skills including: reading, reviewing, presenting, writing, design, etc. Knowledge Representation & Reasoning This module provides the basis for the understanding and use of Knowledge Representation and Reasoning techniques in AI systems in general, and knowledge-based systems in
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particular. The module covers notions of representation and the relationship between representation and that which is represented, along with issues of the resources required to manipulate such representations. The focus is on different logic-based representation languages and proof search using logical calculi, but other approaches are also discussed. Introduction to Computational Linguistics This is an introductory course that presumes no prior familiarity with Computational Linguistics. This course provides an introduction to the basic theory and practice of computational approaches to natural language processing. The module cover the following topic: introduction to programming in Python & NLTK, tokenization, part-of-speech tagging, context-free grammars for natural language, evaluating a natural language processing system, parsing techniques, information extraction, Arabic language processing. The course also provides an introductory insight into the state of current research in Computational Linguistics. Data Mining & Exploration Data mining is about analyzing, interpreting, visualizing and exploiting the data that is captured scientific and commercial environments. The course will also feature paper presentations and a each student will undertake a mini-project on a real-world dataset. IT Project Management This module is about IT project management activities. Covered topics include software systems engineering, project planning and management, quality assurance, and strategic planning. The student will learn to manage software as a distinct project, use specifications and descriptions, make use of structured techniques, complete reviews and audits, confirm product development with planned verification, and validation and testing. Students will work with essential tools and methodologies for managing an effective IT project, including software for version control, and project management. Knowledge Management The aim of this module is to teach the principles and technologies of knowledge management. A case study approach, as and where appropriate, will be adopted in introducing the course contents. The module covers the fundamental concepts in the study of knowledge and its creation, representation, dissemination, use and re-use, and management. The focus is on methods, techniques, and tools for computer support of knowledge management, knowledge acquisition, and how to apply a knowledge management system using one of the knowledge-based system tools. Knowledge Engineering This module introduces a variety of methodologies important to the development of modern knowledge-based systems (KBSs) and their applications, especially pertaining to the Semantic Web. The module covers topics regarding different processes within a KBS lifecycle, ranging from knowledge capture and analysis, systems design and implementation, to knowledge maintenance and system evaluation. Students will learn about the latest applications of KBS in building intelligence into Web applications, and will build a knowledge-based Web application. Machine Learning Machine learning is about making computers learn, rather than simply programming them to do tasks. The course will discuss supervised learning (which is concerned with learning to predict an output, from given inputs), reinforcement learning (which is concerned about learning from interacting with an environment), unsupervised learning, where we wish to discover the
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structure in a set of patterns; there is no output "teacher signal". We will compare and contrast different learning algorithms, and unlike Data Mining Exploration module where the focus was on the applying algorithms to large real-world data sets, in this course we will get to the technical and mathematical details of the studied algorithms. E-Commerce This module is about topics related to creating a business on the web, with particular focus on e-commerce. Students will study the IT issues raised by electronic business and commerce. Techniques and technologies available for designing and implementing e-business and e-commerce applications will be surveyed. Students will have first-hand experience with Web-based tools and services to help design e-Business solutions. IT Entrepreneurship This module provides the students with scientific methodologies for identifying opportunities in the IT space. Students will learn how to create an effective business plan, acquiring funding, establishing a company from scratch and managing in an environment of high growth, high uncertainty and rapid change. The module will include case studies of successful and failed IT entrepreneurial companies and will draw upon the angel investing, venture capital and entrepreneurial communities from guest speakers. Software Systems Design: Practical Object-Oriented Analysis and Design with UML This course is designed to give students knowledge of the principles of object orientation and extensive practice in the application of these principles using the Unified Process (UP) and Unified Modelling Language (UML). It guides the students through the process of UML system modelling approach and from requirements analysis to implementation. The course is very practically oriented and follows the Unified Process so that the students learn how UML is applied in a real software systems engineering project. The course will also give students knowledge of Model Driven Architecture (MDA). MDA is the future of UML and unifies every step of software systems development and integration from business modeling, through architectural and application modeling, to development, deployment, maintenance, and system evolution. The goal of MDA is to move the development of software to a higher level of abstraction through the extensive use of UML models. These models provide the basis for automatic code generation by MDA enabled CASE tools. Systems Requirements Engineering The general aims of this course is to make students understand the ever-increasing importance of requirements in the wider systems engineering process, and to improve systems engineering processes by making them more requirements-oriented. The course describes the role of requirements in the construction and continued maintenance of large, complex and evolving software-intensive systems. It introduces the important concepts and activities in systems requirements engineering, explains how they can knit together to form a through-life requirements engineering process, and demonstrates how such an end-to-end process can be defined and used in practice. The course provides a broad overview of the notations, techniques, methods and tools that can be used to support the various requirements engineering activities, and complements this with the opportunity to gain experience in a selection of these. The course seeks to illustrate the wider applicability of requirements engineering to everyday projects, the breath of skills required and the many contributing disciplines.
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Management Information Systems This module is about determining the information system needs for designing and implementing information systems that support these needs. Management information systems integrate, for purposes of information requirements, the accounting, financial, and operations management functions of an organization. This course will examine the various levels and types of software and information systems required by an organization to integrate these functions. Dissertation Having successfully completed the six modules in the taught stage of the programme, students who wish to proceed to the master’s degree take the dissertation stage. This final project is intended to give students an opportunity to focus on an aspect of the taught subject matter and investigate it in more detail. This will help them consolidate their capacity for independent study, and to learn some of the techniques needed to conduct research and develop knowledge in the subject area of the programme of study. This is a research project. The only piece of work to be submitted for examination is a dissertation, and this is a written report on the research. There are thus two aspects to consider: the research and the writing. Both are governed by implicit rules common to the discipline of formal research; part of the students’ training is to become familiar with these rules. MSc Research project In this module the student will undertake a short research project. This project could be an extension of one or more projects submitted in previous modules. In this module the student will reflect on all his/her research activities in the previous modules, will undertake critical review of previous outcomes in order to prepare a proposal for new research project. The student will focus on applying the knowledge learnt in several modules to analyse, revise, improve and assess a relevant topic. This could include topics on Artificial Intelligence, Intelligent Systems, Knowledge Management, Learning from Data, Software Engineering, IT & management, or any other relevant IT topic as long as it is approved by the module tutor. The student will produce a research report, including an executive summary, reflective analysis of previous works, and details of the project outcome.
3.2.2 Master in IT Management
The MSc programme in IT Management aims to produce hybrid managers who can effectively align business and IT strategies. Graduates will have the necessary competence to successfully manage IT projects as well as teams of IT developers, thus, accelerating and streamlining the development process. The BUiD MSc has accreditation eligibility from the UAE Ministry of Higher Education and Scientific Research. What can I do with an MSc in Information Technology Management? BUiD's MSc in IT Management is a novel programme allowing students to acquire skills that are crucial for career advancement in today's rapidly growing knowledge-economy. Graduates in IT Management will have a competitive advantage over colleagues who only have a background in Programming or Computer Science. During the MSc, essential project management skills are acquired through research based lectures and workshops. These
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include project control and organisational planning, allowing graduates to improve their ability to lead and make strategic decisions. Graduates will also get an extensive experience in a number of cutting edge IT areas, giving them enough confidence to introduce these innovative techniques into their organisations. Members of staff will guide students through state of the art web techniques taught through interactive projects. Lectures and seminars will also address complex issues in designing databases and complex structures, and provide hands-on experience in cutting edge techniques in data-mining and exploration and knowledge management within organisations. The following is a summary of modules that IT management students can take, for more information on the modules please refer to appendix 1. Students is required to take either of the concentrations:
Module Code Module Title Credits
Core: Complete all of the following modules
INF501 Informatics Research Methods 20
INF508 IT Project Management 20
MGT504 Planning, Execution and Control 20
MGT503 People, Culture and Organisation 20
Concentration: Business Intelligence (Student will be required to take all modules)
INF504 Data Mining and Exploration 20
INF506 Knowledge Management 20
Concentration: e-Business Intelligence Concentration (Student will be required to take all modules)
INF 509 E-commerce 20
INF 510 IT Entrepreneurship 20
Independent Research
RES504 Dissertation 60
Total Credits 180
Recommended Study Plans TERM 1 INF501 Informatics Research Methods (Core) INF510 IT Entrepreneurship (e-Business Intelligence) MGT503 People, Culture and Organisation (Core)
TERM 2 MGT504 Planning, Execution and Control (Core) INF504 Data Mining and Exploration (Business Intelligence) TERM 3 INF506 Knowledge Management (Business Intelligence) INF508 IT Project Management (Core) INF509 E-commerce (e-Business Intelligence)
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Data Mining & Exploration Data mining is about analyzing, interpreting, visualizing and exploiting the data that is captured scientific and commercial environments. The course will also feature paper presentations and a each student will undertake a mini-project on a real-world dataset. Knowledge Management The aim of this module is to teach the principles and technologies of knowledge management. A case study approach, as and where appropriate, will be adopted in introducing the course contents. The module covers the fundamental concepts in the study of knowledge and its creation, representation, dissemination, use and re-use, and management. The focus is on methods, techniques, and tools for computer support of knowledge management, knowledge acquisition, and how to apply a knowledge management system using one of the knowledge-based system tools. Informatics Research Methods The aim of this module is to teach the methodologies of and the skills for conducting research in Informatics. It will focus on three main parts: (1) analytical methods, (2) empirical methods, (3) writing and evaluating research. The module will cover: the nature of Informatics and Informatics research; criteria for assessing Informatics research; different methodologies for Informatics research and how to combine them; analytical proof; algorithm and complexity analysis; the design of experiments and evaluations; practical advice on conducting research and numerous research skills including: reading, reviewing, presenting, writing, design, etc. IT Project Management The aim of this module is to provide necessary knowledge to the students about the general principles of Information Technology Management and the management of software projects. The assessment of this module is focused towards management of IT projects, therefore providing students an opportunity to explore the management issues relevant to technical IT projects. e-Commerce In this module students study topics related to creating a business on the web, with particular focus on e-commerce. Students will study the IT issues raised by electronic business and commerce. Techniques and technologies available for designing and implementing e-business and e-commerce applications will be surveyed. Students will have first-hand experience with Web-based tools and services to help design e-Business solutions. IT Entrepreneurship This module provides the students with scientific methodologies for identifying opportunities in the IT space. Students will learn how to create an effective business plan, acquiring funding, establishing a company from scratch and managing in an environment of high growth, high uncertainty and rapid change. The module will include case studies of successful and failed IT entrepreneurial companies and will draw upon the angel investing, venture capital and entrepreneurial communities from guest speakers.
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Planning, Execution and Control This module is designed to provide knowledge and a higher level of understanding of planning, execution and control processes in the management of projects. This covers concepts, models, and methodologies of planning and control of project cost, time and resources. People, Culture and Organisation To gain knowledge and understanding on a wide range of people and culture topics relevant to a project manager. To gain awareness and understanding of a range of perspectives and underpinning techniques for analysing problems. To experience the application of theoretical ideas to work situations through personal reflection. To gain understanding of the theory and practice of creative approaches to problem solving. To create a future learning agenda for personal development. To gain experience and understanding of qualitative concepts and measures with respect to people, culture, and organisations. Dissertation Having successfully completed the six modules in the taught stage of the programme, students who wish to proceed to the master’s degree take the dissertation stage. This final project is intended to give students an opportunity to focus on an aspect of the taught subject matter and investigate it in more detail. This will help them consolidate their capacity for independent study, and to learn some of the techniques needed to conduct research and develop knowledge in the subject area of the programme of study. This is a research project. The only piece of work to be submitted for examination is a dissertation, and this is a written report on the research. There are thus two aspects to consider: the research and the writing. Both are governed by implicit rules common to the discipline of formal research; part of the students’ training is to become familiar with these rules.
3.2.2.1 Premasters
To ensure you have proper management background, ITM students must attend pre-masters intensive course and take an exam. This is offered by Project Management programme, for 3 consecutive days at the start of Term1 & Term2. The last day contains an exam that should be passed in order to enter the programme. For further detail, please contact Godwin, the faculty administrator.
3.2.3 Term-by-Term Plan
The teaching plan to be adopted by Informatics and ITM masters covering three teaching terms per year. This is structured in such a way that both full-time and part-time students can plan their study in an optimal way to finish in a minimum allocated time. The full-time students can take a maximum of three modules per term and the part-time students take typically two modules per term. Module selection is done in consultation with the student's Personal Tutor. In addition students are entitled to attend Study Support sessions equivalent to 1 hour per week on a self-access basis. Student Contact Hours during the Dissertation period are notional as contact is on an individual basis.
3.2.3.1 Postgraduate Diploma (PD Dip)
The majority of the University’s students are in full-time employment and, for some, completion of the dissertation research is not feasible due to substantial work commitments. Students who
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complete all taught modules (120 Cr. Hrs.) but who fail to complete their dissertation and want to get an award can take the Postgraduate Diploma exit route. This exit route provides a valuable and deserved postgraduate qualification in such cases. The taught module structure of the Postgraduate Diploma in Informatics is same as that of MSC in Infomatics (Dissertation) award. However, the students pursuing Postgraduate Diploma award will not be required to take dissertation. PGDip Programme Structure Module Code Module Title Credits
Core: Complete all of the following modules
INF501 Informatics Research Methods 20
INF502 Knowledge Representation & Reasoning 20
INF503 Introduction to Computational Linguistics 20
INF504 Data Mining and Exploration 20
Electives: (Student will be required to take two out of the six modules)
INF505 Knowledge Engineering (pre-requisite INF502, Knowledge Representation & Reasoning)
20
INF506 Knowledge Management 20
INF513 Machine Learning (pre-requisite INF504, Data Mining & Exploration)
20
INF508 IT Project Management 20
INF509 E-commerce 20
INF510 IT Entrepreneurship 20
INF511 Software Systems Design Practical Object-Oriented Analysis and Design with UML
20
INF512 Systems Requirements Engineering 20
INF514 Management Information Systems* 20
Total Credits 120
4. The Academics
Prof. Khaled Shaalan Full Professor of Computer Science Head of Msc. In Informatics, MSc in ITM, PhD in Computer Science, BSc in Computer Science Email: [email protected] Research Impact: http://scholar.google.com/citations?user=keLKdlgAAAAJ Dr. Khaled Shaalan is a full professor of Computer Science at the British University in Dubai (BUiD), UAE. He is an Honorary Fellow at the School of Informatics, University of Edinburgh (UoE), UK. Prof Khaled is an Associate Editor on ACM Transactions of Asian and Low Resource Language Information Processing (TALLIP) editorial board, Association for Computing Machinery (ACM). Dr Khaled has a long experience in teaching in the field of Computer Science for both core and advanced undergraduate and postgraduate levels. He has taught more than 30 different modules at the undergraduate and postgraduate levels.
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Over the last two decades, Dr Khaled has been contributing to a wide range of research topics in Arabic Natural Language Processing, including machine translation, parsing, spelling and grammatical checking, named entity recognition, and diacritization. Moreover, he has also worked on topics in knowledge management, knowledge-based systems, knowledge engineering methodology, including expert systems building tools, expert systems development, and knowledge verification. Nevertheless, Khaled worked on health informatics topics, including context-aware knowledge modelling for decision support in E-Health and game-based learning. Furthermore, Dr Khaled worked in educational topics, including intelligent tutoring, item banking, distance learning, and mobile learning. He has been the principal investigator or co-investigator on research grants from USA, UK, and UAE funding bodies. Dr Khaled has published over 160 referred publications. He has several research publications in his name in highly reputed journals such as Computational Linguistics, Journal of Natural Language Engineering, Journal of the American Society for Information Science and Technology, IEEE Transactions on Knowledge and Data Engineering, Expert Systems with Applications, Software-Practice & Experience, Journal of Information Science, Computer Assisted Language Learning, and European Journal of Scientific Research to name a few. Dr Khaled’s research work is cited extensively worldwide (see his Google Scholar citation indices). He has guided several Doctoral and Master Students in the area of Arabic Natural Language Processing, Healthcare, Intelligent Tutoring Systems, and Knowledge Management. Dr Khaled encourages and supports his students in publishing at highly ranked journals and conference proceedings. Dr Khaled has been actively and extensively supporting the local and international academic community. He is Co-Chair and editor of The International Conference on Arabic Computational Linguistic (ACLing 2015-2018). He has participated in seminars and invited talks locally and internationally, invited to international group meetings, invited to review papers from leading conferences and premier journals in his field, and invited for reviewing promotion applications to the ranks of Associate and Full Professor for applicants from both British and Arab Universities. Dr. Sherief Abdallah Full Professor of Computer Science Email: [email protected] Web: http://homepages.inf.ed.ac.uk/sabdalla/ Research Impact: https://scholar.google.com/citations?user=R7ngExMAAAAJ&hl=en Dr. Sherief Abdallah is a full professor of computer Science at the Faculty of Informatics at the British University in Dubai, and an Honorary Fellow at the University Of Edinburgh, UK. He holds a PhD from the University of Massachusetts at Amherst, the United States. Dr. Sherief Abdallah's research focuses on developing reinforcement learning algorithms that are scalable and have some guarantee of convergence in a multi-agent context. He is also interested in applying machine learning to real and novel problems, including mobile devices, network management, and information retrieval. He collaborated with world-class researchers in the United States, South America, and Europe. He worked on research projects funded by the National Science Foundation (USA), some of which involved hundred researchers from interdisciplinary areas.
19
Dr. Cornelius Ncube Associate Professor Dr Cornelius Ncube is an Associate Professor of Computer Science at the Faculty of Engineering & IT at the British University in Dubai, and an Honorary Fellow at the School of Informatics at the University of Edinburgh, UK. He holds a PhD in Computer Science (City University London, UK), an MSc in Software Engineering (City University London, UK) and BSc(Hons) (Brunel University London, UK). Cornelius’ current core research area is in Systems Engineering with a particular focus on Systems of Systems Engineering (SoSE), Cyber-Physical Systems (CPSs), Systems Security Engineering with Requirements Engineering as a cross-cutting theme. He also has active and on-going research interest in Composition-Based Software Systems (CBSS) development, Opportunistic-Software Systems Development (OSSD) and COTS-Based Systems Development (CBSD). Cornelius has been a Principal Investigator (PI) of the EU funded project T-AREA-SOS and the Mission Assurance and Configuration funded by the UK Ministry of Defence. He has also been a PI on the VANTAGE, NATS-EASM, BANKSEC, GOMOSCE projects. He has published his research in peer-reviewed publications including IEEE Software Journal, Communications of the ACM, Requirements Engineering Journal and the IEEE International Conferences on Software Engineering and on Requirements Engineering. Special recognition include the IEEE Award in 2008 for the Most Influential Paper for work on requirements engineering that had the most influence and impact on the theory or practice of requirements engineering in the last 10 years since its first publication. Email: [email protected] Research Impact: https://scholar.google.com/citations?user=XKaB180AAAAJ&hl=en
5. Module Timetable
Pls. refer to BUiD Website: www.buid.ac.ae Current Students timetables
6. The Dissertation
The dissertation is an essential part of the programme contributing to 60 credits (of the total 180 credits). The dissertation involves both the application of skills learnt in the past and the acquisition of new skills. It allows the students to demonstrate their ability to carry out and organise a major piece of work according to sound scientific and engineering principles.
6.1 Dissertation Guidelines
Dissertations guidelines for submission are available under Study Skills. This includes advice for the initial submission and for the final hardback submission once the dissertation has been marked. These guidelines relate to formatting, layout and presentation only. This information is placed on Blackboard for easy access.
1. The student finishes required courses (in three Terms if full time, and six Terms if part time)
20
2. Completing MSC DISSERTATION TOPIC form 3. Completing MSC DISSERTATION CONTRACT. 4. Completing the dissertation and getting the degree
a. Student submits a research proposal . b. Student submits a progress report . c. Student may submit a draft dissertation to the supervisor(s), along with a
copy to academic services, at least 4 weeks before the submission deadline d. Student submits the dissertation before the submission deadline. One copy
to academic services, one copy to each supervisor, one copy to each examiner (if different), and one copy to the exam coordinator.
e. Student schedules the oral exam within one week after submitting the dissertation with the supervisor(s) and examiner(s).
f. Oral exam takes place. g. Supervisor(s) sends one copy of the final result (after board of examiners
meeting) to the student, and one copy to academic services. h. The student make sure s/he addresses all the comments made by the
examiners fulfils all university requirements. i. The student submits one copy of the final dissertation to his/her main
supervisor, one copy to academic services, one copy of to the dissertation coordinator, and keeps one copy to him/herself.
Additional Information
The final dissertation grade is based primarily on the dissertation itself, and not on any associated work or effort.
If the student needs more information at any point, s/he should contact his supervisor, personal tutor, and dissertation coordinator.
Dissertation Title: dissertation title is tentative (i.e. can be changed) throughout the dissertation process until the final dissertation is submitted.
Dissertation process o Each student is entitled to a total of 10 hours of the supervisors' time
throughout the dissertation process. As supervisors may be busy at different periods, the student needs to
schedule any meeting at least one week beforehand. o Students are encouraged to consult the unit of "study support skills" at an early
stage of their dissertation writing to address any major issue in their writing skill.
If the dissertation is badly written, the board of examiners may ask the student to resubmit his/her dissertation without giving a grade.
o The student is entitled to one feedback of the dissertation before final submission, provided the student submits the dissertation at least four weeks before the submission deadline.
The feedback shall address high-level critique of the dissertation and need not address any style issues or syntactic mistakes.
The feedback remains advisory and does not guarantee any grade even if the student addresses the feedback concerns. The quality of the dissertation remains the responsibility of the student.
22
Microsoft Word
Document
Appendix 1- Module Syllabi Notice that approved changed will be announced to students through module coordinator, most probably the instructor.
Module Title Informatics Research Methods
Module Code INF501
Credits 20
Pre-requisites None
Co-requisites None
Module
Description
The aim of this module is to teach the methodologies of and the
skills for conducting research in Informatics. The module will
cover, among other topics: the nature of Informatics and
Informatics research; criteria for assessing Informatics research;
analytical proof; the design of experiments and evaluations;
practical advice on conducting research and numerous research
skills including: reading, reviewing, presenting, writing, design,
etc.
Instruction and
Assessment
Study Format Hours
Lectures 30
Coursework assignment hours 100
Laboratories 6
Exams 0 1Private Study 64
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination
Assessed Assignments 100
Oral Presentations -
Term 1, 20XX-20XX
Module
Coordinator
Prof. Sherief Abdallah
Office Hours By Appointment
1Private Study covers time spent reading over lecture notes, texts, recommended texts,
scientific papers, library searches and module information reviews, etc.
Learning Outcomes
Below are outcomes of the module. The students will be able to:
Knowledge K1: Demonstrate knowledge of research methodologies related to IT.
Skills
S1: Formulate scientific hypotheses clearly and precisely
23
S2: Critically analyse and write academic reviews of existing research
Aspects of Competence
Autonomy and Responsibility
C1: Work independently on a research topic
Role in Context
C2: Communicate scientific knowledge at different levels of abstraction.
Self-Development
C3: Manage own time effectively
C4: Demonstrate understanding of ethical issues related to Informatics
Module Learning Outcomes V.S. Program Learning Outcomes Knowledge Skill Competence Module
Learning
Outcomes
(MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 x
Skill S1 X
Skill S2 X
Competence C1 x
Competence C2 x
Competence C3 X
Competence C4 x
Syllabus
Breakdown by week:
Part I: Writing, Evaluating and Presenting Research:
1. Introduction to the Scientific Method; overview of Informatics research
methodologies
2. Evaluating and reviewing academic papers; writing papers, theses and
proposals
Part II: Empirical Methods
3. Exploratory data analysis
4. Experiment design and hypothesis testing
5. Use of statistical packages; issues in simulation
6. Ethical Issues, Qualitative Analysis & Case Studies
7. Student presentations
Part III: Theoretical Methods
8. Mathematical proof techniques
9. Algorithm analysis as application of theoretical analysis
Relevant QAA Computing Curriculum Sections:
24
Theoretical Computing, Professionalism, Simulation and Modelling, Information
Systems
Assessment 1, 2
There are 3 items of assessed coursework.
Handed Due Deliverable Weight Assessed LOs
1 Week 1 Week 3 Report reviewing 2
research papers
20 S2,C2,C4
2 Week 3 Week 6 Presentation on a
research topic of
choice
30 S2,C2,C3,C4
3 Week 5 Week 9 Report describing mini
research project
50 K1,S1,C1,C2,C3,C4
Refer to the description of each assignment for more details.
Module Text(s)
There is no textbook. We will rely on web resources and papers. Below are
recommended readings.
1. Olsson, H. H. (2018, May). Challenges and Strategies for
Undertaking Continuous Experimentation to Embedded
Systems: Industry and Research Perspectives. In Agile
Processes in Software Engineering and Extreme Programming:
19th International Conference, XP 2018, Porto, Portugal, May
21–25, 2018, Proceedings (Vol. 314, p. 277). Springer.
2. Kohavi, R., & Longbotham, R. (2017). Online controlled
experiments and a/b testing. In Encyclopedia of Machine
Learning and Data Mining (pp. 922-929). Springer US.
3. Eckles, D., Karrer, B., & Ugander, J. (2017). Design and
analysis of experiments in networks: Reducing bias from
interference. Journal of Causal Inference, 5(1).
4. Buchert, Tomasz, et al. "A survey of general-purpose
experiment management tools for distributed systems." Future
Generation Computer Systems 45 (2015): 1-12.
5. Tedre, M., & Moisseinen, N. (2014). Experiments in
computing: A survey. The Scientific World Journal, 2014.
1 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
2 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval)
25
6. Campos, P. G., Díez, F., & Cantador, I. (2014). Time-aware
recommender systems: a comprehensive survey and analysis of
existing evaluation protocols. User Modeling and User-
Adapted Interaction, 24(1-2), 67-119.
7. Jens Gustedt, Emmanuel Jeannot, and Martin Quinson,
“Experimental methodologies for large-scale systems: a
survey”. Parallel Processing Let. 19(3) pp. 399-418, 2009.
8. Ron Kohavi, Roger Longbotham, Dan Sommerfield, and
Randal M. Henne, “Controlled experiments on the web: survey
and practical guide”. Data Mining and Knowledge Discovery
18(1), pp. 140-181, Feb 2009.
9. D. G. Feitelson, Experimental Computer Science: The Need
for a Cultural Change. Manuscript, 2005.
10. B. A. Kitchenham, S. L. Pfleeger, L. M. Pickard, P. W. Jones,
D. C. Hoaglin, K. El Emam, and J. Rosenberg, “Preliminary
Guidelines for Empirical Research in Software Engineering”.
IEEE Trans. Softw. Eng. 28(8), pp. 721-734, Aug 2002.
11. D. S. Johnson, A Theoretician's Guide to the Experimental
Analysis of Algorithms. Nov 2001.
12. W. F. Tichy, “Should Computer Scientists Experiment More?”.
Computer 31(5) pp. 32-40, May 1998.
13. Neideen, Todd, and Karen Brasel. "Understanding statistical
tests." Journal of surgical education 64.2 (2007): 93-96.
14. Introduction to Hypothesis testing
http://wise.cgu.edu/hypomod/
15. Introduction to Mathematical Arguments
http://math.berkeley.edu/~hutching/teach/proofs.pdf
Recommended Reading
1. Zobel J. Writing for Computer Science. Springer, 2nd ed. edition (2004)
2. Velleman D. J. How to Prove It: A Structured Approach. Cambridge
University Press, 2 edition (2006)
Module Title Knowledge Representation and Reasoning
Module Code INF502
Credits 20
Pre-requisites None
Co-requisites None
Module Description This module provides the basis for the understanding and use of
Knowledge Representation and Reasoning techniques in AI systems
in general, and knowledge-based systems in particular. The module
covers notions of representation and the relationship between
representation and that which is represented, along with issues of the
resources required to manipulate such representations. The focus is
on different logic-based representation languages and proof search
using logical calculi, but other approaches are also discussed.
Instruction and Study Format Hours
26
Assessment Lectures 28
Coursework assignment hours 62
Laboratories 8
Exams 2 1Private Study 100
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations 0
Term 2, 2016-2017
Module Coordinator Dr Khaled Shaalan
Office Hours By Appointment
1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library searches and module
information reviews, etc.
Learning Outcomes
On successful completion of this module the student will be able to:
Knowledge
K1: Describe the knowledge representation and reasoning techniques that contribute
to building AI systems in all its stages
K2: Demonstrate knowledge of advanced knowledge representation and reasoning
techniques.
Skill
S1: Use a range of knowledge representation and reasoning techniques to apply
knowledge specifications
S2: Represent a problem using various formal techniques and languages.
Aspects on Competence
Autonomy and Responsibility
C1: Work independently and proactively to formulate ideas and execute plans by
which to evaluate these ideas and produce research reports in knowledge
representation and reasoning
Role in Context
C2: develop a broad knowledge of independent design and management of their
learning activities in knowledge representation and reasoning.
Self-Development
C3: critically evaluate intellectual and academic work
Module Learning Outcomes V.S. Program Learning Outcomes INF502
Knowledge
Representation
and Reasoning
Knowledge Skill Competence
Module Learning
Outcomes (MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X
Syllabus
Breakdown by week:
27
1. Lecture: Representation and the relationship between symbolic representations and
represented structures (syntax and semantics). Deduction as inference.
2. Lecture: Syntax and semantics of propositional logic; inference procedures including
truth-table enumeration, natural deduction, and resolution on conjunctive normal
forms.
3. Lecture: Syntax and semantics and first-order logic. Inference procedures using
generalised modus ponens, including forward-chaining and backward-chaining.
4. Lab: Using a suitable programming language (e.g. Prolog) to encode knowledge and
apply deductive reasoning through interpreters.
5. Lecture/Lab: Blind search techniques and their implementation (depth-first, breadth-
first, iterative deepening)
6. Lecture/Lab: Informed search techniques and their implementation (best-first, greedy,
uniform cost, A*)
7. Lecture: Constraints and constraint satisfaction problems; constraint propagation and
other solution techniques for CSPs.
8. Lecture: Representations and reasoning with uncertainty.
9. Lecture: Bayesian networks and applications.
Relevant QAA Computing Curriculum Sections
Artificial Intelligence, Computer Based Systems, Developing Technologies, Intelligent
Information Systems Technologies
Assessment 3, 4
The assessment will relate to the learning outcomes and will be 40% by a final exam, and
60% (20% + 40%) by assignment reports.
Sequence Handed Due Topic and Associated Weight Learning Outcomes Assessed
1 Week 4 Week 7 Assignment 1 – 20% K1, K2, C1, C3
2 Week 6 Week 9 Assignment 2 – 40% S1, S2, C1, C2
Week 11 Week 11 Final Exam-40% K1, K2, S1,S2
Assignment
Students are required to submit a report (5000 words limit) about critical reading in language
processing, and a practical problem report (7,000 words limit), as per the due dates.
Exam
Final exam which worth 40% of final mark will be based on topics treated from week 1 to
week 9.
Module Text(s)
1. Russell, S. and Norvig, P. (2010). Artificial intelligence: a modern approach. 3rd ed.
Upper Saddle, NJ: Prentice Hall. (new edition is expected this year)
2. Bratko, I. (2012). Prolog programming for artificial intelligence. 4th ed. Harlow:
Addison Wesley.
Recommended Reading
3 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university policies and procedures carefully in the university student
handbook so that you are aware of all university procedures and abide by them to avoid penalties. Please note that
all written assignment will be checked using specified plagiarism detection software
4 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical dimensions, the
tutor is responsible for initial guidance and the student is directed to use relevant approval forms and procedures.
(see policy 9.3.2 Frame Work for Research Ethics Approval).
28
1. Brachman, R. and Levesque, H. (2004). Knowledge representation and reasoning.
Amsterdam: Elsevier.
2. Luger, G. (2008). Artificial intelligence: structures and strategies for complex
problem solving. 6th ed. Addison Wesley.
3. Sowa, J. F. (2000). Knowledge representation: logical, philosophical, and
computational foundations. Australia: Course Technology.
4. Van Harmelen, F., Lifschitz, V. and Porter, B. (eds). (2008). Handbook of knowledge
representation. Amsterdam: Elsevier.
Module Title Introduction to Computational Linguistics
Module Code INF503
Credits 20
Pre-requisites None
Co-requisites None
Module Description
This is an introductory course that presumes no prior familiarity with
Computational Linguistics. This course provides an introduction to
the basic theory and practice of computational approaches to natural
language processing. The module cover the following topic:
introduction to programming in Python & NLTK, tokenization, part-
of-speech tagging, context-free grammars for natural language,
evaluating a natural language processing system, parsing techniques,
information extraction, Arabic language processing. The course also
provides an introductory insight into the state of current research in
Computational Linguistics.
Instruction and
Assessment
Study Format Hours
Lectures 28
Coursework assignment hours 62
Laboratories 8
Exams 2 1Private Study 100
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term Term2
Module Coordinator Dr Khaled Shaalan [email protected]
Office Hours 4-6pm on the class date or by appointment
1Private Study covers time spent reading over lecture notes, tutorials, texts, recommended
texts, preparation for examination, library searches and module information reviews, etc.
Learning Outcomes
On successful completion of this module the student will be able to:
Knowledge
K1: Describe the natural language processing tasks that contribute to building
computational linguistics systems in all its stages
29
K2: Demonstrate knowledge of advanced computational linguistics techniques and
tools.
Skill
S1: Use a range of natural language processing techniques to apply linguistic
specifications, such as regular expressions and grammars
S2: Design a small experiment to evaluate the performance of a natural language
processing system and will be capable of accurately interpreting quantitative results
of the experiment
Aspects on Competence
Autonomy and Responsibility
C1: work independently and proactively to formulate ideas and execute plans by
which to evaluate these ideas and produce research reports in natural language
processing and linguistics resources.
Role in Context
C2: develop a broad knowledge of independent design and management of their
learning activities in computational linguistics
Self-Development
C3: critically evaluate intellectual and academic work
Module Learning Outcomes V.S. Program Learning Outcomes INF503
Introduction
to
Computational Linguistics
Knowledge Skill Competence
Module Learning Outcomes
(MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy & Responsibility
Role in context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO13
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X
Syllabus
Breakdown by week:
10. Lab: Introduction to NLP tools: Basic object types, control flow, functions
11. Lab: Introduction to Natural Language Toolkit (NTLK): corpora operations.
12. Lecture: Words, tokenization, and regular expressions
13. Lecture: Ngram models and (POS) tagging.
14. Lecture: Presentations. Evaluating natural language processing systems.
15. Lecture: Syntactic analysis and Parsing: context-free grammars, parsing techniques,
partial parsing, and chunking.
16. Lecture: Natural Language Applications: Information extraction
17. Lecture: Natural Language Applications: Machine Translation
18. Lecture: Arabic language processing: morphology and parsing
Relevant QAA Computing Curriculum Sections
Artificial Intelligence, Computer Based Systems, Developing Technologies, Intelligent
Information Systems Technologies
Assessment
The assessment will relate to the learning outcomes and will be 40% by a final exam, and
60% (20% + 40%) by assignment reports.
30
Sequence Handed Due Topic and Associated Weight Learning Outcomes Assessed
1 Week 1 Week 4 Assignment 1– 20% K1, K2, C1, C3
2 Week 1 Week 9 Assignment 2 – 40% S1, S2, C1, C2
3 Week 11 Week 11 Final Exam-40% K1, K2, S1
Assignment
Students are required to submit a report (5000 words limit) about critical reading in natural
language processing, and a practical problem report (7,000 words limit), as per the due dates.
Exam
Final exam which worth 40% of final mark will be based on topics treated from week 1 to
week 9.
Module Text(s)
1. Daniel Jurafsky and James H.Martin, Speech and Language Processing (second
edition), Pearson Prentice Hall, 2009. (new edition is expected this year)
2. Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with
Python, O'Reilly, 2009.
Recommended Reading
1. Mark Lutz and David Ascher, Learning Python. O'Reilly, 1999.
2. Ruslan Mitkov, The Oxford Handbook of Computational Linguistics, Oxford
university press, USA, Feb 2005.
3. James Allen, Natural Language Understanding, Benjamin Cummings, Second
Edition, 1994.
4. Ricardo Baeza-Yates, Berthier Ribeiro-Neto, Modern Information Retrieval,
Addison-Wesley 1999.
5. Chris Manning and Hinrich Schutze, Foundations of Statistical Natural Language
Processing. MIT Press 1999.
6. Steven Abney. "Statistical Methods and Linguistics." In: Judith Klavans and Philip
Resnik (eds.), The Balancing Act: Combining Symbolic and Statistical Approaches to
Language. The MIT Press, Cambridge, MA. 1996.
7. Arabic Computational Morphology: Knowledge-Based and Empirical Methods by
Abdelhadi Soudi, Antal Van Den Bosch (Editor), Gnter Neumann (Editor), Springer,
2007
8. Nizar Habash, Introduction to Arabic Natural Language Processing, Synthesis
Lectures on Human Language Technologies is edited by Graeme Hirst, Morgan &
Claypool Publishers, CA, USA, 2009
9. Supplementary notes on Python and NLTK.
10. Supplementary papers on natural language processing, in particular, Arabic.
Module Title Data Mining and Exploration
Credits 20
Module Code INF504
Pre-requisites Familiarity with elementary mathematics, including algebra and calculus is
essential. A reasonable knowledge of computational, logical, geometric, and set-
theoretic concepts, vectors and matrices, together with a basic grasp of
probability is strongly recommended.
Co-requisites None
31
Module
Description
Data mining is about analyzing, interpreting, visualizing and exploiting the data
that is captured scientific and commercial environments. This module provides
students with an opportunity to gain an in depth understanding of the theories and
issues related to mining and exploring data, ranging from statistical summaries, to
visualization, to classification and clustering. Practical case studies will be used
for illustration.
Study Format Hours
Instruction and
Assessment
Lectures 27
Tutorials/Laboratories 9
Assessed assignments 64 1Private Study 100
Total 200
Assessment %
Assessment
Weightings (%)
Written Examination 40
Assessed Assignments 60
Oral Presentations 0
Total 100
Term 2, 2013-2014
Module
Coordinator
Dr Sherief Abdallah
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
Below are outcomes of the module. The students will be able to:
Knowledge K1: Demonstrate knowledge of data mining techniques, including the strength and
weaknesses of different techniques
Skills
S1: Choose and justify an appropriate data mining technique, given a problem.
Aspects of Competence
Self-Development
C1: Manage own time effectively
C2: critically evaluate intellectual and academic work
Module Learning Outcomes V.S. Program Learning Outcomes
INF504 Data Mining and Exploration
Knowledge Skill Competence
Module Learning
Outcomes (MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy & Responsibility
Role in context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1
x
Skill S1 X X
Competence C1
X
Competence C2
x
32
Syllabus
Breakdown by week:
1. Introduction (e.g. components of data mining algorithms, data mining tasks)
2. Data types & preprocessing
a. Lab tutorial about pre-processing data
3. Visualization & exploratory data analysis.
a. Lab tutorial about PCA
4. Overview of important classification techniques (e.g. k-nearest neighbour, decision
trees, and neural networks) and performance evaluation
a. Lab tutorial about classification
5. Clustering (e.g. K-means and hierarchical clustering)
6. Association rules, retrieval-by-content (text retrieval)
7. Graph mining & Time series 1 (short time series)
a. Lab tutorial about text mining and cluster analysis
8. Time series 2 (long time series) & Advanced Topics (recommendation systems)
9. Presentations and Revision
Relevant QAA Computing Curriculum Sections: Artificial Intelligence
Assessment 5, 6
There are 3 items of assessment. Please consult the coursework descriptor for assessment
details, including word limit and evaluation criteria.
Handed Due Deliverable Weight Assessed LOs
1 Week 1 Week 4 Critical survey of a topic
related to data mining
20 K1,C1,C2
2 Week 1 Week 9 Report describing research
project in data mining
40 K1,S1,C1,C2
3 Week 11 Week 11 Final Exam 40 K1,S1,C1
Module Core Text(s)
We will primarily rely on the following textbook.
Tan, P., Steinbach, M., Kumar, V. (2018). Introduction to data mining (2nd Edition).
Boston, MA: Addison Wesley.
Recommended Readings
Below is a list of recommended readings
5 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university policies and procedures carefully in the university student
handbook so that you are aware of all university procedures and abide by them to avoid penalties. Please note that
all written assignment will be checked using specified plagiarism detection software
6 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical dimensions, the
tutor is responsible for initial guidance and the student is directed to use relevant approval forms and procedures.
(see policy 9.3.2 Frame Work for Research Ethics Approval).
33
Abdallah, S. (2018). An Intelligent System for Identifying Influential Words in Real-Estate
Classifieds. Journal of Intelligent Systems, 27(2), 183-194.
L'Heureux, A., Grolinger, K., ElYamany, H. F., & Capretz, M. (2017). Machine Learning
with Big Data: Challenges and Approaches. IEEE Access.
Sapountzi, A. and Psannis, K.E., 2016. Social networking data analysis tools & challenges.
Future Generation Computer Systems.
Sarstedt, M., & Mooi, E. (2014). Cluster analysis. In A concise guide to market research (pp.
273-324). Springer, Berlin, Heidelberg.
Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, Albert-László Barabási (2011)
Flavor network and the principles of food pairing, Scientific Reports 1, Article number: 196
doi:10.1038/srep00196
Acerbi A, Lampos V, Garnett P, Bentley RA (2013) The Expression of Emotions in 20th
Century Books. PLoS ONE 8(3): e59030. doi:10.1371/journal.pone.0059030
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U. (2006). Complex networks:
structure and dynamics. Physics Reports. Vol. 424 (4-5), February 2006, Section
2.1.1.
Fawcett, T. (2004). ROC graphs: notes and practical considerations for researchers [online].
Section 1 and 2. Available at: http://binf.gmu.edu/mmasso/ROC101.pdf
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U. (2006). Complex networks:
structure and dynamics. Physics Reports. Vol. 424 (4-5), February 2006, Section 2, 4
and 6.
Keogh, E. (2002a). ‘Exact indexing of dynamic time warping’, in 28th International
Conference on Very Large Data Bases. Hong Kong, pp 406-417.
Keogh, E. and Kasetty, S. (2002b). ‘On the need for time series data mining benchmarks: a
survey and empirical demonstration’, in The 8th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002.
Edmonton, Alberta, Canada, pp 102-111.
Lin, J., Keogh, E., Lonardi, S., Lankford, J. P. & Nystrom, D. M. (2004). ‘Visually mining
and monitoring massive time series’, in Proceedings of the Tenth ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining. Aug 22-25.
Seattle, WA.
Ratanamahatana, C.A. and Keogh, E. (2004). ‘Everything you know about dynamic
time warping is wrong’ in the Tenth ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD-2004): Third Workshop on
Mining Temporal and Sequential Data, August 22-25, Seattle, WA.
Data mining and Exploration
Coursework
Grade 60% from total
Team assignments: 2-3 members
1. Description:
34
The coursework consists of two main components: 1) a critical survey, due on the 4th week,
and 2) a research project with final report and presentation that are due on the 9th week. This
is a team project; each team shall consist of 2-3 members.
2. Milestones:
1. Critical Survey [due on the 4th Week, weight: 20%]
2. Final Report [due on the 9th Week, weight: 40%]
Due time is at the beginning of the first lecture in the due week (i.e. Wednesday at 6pm)
The following is a summary of coursework steps and milestones.
3. Critical Survey
This year all projects should be related to real UAE data. In particular, you are highly
encouraged to use data from Dubai Pulse (https://www.dubaipulse.gov.ae/). For example:
Analyzing car accident data
Analyzing bus ridership
Analyzing real estate transactions
Each team needs to survey at least 12 papers. Use google scholar and other online resources
in order to obtain papers.
Deliverables:
A survey, no more than 5000 words, single column, describing briefly and precisely
Title of the topic (to be surveyed) and the team members (name and ID of each).
The topic should be related to one of the datasets on Dubai pulse (otherwise you
need instructor’s approval).
Division of work: what did each team member do?
Survey: summarize the different papers, highlighting:
What is the data that were analyzed? Is the data available (e.g. can you
download it?)
how the data were represented
what analysis was conducted on the data and what patterns/findings were
discovered
the more integrated the survey is, the better (i.e., not simple collection of paper
summaries.)
Proposal: From what you read, what ideas you think are worth pursuing?
4. Final Report
Choose an idea from the previous report to pursue in further detail. It is important to make
sure the data you need is available. Analyze your data set and report the results.
Deliverables:
1- a report, no more than 6000 words, single column, describing briefly:
Title of the topic.
Division of work: what did each team member do?
Setting
o What tools did you use?
35
o What measurements did you use for evaluation?
Results:
o At least 3 figures plotting different measure combinations. Each figure
must have a caption summarizing the interesting patterns in the figure (do
NOT include a figure without a caption describing it)
Analysis:
o Comment on the results. This is the heart of the analysis. Point out what
relationships you observed and what interesting findings you discovered.
2- Oral presentation, 20 minutes, about the work done in the project. Each team member
should participate in the presentation, to describe what s/he did. The presentation
should include:
Brief introduction
o Problem
o Related work
Approach
o Technique used & why chosen?
o Tools used & why chosen?
Results
o Main results
o Analysis & findings
Conclusion
o What is the main lesson
7. Important Notes
a) More than one team can choose the same topic.
b) All deliverables to be submitted through turnitin on the blackboard.7
c) Acknowledge all assistance on assignments and all references.
d) Use blackboard for questions
8. Late submission
According to the university policy, every day late you lose (-) 2%, up to 5 working days
excluding the weekend and holidays. If you are late more than the 5 days you will get zero.
Notice that. It is the student responsibility to make sure that the deliverable satisfies the
requirements.
7 Turnitin is used to detect plagiarism. Plagiarism is a very serious offence that can lead to being expelled from the
programme.
36
Marking Scheme
The grading will be broken down based on the following criteria:
Deliverable Criterion Max Actual
Survey Clarity & quality of writing 8%
Quantity & Quality of surveyed papers 4%
Originality of proposed ideas 4%
Individual contribution 4%
Total for Survey 20%
Written
Report
difficulty of the problem (amount of work) &
originality of the solution (how it differs from related
work)
6%
Overall organization, readability & academic writing
style
6%
Results, Depth of analysis, Critical evaluation of own
work (discussion of results & conclusion)
10%
Individual contribution 6%
Referencing (style and completeness) 2%
Oral
Presentation
Clarity of slides & presentation 4%
Innovation & quality of slides (e.g. illustrative
animation)
2%
Individual understanding 4%
Total for Report 40%
Module Title Knowledge Engineering
Module Code INF505
Credits 20
Pre-requisites INF502 Knowledge Representation and Reasoning or approval
of the instructor
Co-requisites None
Module
Description
This module introduces a variety of methodologies important to
the development of modern knowledge-based systems (KBSs)
and their applications, especially pertaining to the Semantic
Web. The module covers topics regarding different processes
within a KBS lifecycle, ranging from knowledge capture and
analysis, systems design and implementation, to knowledge
maintenance and system evaluation. Students will learn about
the latest applications of KBS in building intelligence into Web
applications, and will build a knowledge-based Web application.
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 62
Laboratories 0
Exams 2
1Private Study 100
Total 200
37
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term Term3
Module
Coordinator
Dr Sherief Abdalllah
Office Hours 4-6pm on the class date or by appointment
1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
On successful completion of this module the student will be able to:
Knowledge
K1: Describe the life cycle of a Knowledge-Based System and its key
methodologies
K2: Demonstrate knowledge of advanced methodologies for developing
knowledge-based systems.
Skill
S1: Select, describe and critique alternative methodologies for the
development and application of knowledge-based systems in a given
application area.
S2: Evaluate systems in terms of their knowledge properties
Aspects on Competence
Autonomy and Responsibility
C1: work independently on problem analysis, systems design and
implementation to address a given Knowledge Engineering problem, using
appropriate Semantic Web and AI programming techniques.
Role in Context
C2: use key knowledge modelling abstraction in a variety of settings
Self-Development
C3: critically evaluate intellectual and academic work
Module Learning Outcomes V.S. Program Learning Outcomes INF505
Knowledge
Engineering Knowledge Skill Competence
Module Learning
Outcomes (MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO13
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X
Syllabus
38
Breakdown by week:
1. Fundamentals of Web page development.
2. Representing structured documents on the Web using XML;
manipulating and querying XML; the idea of the Semantic Web and
linked data.
3. Ontologies for structuring knowledge and combining disparate
information; RDF, RDF Schema, and OWL for Web knowledge
engineering;
4. Description Logics as formal foundations of ontologies
5. Ontology editing tools and programming libraries
6. Description Logic reasoners and other ontology reasoning techniques
7. Methodologies for developing ontologies and knowledge-based
systems
8. Structured interview based knowledge elicitation, automated
knowledge acquisition from historical data, knowledge acquisition
methodologies.
9. Distributed multi-agent architectures, shared and common knowledge,
evaluation of reasoning about multiple agents.
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Computer Based Systems, Developing Technologies,
Intelligent Information Systems Technologies
Assessment
1. The final examination will contribute 40% of the final assessment.
2. The assessed assignments will contribute 60% of the final assessment and consist
of the following items.
Sequence Handed Due Topic and Associated
Weight
Learning Outcomes Assessed
1 Week 1 Week 4 Assignment 1– 20% K1, K2, C1, C3
2 Week 1 Week 9 Assignment 2 – 40% S1, S2, C1, C2
3 Week 11 Week 11 Final Exam-40% K1, K2, S1
Assignment
Students are required to submit a report (5000 words limit) about critical reading in
Knowledge Engineering, and a practical problem report (7,000 words limit), as per
the due dates.
Exam
Final exam which worth 40% of final mark will be based on topics treated from week
1 to week 9.
Module Text(s)
1. Grigoris Antoniou, Paul Groth, Frank van Harmelen, Rinke Hoekstra. A
Semantic Web, MIT Press, 2012.
Recommended Reading
39
1. Y. Shoham and K. Leyton-Brown. Multiagent Systems : Algorithmic, Game-
Theoretic, and Logical Foundations. Cambridge University Press, 2009.
2. F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. Patel-Schneider
(Editors). The Description Logic Handbook. Cambridte University Press, 2003
3. Schreiber, G., Akkermans, H., and Anjewierden, A. Knowledge Engineering
and Management: The CommonKADS Methodology. MIT Press, 1999.
Module Title Knowledge Management
Module Code INF506
Credits 20
Pre-requisites None
Co-requisites None
Module
Description
The aim of this module is to teach the principles and technologies
of knowledge management. A case study approach, as and where
appropriate, will be adopted in introducing the course contents.
The module covers the fundamental concepts in the study of
knowledge and its creation, representation, dissemination, use and
re-use, and management. The focus is on methods, techniques,
and tools for computer support of knowledge management,
knowledge acquisition, and how to apply a knowledge
management system using one of the knowledge-based system
tools.
Instruction and
Assessment
Study Format Hours
Lectures 32
Coursework assignment hours 62
Laboratories 4
Exams 2 1Private Study 100
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term
Module
Coordinator
Dr Khaled Shaalan
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
Upon completion of the module the students will be able to:
Knowledge
K1: Describe the knowledge management processes that contribute to building
knowledge management systems in all its stages
K2: Demonstrate knowledge of advanced knowledge management techniques
and tools.
40
Skill
S1: Apply learned concepts to select knowledge management techniques and
tools that are appropriate for specific organisation problem.
S2: Apply knowledge management solutions for actual organizational
problems
Aspects on Competence
Autonomy and Responsibility
C1: Utilize knowledge management systems to assist in smooth running of
organization processes.
Role in Context
C2: Manage own time effectively.
Self-Development
C3: Conduct a case study in Knowledge Management and write academic
review report describing lessons learned
Module Learning Outcomes V.S. Program Learning Outcomes INF506
Knowledge
Management Knowledge Skill Competence
Module
Learning
Outcomes (MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X X
Syllabus
Breakdown by week:
1. Lecture: Introducing knowledge management. Explaining the nature of
knowledge.
2. Lab: Knowledge management building tools.
3. Lecture: Knowledge Management Solutions
4. Lecture: Organizational Impacts of Knowledge Management
5. Lecture: Factors Influencing Knowledge Management. Knowledge
Management Lecture: Assessment of an Organization.
6. Lecture: Preserving and Applying Human Expertise: Knowledge-Based
Systems. Using Past History Explicitly as Knowledge: Case-Based Systems.
7. Lecture: Knowledge Elicitation—Converting Tacit Knowledge to Explicit.
8. Lecture: The Computer as a Medium for Sharing Knowledge.
9. Lecture: Examples of Knowledge management systems.
Relevant QAA Computing Curriculum Sections:
Artificial Intelligence, Developing Technologies, Intelligent Information Systems
Technologies
41
Assessment 8, 9
The assessment will relate to the learning outcomes and will be 40% by a final exam,
and 60% (20% + 40%) by assignment reports.
Sequence Handed Due Learning Outcomes
Assessed Topic and Associated
Weight
1 Week 4 Week 6 K1, K2, S1 Assignment 1– 20%
2 Week 6 Week 8 S2, C1, C2, C3 Assignment 2 – 40%
3 Week 11 Week 11 K1, K2, S2 Final Exam-40%
Assignment
Students are required to submit a report (5000 words limit) about practical problem,
and a critical analysis report (7,000 words limit), as per the due dates.
Exam
Final exam which worth 40% of final mark will be based on topics treated from Week
1 to Week 9.
Module Text(s)
1. Irma Becerra-Fernandez, Rajiv Sabherwal, Knowledge Management:
Systems and Processes, Routledge; 2 edition (December 17, 2014), ISBN-
10: 0765639157, ISBN-13: 978-0765639158
Recommended Reading
2. Kimiz Dalkir, Knowledge Management in Theory and Practice, 2nd
Edition, 2011 Boston, MA: MIT Press.
3. Irma Becerra-Fernandez, Rajiv Sabherwal, Knowledge Management
Systems and Processes, 2010, M.E. Sharpe
4. Elias M. Awad, Hassan M. Ghaziri. Knowledge Management. 2010, North
Garden, V.A., International Technology Group Ltd, ISBN:
9780692004883, 0692004882.
5. Irma Becerra-Fernandez, Avelino Gonzalez, Rajiv Sabherwal,Knowledge
Management: Challenges, Solutions, and Technologies, ISBN 0-13-
101606-7, Copyright 2004. Prentice Hall.
6. Amrit Tiwana (2002). The Knowledge Management Toolkit: Orchestrating
IT, Strategy, and Knowledge Platforms (2nd Edition). Prentice Hall.
ISBN: 013009224X.
Module Title IT Project Management
Module Code INF508
Credits 20
Pre-requisites None
Co-requisites None
8 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
9 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval)
42
Module Description In this module students study IT project management
activities. Covered topics include software systems
engineering, project planning and management, quality
assurance, and strategic planning. The student will learn to
manage software as a distinct project, use specifications and
descriptions, make use of structured techniques, complete
reviews and audits, confirm product development with
planned verification, and validation and testing. Students will
work with essential tools and methodologies for managing an
effective IT project, including software for version control,
and project management.
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 62
Laboratories
Exams 2 1Private Study 100
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 30
Assessed Assignments 50
Oral Presentation 20
Term Term 3
Module
Coordinator
Dr Cornelius Ncube
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
Upon completion of the module the students will be able to:
Knowledge
K1: Demonstrate advanced knowledge of the state-of-the-art methodologies in IT
project management.
K2: Identify the Project Management Body of Knowledge, as agreed upon by
established practitioners
Skills
S1: Manage project goals, constraints, deliverables, performance criteria, quality
control needs, and resource requirements as defined by the project stakeholders
S2: Assess the effectiveness of the project team and suggest ways to improve the
process in the future
S3: Facilitate communication, negotiation, and collaboration with all stakeholders to
ensure the successful completion of information technology projects
Aspects of Competency
Autonomy and Responsibility
C1: Work effectively and professionally in a team-based development.
43
Role in Context
C2: Manage relationships and resolve conflict to establish motivation and promote
positive organizational change
Self-development
C3: Display an appreciation of cultural differences and respect for diversity when
managing information technology projects.
Module Learning Outcomes V.S. Program Learning Outcomes Knowledge Skill Competence INF508 IT Project
Management
PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Skill S3 X
Competence C1 X
Competence C2 X
Competence C3 X
Syllabus
Breakdown by week: (9 lectures of 4 hrs each)
Week 1 Introduction, IT management software (e.g. MS project)
Weeks 2-3 IT lifecycle: concept of operations, requirements specifications,
design and development, delivery, verification, validation and
post audit
Week 4 Role of documentation in IT PM and software maintenance.
Week 5 SW Configuration management, SW testing and quality
assurance
Week 6 The Scrum Framework: become a ScrumMaster
Week 7 Project management: PMBOK to agile
Week 8 Strategic planning & Risk Management
Week 9 IT project management and organization strategy: case study
Assessment 10, 11
10 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
11 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval )
44
The assessment will relate to the learning outcomes and will be 30% by a final exam,
and 70% (50% group project report+ 20% group presentation) by a project report and
presentation
Sequence Handed Due Topic and Associated Weight Learning Outcomes Assessed
1 Week 1 Week 12 Project Report – 50% K1, K2, S1, S2. S3,, C1, C2, C3,
2 Week 1 Week 10 Group Presentation – 20% K2, S2, C2,
3 Week 11 Week 11 Final Exam-30% K1,K2, S2
3. The assessed assignments will contribute 70% of the final assessment. This part of
the assessment will consists of one team project where students will apply the
techniques and the software tools they learn in class in managing an IT project.
The team project should cover the management activities of all the major life-
cycle development stages from requirements analysis, design and implementation.
Key deliverables should include requirements specification document, project
management plan, description of the chosen SDLC methodology and a working
prototype system. The project report is worth 50%
4. Each team will do a 20-minute oral presentation and should demonstrate
individual contribution of each team member. This part is worth 20%
5. The final examination will contribute 30% of the final assessment mark based on
topics covered in the class from Week 1 to Week 9.
Module Texts
1. Information Technology Project Management (8th Edition), Kathy Schwalbe,
2015, ISBN-13: 978-1285452340; ISBN-10: 1285452348
Recommended Reading
1. The Project Manager’s Guide to Software Engineering Best Practices, Mark
Christensen, Richard H. Thayer, 2002, Wiley-IEEE Computer Society Press,
ISBN: 978-0-7695-1199-3
2. Gardiner, Paul D. (2005). Project Management – A Strategic Planning Approach,
Palgrave Macmillan – Chapter 3 and selected case studies
3. Death March, Edward Yourdon, 2003, Prentice Hall, ISBN: 013143635X
4. Cockburn, A. "Agile Software Development: The Cooperative Game" second
edition; Addison-Wesley 2006; ISBN 0321482751
5. Cockburn, A. "Agile Software Development: Software Through People" Addison
Wesley 2002; ISBN 0201699699
6. Newkirk, J. and Martin, R. "Extreme Programming in Practice (XP)" Addison
Wesley 2001; ISBN 0201709376
7. Beck, K. "Extreme Programming Explained: Embrace Change" 2nd edition;
Addison Wesley 2004; ISBN 0321278658
8. Beedle, M.A., Schwaber, K. "Agile Software Development with SCRUM"
Prentice Hall 2002; ISBN 0130676349
9. Kan, S.H. "Metrics and Models in Software Quality Engineering" Addison
Wesley 1995; ISBN 0201633396
45
10. Booch, G., Rumbaugh, J. and Jacobson, I. "The Unified Modelling Language User
Guide" 2nd edition; Addison-Wesley 2005; ISBN 0321267974
11. Sommerville, I.; "Software Engineering, 6th edition"; Addison Wesley; (2000);
ISBN 020139815X
12. Pressman, R.S.; "Software Engineering: A Practitioner's Approach, 5th edition";
McGraw-Hill; (2000); ISBN 0077096770
Module Title E-Commerce
Module Code INF509
Credits 20
Pre-requisites None
Co-requisites None
Module
Description
In this module students study topics related to creating a
business on the web, with particular focus on e-commerce.
Students will study the IT issues raised by electronic business
and commerce. Techniques and technologies available for
designing and implementing e-business and e-commerce
applications will be surveyed. Students will have first-hand
experience with Web-based tools and services to help design e-
Business solutions.
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 80
Laboratories 0
Exams 2 1Private Study 82
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 50
Assessed Assignments 50
Oral Presentations -
Term Term 2
Module
Coordinator
Dr Cornelius Ncube
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Upon completion of the module the students will be able to:
Knowledge
K1: Demonstrate knowledge of current state-of-the-art technologies in e-commerce,
their strengths and weaknesses
K2: Demonstrate knowledge of basic business models on the web (b2b, b2c, c2b, c2c)
with examples of their implementation
Skills
S1: Recognize and discuss global E-commerce issues
S2: Evaluate the functionality needs for a real-world e-commerce application.
Aspects of Competence
46
Autonomy and responsibility
C1: Use critical thinking, problem-solving, and decision-making skills to evaluate
barriers and opportunities for e-commerce adoption
Role in Context
C2: Critically assess the effect of changing technology on traditional business models
and strategy.
Self-development
C3: Analyze different types of portal technologies and deployment methodologies
commonly used in the industry.
Module Learning Outcomes V.S. Program Learning Outcomes Knowledge Skill Competence INF509 E-Commerce
PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X
Syllabus
Breakdown by week:
Week 1 Introduction, Business Models & Concepts.
Week 2 Marketing (The 4 Ps of Marketing, Email Marketing,
Promotions, Banner Adverts, …)
Week 3 Online Payment & Security (Off-line and Online Payment, The
Online Credit/Debit Card Process, e-Security Issues, …)
Week 4 Law & Ethics (Levels of Service, Privacy, Discrimination,
Advertising, Outsourcing,…)
Week 5 Infrastructure (Network Architectures, Web Site Meta-
Architecture, The Web Server …)
Week 6 Building an e-commerce website (Structuring the Site,
Structuring the Page, Navigation, Error Messages,…)
Week 7 Online retailing and services
Week 8 Social networks, auctions, and portals
Week 9 B2B e-commerce
Assessment 12, 13
12 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
13 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval )
47
6. The final examination will contribute 50% of the final assessment.
7. The assessed assignment will contribute 50% of the final assessment.
The assessment will relate to the learning outcomes and will be 50% by a final exam,
and 50% individual report
Sequence Handed Due Topic and Associated
Weight
Learning Outcomes
Assessed
1 Week 1 Week 10 Report on a research
topic related to e-
Commerce - 50%
K1, K2, S2, C1, C2, C3,
3 Week 11 Week 11 Final Exam-50% K1,K2, S1, C1, C2
8. The assessed assignments will contribute 50% of the final assessment. This part of
the assessment will consist of an individual report where students will discuss the
current state-of-the-art and trends in E-Commerce within the UAE context.
Your report should not exceed 10 pages including all text, references,
appendices and figures. Reports that fail to conform to these requirements
will be rejected without review.
9. The final examination will contribute 50% of the final assessment mark based on
topics covered in the class from Week 1 to Week 9.
Module Texts
2. E-Commerce 2018: Business. Technology. Society, 2018, (14th Edition), Kenneth
Laudon and Carol Guercio Traver, 2018, ISBN-13: 978-0134839516; ISBN-10:
013483951X
Recommended Reading
Students are expected to keep themselves current with e-commerce developments by
reading newspapers, business magazines, and online e-commerce news sources.
Below are some resources.
1. Industrial Organization: Contemporary Theory and Practice by Pepall, Richards
and Norman (South-Western College, 1999) .
2. Information Rules by Shapiro and Varian (Harvard Business School, 1999).
3. Principles of Internet Marketing by Hanson (South-Western College, 2000).
4. The Internet Economy by Soon-Yong Choi and Andrew Whinston (SmartEcon,
2000)
5. E-commerce times: http://www.ecommercetimes.com/perl/section/ecommerce/
Module Title IT Entrepreneurship
Module Code INF510
Credits 20
Pre-requisites None
Co-requisites None
Module Description This module provides the students with scientific methodologies for
identifying opportunities in the IT space. Students will learn how to
create an effective business plan, acquiring funding, establishing a
48
company from scratch and managing in an environment of high
growth, high uncertainty and rapid change.
The module will include case studies of successful and failed IT
entrepreneurial companies and will draw upon the angel investing,
venture capital and entrepreneurial communities from guest speakers.
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 64
Laboratories 0
Exams 0 1Private Study 100
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 0
Assessed Assignments 100
Oral Presentations -
Term 1
Module
Coordinator
Dr Cornelius Ncube
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
Upon completion of the module the students will be able to:
Knowledge
K1: Demonstrate an understanding of the different stages for starting an e-business
and the associated risks
K2: Demonstrate an understanding of advanced topics of entrepreneurship, including
understanding the components of the business plan: (i.e. business idea, feasibility
analysis, target market, PEST, competitive/industry analysis, marketing plan,
organizational structure, operations, pro-forma financial statements, and evaluation
and control)
Skills
S1: Verify the validity of different assumptions in a business plan
S2: Write a comprehensive business plan for an IT venture that justifies potential
profitability and sustainability of the e-business model
Aspects of Competence
Autonomy and Responsibility
C1: assess commercial viability of new information technologies to detect weaknesses
and strengths within a business opportunity
Role in Context
49
C2: effectively combine information technology and entrepreneurship to identify and
communicate relevant legal and ethical issues associated with an IT venture.
Self-development
C3: Carry out scientific research and write comprehensive scientific report that
effectively communicate research findings to experts in the field of IT
entrepreneurship
Module Learning Outcomes V.S. Program Learning Outcomes Knowledge Skill Competence INF510 IT
Entrepreneurship
PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X
Syllabus
1. Introduction & Course Overview
2. IT Opportunity Evaluation, Business Pitch, and formulating the business
model
3. Forming the venture vision and building a Competitive Advantage in IT
industry
4. Industry Analysis of an IT sector and identifying potential risks and returns
5. Developing the business plan
6. Entrepreneurial Marketing and the sales plan
7. Acquiring the necessary resources for an IT venture
8. Legal Issues & Intellectual Property
9. Negotiations, Alliances and Partnerships
Assessment 14, 15
Coursework Assignment, worth 100%, involving starting up an e-business
There are 2 assessed items
Sequence Handed Due Topic and Associated Weight Learning Outcomes Assessed
1 Week 2 Week 6 An Executive Summary that
gives a summary of your
Business Plan – 40%
K1, K2, S1, C1
14 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
15 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval)
50
3 Week 2 Week 12 A fully developed Business Plan
covering all the Components you
will Expect in a Good Business
Plan – 60%
K1, K2, S2, C2, C3
Module Text
Technology Ventures: From Idea to Enterprise (4th Edition), Byers, Dorf, and Nelson.
McGraw Hill. 2014
Recommended Reading
The Four Steps to the Epiphany: Successful Strategies for Products that Win. Steven
Gary Blank. Cafepress, 2005
Module Title Software Systems Design: Practical Object-Oriented Analysis
and Design with UML
Module Code INF511
Credits 20
Pre-requisites None
Co-requisites None
Module Description
The last several years have seen a seismic shift in how organizations
develop software-intensive systems, from the use of structured
analysis methods and conventional programming languages to
object-oriented development methods. The industry standard has
become the Unified Process (UP) and Unified Modeling Language
(UML).
This course is designed to give students knowledge of the principles
of object orientation and extensive practice in the application of these
principles using the Unified Process (UP) and Unified Modelling
Language (UML). It guides the students through the process of UML
system modelling approach and from requirements analysis to
implementation. The course is very practically oriented and follows
the Unified Process so that the students learn how UML is applied in
a real software systems engineering project.
The course will also give students knowledge of Model Driven
Architecture (MDA). MDA is the future of UML and unifies every
step of software systems development and integration from business
modeling, through architectural and application modeling, to
development, deployment, maintenance, and system evolution. The
goal of MDA is to move the development of software to a higher
level of abstraction through the extensive use of UML models. These
models provide the basis for automatic code generation by MDA
enabled CASE tools.
The course is aimed at anyone wanting to learn object-oriented
analysis and design techniques using UML and is suitable for
managers, project leaders, systems engineers and system
architectures.
51
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 64
Laboratories/Tutorials
Exams 2 1Private Study 98
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term 1
Module
Coordinator
Dr Cornelius Ncube
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts,
preparation for examination, library searches and module information reviews, etc.
Learning Outcomes
Upon completion of the module the students should be able to:
Knowledge
To demonstrate a thorough understanding of OO analysis and design with
UML
To follow the process of OO analysis and design from requirements capture
through to implementation using UML and the Unified Process as the
framework
To explore the problems associated with traditional systems development such
as human computer interface design, usability and the evaluation of the
implemented system
Skills
1. Can read and understand UML diagrams
2. Can produce UML models in the laboratory work
3. Can understand problems associated with traditional systems development
such as human computer interface design, usability and the evaluation of the
implemented system
4. Can apply knowledge effectively back at your workplace
Aspects of Competence
Autonomy and Responsibility
1. Acquire knowledge of the principles of object orientation and extensive practice
in the application of these principles using the Unified Process (UP) and Unified
Modeling Language (UML) and the Model-Driven Architecture (MDA)
2. Learn practical skills in Unified Modelling Language (UML), the Unified Process
(UP) and Model Driven Architecture (MDA) via tutorial, group and coursework
exercises;
52
3. Learn how to produce UML models - use case, class, sequence and other object-
oriented models for software-intensive systems;
Role in Context
4. Be able to put the UP, UML and MDA in context using case studies
5. Be able to demonstrate knowledge of the principles of object-orientation and their
role in the object-oriented analysis and design of software systems.
Self-Development
6. Conduct a substantive real-world case study to analyse and design a system using
the UML, including the development object-oriented models for software-
intensive systems;
7. Demonstrate the practical use of UML modelling techniques and supporting tools
on the case study project
Syllabus
Part I: INTRODUCTION TO OBJECT-ORIENTATION, UML and UP
Week 1: Introduction to Object Orientation and the Principles of Object-Orientation
Week 2: Introduction to UML Principles and the Unified Process (UP)
Part II A METHOD FOR SOFTWARE SYSTEMS ENGINEERING
Week 3: Use Case Modelling 1 – Analysis Model
Week 4: Use Case Modelling 2
Week 5: Class Diagram Modelling 1 – Design Model
Week 6: Class Diagram Modelling 2
Week 7: Sequence Diagram – Implementation Model
Week 8: Model-Driven Architecture – The Future of UML
Week 9: From Analysis to Design – A Worked Example
Assessment 16, 17
Coursework Assignment, worth 60% and Exam worth 40%
There are 2 assessed items
Assignment Handed Due Topic
16 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
17 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval)
53
1 Week 1 12 The assignment will normally be a group
project of 3-6 members but would accept
individual work in special circumstances.
Students will specify a design approach for
a scenario and produce complete UML
models from analysis-to-specification –to
design. They will submit worked examples
of their analysis and designs
Students will be encouraged to draw on
theories to analyse their employing
organisations activities. The assignment is
worth 60% of the total module mark
Exam Week
10-12
A written exam worth 40% of the total
module marks
Core Module Text
Arlow J. & Neustadt I. UML 2 and the Unified Process: Practical Object-Oriented
Analysis And Design, 2/E Paperback, (Pearson India, 2016), ISBN-10: 9332547920;
ISBN-13: 978-9332547926
Essential Reading
Arlow J. & Neustadt I. UML 2 and the Unified Process. Practical Object-Oriented
Analysis and Design. 2nd Edition, Addison Wesley Professional, 2005.
Grady Booch, Robert A. Maksimchuk, Michael W. Engle, Bobbi J. Young, Jim
Conallen, Kelli A. Houston: Object-Oriented Analysis and Design with Applications:
Addison-Wesley Professional; 3rd Edition (April 30, 2007); ISBN-10: 020189551X;
ISBN-13: 978-0201895513
Arlow J. & Neustadt I. Enterprise Patterns and MDA: Building Better Software with
Archetype Patterns and UML 1st Edition, ISBN-13: 978-0321112309
ISBN-10: 032111230X
Recommended Reading
Martin Fowler. UML Distilled: A Brief Guide to the Standard Object Modeling
Language (3rd Edition), ISBN-13: 978-0321193681 ISBN-10: 0321193687
Stephen J. Mellor. MDA Distilled: Principles of Model-driven Architecture.
Addison-Wesley Professional, 2004
Ian F. Alexander and Neil Maiden. Scenarios, Stories, Use Cases: Through the
Systems Development Life-Cycle. ISBN: 978-0-470-86194-3. August 2004
54
Module Title Systems Requirements Engineering (SRE)
Module Code INF512
Credits 20
Pre-requisites Software Systems Design Practical Object-Oriented Analysis and
Design with UML
Co-requisites None
Module Description Establishing firm and precise requirements is an essential component
of successful software systems development.
The general aims of this course is to make students understand the
ever-increasing importance of requirements in the wider systems
engineering process, and to improve systems engineering processes
by making them more requirements-oriented. The course describes
the role of requirements in the construction and continued
maintenance of large, complex and evolving software-intensive
systems. It introduces the important concepts and activities in
systems requirements engineering, explains how they can knit
together to form a through-life requirements engineering process, and
demonstrates how such an end-to-end process can be defined and
used in practice. The course provides a broad overview of the
notations, techniques, methods and tools that can be used to support
the various requirements engineering activities, and complements
this with the opportunity to gain experience in a selection of these.
The course seeks to illustrate the wider applicability of requirements
engineering to everyday projects, the breath of skills required and the
many contributing disciplines.
This course will also demonstrate why traditional approaches to
requirements engineering are not adequate for building ultra-large-
scale, complex systems-of-systems and Internet of Things-enabled
Cyber-Physical Systems such as Smart Cities and Industry 4.0
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 64
Laboratories/Tutorials -
Exams 2 1Private Study 98
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term 2
Module
Coordinator
Dr Cornelius Ncube
Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts,
preparation for examination, library searches and module information reviews, etc.
55
Learning Outcomes
Upon completion of the module the students should:
Knowledge
Be aware of:
The notion of requirements engineering
Importance of effective requirements engineering
Range and types of problems which arise
Why requirements at different levels of detail are needed
Why requirements evolve during the lifetime of a system
Leading edge research and practice
Skills
Have gained:
Practical skills in leading-edge requirements engineering methods and
techniques
Practical knowledge of requirements engineering tools
Aspects of Competence
Autonomy and Responsibility
8. Learn how to discover, model, analyse and communicate requirements for
software intensive systems requirements.
9. Develop modelling skills and the ability to communicate requirements with clarity
and precision to business stakeholders and software developers.
10. Develop an appreciation of the engineering issues which form the background to
establishing, defining and managing requirements for large, complex, evolving
(software-intensive) systems;
11. Develop an appreciation that requirements engineering is part of a wider software
systems design process
Role in Context
12. Be able to explain how various supporting concepts, notations, techniques,
methods and tools can be used together to define and support a requirements
engineering process;
13. Be able to discuss and evaluate current and future developments in the area of
systems requirements engineering
14. Have a breadth of knowledge about the range of requirements engineering
methods, tools, and techniques
Self-Development
15. Conduct a substantive real-world case study to apply a requirements engineering
process for a small project and demonstrate how it can be used to acquire set of
measurable requirements for the project;
16. Demonstrate the practical use of selected techniques and tools on case study
project and practical guidance on elicitation techniques
56
Syllabus
Week 1: An Introduction to Requirements Engineering
Week 2: How Not To Do Requirements Engineering – Lessons Learned from Failed
Large-Scale Systems – the London Ambulance Services, UK NHS IT System,
FireControl
Week 3: Some Key Definitions of Requirements and Their Attributes
Week 4: Use Case Driven Requirements Analysis – use cases, actors and use case
diagram
Week 5: Use Cases, Requirements and Environment
Week 6: Requirements Acquisition and Stakeholder Identification Techniques
Week 7: Security Requirements Engineering
Week 8: Capability Engineering
Week 9: Future Trends in Requirements Engineering Challenges: Systems-of-Systems
Engineering, Systems of Systems, Smart Cities, Cyber-Physical Systems, Ultra-Large
Scale Software Systems, The Fourth Industrial Revolution (Industry 4.0), Internet of
Things
Assessment 18, 19
Coursework Assignment, worth 60% and Exam worth 40%
There are 2 assessed items
Assignment Handed Due Topic
1 Week 1 Week 12 The assignment will normally be a small
group project (3-4 students) but can accept
individual assignment in special
circumstances. Students will produce a
requirements specification with complete,
testable and measurable requirements for a
comprehensive case study scenario. The
scenario would be based on a real-world
system. They will submit worked
examples of their analysis and designs.
The assignment is worth 60% of the total
module mark
Exam Week
10-12
A written exam worth 40% of the total
module marks
18 Academic integrity is the key to academic success. Cheating is considered as a serious offence at the British
University in Dubai. Please read the university polices and procedure carefully in the university student handbook
so that you are aware of all university procedures and abide by them to avoid penalties. Please note that all written
assignment will be checked using specified plagiarism detection software
19 The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within learning
activities and assignments. If these include research participation by third parties or other ethical other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval)
57
Core Module Text
The main references are the following:
Robertson S. & Robertson J., 2013, ‘Mastering the Requirements Process: Getting
Requirements Right (3rd Edition) Addison-Wesley Addison-Wesley Professional;
ISBN-13: 978-0321815743; ISBN-10: 0321815742
Recommended Reading
Karl Wiegers & Joy Beatty Software Requirements (3rd Edition) (Developer Best
Practices) 3rd Edition: ISBN-13: 978-0735679665; ISBN-10: 0735679665
Alex van Lamsweerde, Requirements Engineering: From System Goals to UML
Models to Software Specifications, Wiley, 2009.
Ian Sommerville, 2015, Software Engineering 10th Edition, Pearson;
ISBN-10: 0133943038; ISBN-13: 978-0133943030
Arlow J. & Neustadt I. UML and the Unified Process. Practical Object-Oriented
Analysis and Design. 2nd Edition, Addison Wesley Professional, 2005.
Ian F. Alexander and Neil Maiden. Scenarios, Stories, Use Cases: Through the
Systems Development Life-Cycle. ISBN: 978-0-470-86194-3. August 2004
Module Title Machine Learning
Module Code INF513
Credits 20
Pre-requisites INF504 Data Mining and Exploration or approval of the instructor
Co-requisites None
Module Description Machine learning is about making computers learn, rather than
simply programming them to do tasks. The course will discuss
supervised learning (which is concerned with learning to predict an
output, from given inputs), reinforcement learning (which is
concerned about learning from interacting with an environment),
unsupervised learning, where we wish to discover the structure in a
set of patterns; there is no output "teacher signal". We will compare
and contrast different learning algorithms, and unlike Data Mining
Exploration module where the focus was on the applying algorithms
to large real-world data sets, in this course we will get to the technical
and mathematical details of the studied algorithms.
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 62
Laboratories 0
Exams 2 1Private Study 100
Total 200
58
Assessment
Weightings (%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term 3, 20XX-20XX
Module
Coordinator
Prof. Sherief Abdallah
Office Hours By Appointment
1Private Study covers time spent reading over lecture notes, texts, recommended texts,
preparation for examination, library searches and module information reviews, etc.
Learning Outcomes
Below are outcomes of the module. The students will be able to:
Knowledge
K1: Demonstrate knowledge of machine learning techniques, including the
strength and weaknesses of different techniques
Skills
S1: Choose and justify an appropriate machine learning technique, given a
problem.
Aspects of Competence
Autonomy and Responsibility
Role in Context
Self-Development
C1: Manage own time effectively
C2: critically evaluate intellectual and academic work
Module Learning Outcomes V.S. Program Learning Outcomes INF507
Learning from
Data Knowledge Skill Competence
Module Learning
Outcomes (MLOs)
PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 x
Skill S1 X X
Competence C1 X
Competence C2 x
59
Syllabus
The module will cover the following topics:
1. Introduction & theoretical background (Maximizing likelihood vs.
minimizing error, Generalization and overfitting, Model Selection)
2. Supervised Learning 1 (Logistic Regression & Neural Networks & Decision
Trees)
3. Supervised Learning 2 ( Naive Bayes and Bayesian Classifiers & Nearest
Neighbour Methods)
4. Supervised Learning 3 (Linear parameter model & Support Vector Machines)
5. Ensemble Learning (Boosting & bagging)
6. Feature Selection (Criteria, Search methods)
7. Unsupervised Learning & Dimensionality Reduction (Principal component
analysis & Factor analysis)
8. Single agent Reinforcement Learning (Markov Decision Process, Bellman’s
equation and its variants)
9. Multi-agent Reinforcement Learning (Gradient-ascent learners, Deterministic
learners)
Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Intelligent Information Systems Technologies, Simulation and
Modelling, Theoretical Computing
Assessment 20, 21
There are 3 items of assessment.
Handed Due Deliverable Weight Assessed LOs
20
Academic integrity is the key to academic success. Cheating is considered as a serious offence at the
British University in Dubai. Please read the university policies and procedures carefully in the university student
handbook so that you are aware of all university procedures and abide by them to avoid penalties. Please note that
all written assignment will be checked using specified plagiarism detection software
21
The module tutor is “lead academic monitor” for ethical aspects of ‘routine research’ undertaken within
learning activities and assignments. If these include research participation by third parties or other ethical
dimensions, the tutor is responsible for initial guidance and the student is directed to use relevant approval forms
and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval).
60
1 Week 1 Week 4 Critical survey of a
topic related to machine
learning
20 K1,C1,C2
2 Week 1 Week 9 Report describing
research project in
machine learning
40 K1,S1,C1,C2
3 Final Exam 40 K1,S1,C1
Refer to the description of each assignment for more details.
Module Texts
The lecture notes are designed to be self-contained, with pointers to web-resources
and related material. Recommended readings include
1. Crandall, Jacob W., Mayada Oudah, Fatimah Ishowo-Oloko, Sherief
Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael
A. Goodrich, and Iyad Rahwan. (2018) "Cooperating with machines." Nature
communications 9, no. 1: 233.
2. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H.
(2017). Feature selection: A data perspective. ACM Computing Surveys
(CSUR), 50(6), 94.
3. Abdallah, S., & Kaisers, M. (2016). Addressing environment non-stationarity
by repeating Q-learning updates. The Journal of Machine Learning
Research, 17(1), 1582-1612.
4. Gu, B., Sheng, V. S., Tay, K. Y., Romano, W., & Li, S. (2015). Incremental
support vector learning for ordinal regression. IEEE Transactions on Neural
networks and learning systems, 26(7), 1403-1416.
5. Li, J., Hu, X., Jian, L., & Liu, H. (2016, December). Toward time-evolving
feature selection on dynamic networks. In Data Mining (ICDM), 2016 IEEE
16th International Conference on(pp. 1003-1008). IEEE.
6. MacKay, D. (2003). Information theory, inference, and learning algorithms.
Cambridge: Cambridge University Press.
Available at:
http://www.inference.phy.cam.ac.uk/itprnn/book.pdf
7. Chapters in the following books are interesting to read.
a. Russell, S. and Norvig, P.
(2010). Artificial intelligence: a modern approach. 3rd ed. Upper Saddle,
NJ: Prentice Hall
b. Mitchell. T. (1997).
Machine learning. Singapore: McGraw-Hill.
c. Bishop. C. M. (1995).
Neural networks for pattern recognition. Oxford: Oxford University Press,
Oxford.
Module Title Management Information Systems
Module Code INF514
Credits 20
Pre-requisites None
Co-requisites None
61
Module Description Managers have increasing responsibility for determining their
information system needs and for designing and implementing
information systems that support these needs. Management
information systems integrate, for purposes of information
requirements, the accounting, financial, and operations
management functions of an organization. This course will
examine the various levels and types of software and
information systems required by an organization to integrate
these functions.
Instruction and
Assessment
Study Format Hours
Lectures 36
Coursework assignment hours 62
Laboratories 0
Exams 2 1Private Study 100
Total 200
Assessment Weightings
(%)
Assessment %
Written Examination 40
Assessed Assignments 60
Oral Presentations -
Term 2
Module Coordinator Prof Khaled Shaalan
Office Hours by appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
On successful completion of this module the student will be able to:
Knowledge
K1: Demonstrate the steps involved in the development of information systems
and identify threats to information systems security and how to mitigate these
threats.
K2: Demonstrate an understanding of major organization’s information systems
and assess its influence on the organizational performance.
Skill
S1: Apply relevant theories and techniques needed at the forefront of professional
practice in information systems design
S2: Evaluate and utilize computer–based information systems from a management
perspective.
Aspects on Competence
Autonomy and Responsibility
C1: Utilize information systems to assist in smooth running of business operations.
Role in Context
C2: Critically assess, evaluate and communicate risk considerations in MIS
implementation and operations.
62
Self-Development
C3: Conduct a case study in MIS and write academic review report describing
lessons learned
Module Learning Outcomes V.S. Program Learning Outcomes INF511
Management
Information
Systems
Knowledge Skill Competence
Module Learning
Outcomes
(MLOs) PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self
Development
PLO9 PLO10 PLO11 PLO12 PLO13
Knowledge K1 X
Knowledge K2 X
Skill S1 X
Skill S2 X
Competence C1 X
Competence C2 X
Competence C3 X X
Syllabus
Breakdown by week:
Week 1 Lecture: Information Systems in Global Business Today
Week 2 Lecture: Global E-Business and Collaboration
Week 3 Lecture: Information Systems, Organizations, and Strategy
Week 4 Lecture: Business Intelligence and Decision Making Systems
Week 5 Lecture: IT Infrastructure and Emerging Technologies
Week 6 Lecture: Foundations of Business Intelligence: Databases and
Information Management
Week 7 Lecture: Telecommunications, the Internet, and Wireless Technology
Week 8 Lecture: Securing Information Systems
Week 9 Lecture: Achieving Operational Excellence and Customer Intimacy:
Enterprise Applications
Assessment
10. The final examination will contribute 40% of the final assessment.
11. The assessed assignment will contribute 60% of the final assessment.
Sequence Handed Due Learning
Outcomes
Assessed
Topic and Associated Weight
1 Week 1 Week 9 C1, C2, C3 A case study on MIS; analysing some
issues due to lack or inappropriate MIS in
one or more organisations and suggesting
their solutions with suitable plans- size
7,000 words limit - report- 60%
2 K1, K2, S1, S2 Final Exam-40%
Assignment
Students are required to submit a report about practical problem, and a critical
analysis (7,000 words limit), as per the due dates.
Module Texts
Jane Laudon, Management Information Systems: Managing the Digital Firm, 15th
Edition, Prentice Hall, 2017, ISBN-10: 129221175X & ISBN-13: 978-1292211756
63
Recommended Readings
Keri E. Pearlson and Carol S. Saunders (2016) Managing and Using Information
Systems, Binder Ready Version: A Strategic Approach; Wiley. SBN-13: 978-
1119244288; ISBN-10: 1119244285
Ken J. Sousa and Effy Oz (2014). Management Information Systems; Course
Technology. ISBN-10: 1285186133
Stair R et al., (2014) Fundamental of Information Systems, 7th Ed. Cengage
Learning, Inc., ISBN: 978-1-285-07298-2
David M. Kroenke (2014) MIS Essentials, 3rd Ed Pearson Education, ISBN: 978-
013-3382839
Paige Baltzan et al., (2014) Business Driven Information Systems, 6th Ed.
McGraw-Hill / Irwin, ISBN: 978-0073376905
Module Title MSc Research project
Module Code INF520
Credits 20
Pre-requisites Start in the 2nd to last term of study
Co-requisites None
Module Description In this module the student will undertake a short research project. This
project could be an extension of one or more projects submitted in
previous modules. In this module the student will reflect on all his/her
research activities in the previous modules, will undertake critical
review of previous outcomes in order to prepare a proposal for new
research project. The student will focus on applying the knowledge
learnt in several modules to analyse, revise, improve and assess a
relevant topic. This could include topics on Artificial Intelligence,
Intelligent Systems, Knowledge Management, Learning from Data,
Software Engineering, IT & management, or any other relevant IT
topic as long as it is approved by the module tutor. The student will
produce a research report, including an executive summary, reflective
analysis of previous works, and details of the project outcome. In
addition to the report, the student will have to give a presentation
explaining and defending the steps undertaken during the project. The
jury for the presentation will include one or more jurors from the
relevant industry who will take part in the assessment of the
presentation as well. This module will run over two consecutive terms
in order to give the student enough time to properly research,
document, propose and assess his/her selected topic of the project.
Instruction and
Assessment
Study Format Hours
Lectures/Tutorials 4
Coursework assignment hours 0
Laboratories 0
Presentations and exam 1
Private Study1 195
Total 200
Assessment
Weightings (%)
Assessment %
Written Examination 0
Assessed Assignments 75
Oral Presentations 25
Term 2 & 3
64
Module Coordinator Prof Sherief Abdallah
Office Hours TBA 1Private Study covers time spent reading over notes, texts, recommended texts, preparation for assessment, library searches and
module information reviews, etc.
Learning Outcomes
Upon completion of the module a student will be able to:
Knowledge
K1: demonstrate understanding of a particular area of knowledge related to a
selected aspect of the subject area of the programme of study;
Skills
S1:apply established techniques of analysis and evaluation to solve complex
problems
S2: Apply current knowledge appropriately and with originality to developing
computational systems.
S3: Analyse highly complex issues with incomplete data and combine
advanced problem-solving skills to construct innovative solutions and
proposals relevant to Information Technology.
Aspects on Competence
Autonomy and Responsibility
C1: Demonstrate the ability to do research and further develop knowledge and
methods in the field of Information Technology
Role in Context
C2: Apply well-developed interpersonal skills including the ability to
communicate effectively and to interact with groups and individuals at all
levels.
Self-Development
C3: Self-evaluate, develop, and implement further learning consistently,
sensitively, and independently
C4: Carry out original research at the forefront of knowledge on a relevant
Information Technology topic.
Module Learning Outcomes V.S. Program Learning Outcomes Knowledge Skill Competence INF520 MSc
Research
project PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1
x X
Skill S1 x
Skill S2 x
Skill S3 x
Competence
C1
X
Competence
C2
X
Competence C3
X
Competence x
65
C4
Syllabus
Breakdown by week (this module spans two terms, i.e. 26 weeks):
Week 1 Induction to the project. Outline of scope, requirements and
deadlines of the project. Presentation and discussion of
suggested topics (some from the industry) and samples of
previous projects.
Week 3 Submission of project proposal
Week 11 Progress report
Week 23 Oral presentation of the project
Week 25 Submission of final report
Assessment
Assessment Handed Due Topic and Associated Weight
1 (5%) Week 1 Week 3 Submission of a 1-page project outline
including scope, aims, objectives,
methodology and expected outcomes.
2 (10%) Week 1 Week
12
Submission of a progress report outlining the
scope and significance of the topic, tasks
completed and timeline for completion of the
project (approximately 1500 words*).
3 (30%) Week 1 Week
23
Oral presentation of the topic in front of a jury
includes professional(s) from the topic’s field.
The student is to clearly present the topic and
its significance, present the proposed
changes/modifications/additions and the
impact of such actions of on the overall
performance of the topic of study. The 30-
minutes presentation will be followed by 20-
minutes of Q&A in which the student has to
answer questions of the jury.
4 (55%) Week 1 Week
25
Submission of a final report covering the
selected topic. This will be a professional type
report with an executive summary highlighting
the research topic, motivation, methodology
and main outcomes(approximately 1000
words*) followed by detailed sections
including: Introduction to the topic &
motivation, Methodology selection and
description, Presentation, discussion and
insight to the results, Conclusions as to how
the results can better help address the aims and
objectives of the research, list of references
used (not less than 25) and Appendices (if
66
needed) (approximately 9000 words*) *word count excluding list of references and appendices
Module Text(s)
There is no single main textbook for this module. You are expected to read from a
variety of relevant research journals and books (including those shown below). The
use of electronic resources is essential.
Recommended Reading
1. Sharp, J. and Howard, K. (2002). The management of a student research
project. Aldershot.
2. Turabian, K. (2002). A manual for writers of research papers, theses and
dissertations. University of Chicago Press.
3. Booth, V. (1993). Communicating in science: writing and speaking.
Cambridge University Press.
4. Gomm, R. , Hammersley, M. and Foster, P. (2002). Case Study Methods.
SAGE Publications.
Electronic resources
1. Students are encouraged to use the e-library resources to supplement the course
materials.
2. Google advanced search: www.google.com/advanced_search?hl=en
3. Google Scholar website: scholar.google.com
4. Experimental Science Projects:
http://www.isd77.k12.mn.us/resources/cf/SciProjIntro.html
5. How to Read a Scientific Paper:
http://helios.hampshire.edu/~apmNS/design/RESOURCES/HOW_READ.html#fac
ulty
6. Introduction to the Scientific Method:
http://teacher.nsrl.rochester.edu/phy_labs/AppendixE/AppendixE.html#Heading2
Module Title Dissertation
Module Code RES506
Credits 60
Pre-requisites Successful completion of all taught modules
Co-requisites None
Module
Description
Having successfully completed the six modules in the taught stage of the
programme, students who wish to proceed to the masters degree take the
dissertation stage. This final project is intended to give students an opportunity
to focus on an aspect of the taught subject matter and investigate it in more
detail. This will help them consolidate their capacity for independent study, and
to learn some of the techniques needed to conduct research and develop
knowledge in the subject area of the programme of study.
This is a research project. The only piece of work to be submitted for
examination is a dissertation, and this is a written report on the research. There
are thus two aspects to consider: the research and the writing. Both are governed
by implicit rules common to the discipline of formal research; part of the
students’ training is to become familiar with these rules.
67
Instruction and
Assessment
Study Format Hours
Lectures 0
Coursework assignment hours 0
Laboratories 0
Presentations and exam 16
Private Study 584
Total 600
Assessment
Weightings (%)
Assessment %
Written Examination 0
Assessed Dissertation 100
Oral Presentations 0
|Term NA
Supervisor(s) Selected Academic staff members from all BUiD faculties
Office Hours Consultation with Supervisor/s as required or as specified in learning contract 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library
searches and module information reviews, etc.
Learning Outcomes
Below are outcomes of the module. The students will be able to:
Knowledge
K1: demonstrate understanding of a particular area of knowledge related to a
selected aspect of the subject area of the programme of study;
Skills
S1:apply established techniques of analysis and evaluation to solve complex
research problems
S2: Apply current knowledge appropriately and with originality to developing
computational systems.
S3: Analyse highly complex issues with incomplete data and combine
advanced problem-solving skills to construct innovative solutions and
proposals relevant to Information Technology.
Aspects on Competence
Autonomy and Responsibility
C1: Demonstrate the ability to do research and further develop knowledge and
methods in the field of Information Technology
Role in Context
C2: Apply well-developed interpersonal skills including the ability to
communicate effectively and to interact with groups and individuals at all
levels.
Self-Development
68
C3: Self-evaluate, develop, and implement further learning consistently,
sensitively, and independently
C4: Carry out original research at the forefront of knowledge on a relevant
Information Technology topic.
Module Learning Outcomes V.S. Program Learning Outcomes Knowledge Skill Competence RES506
Dissertation
PLO1 PLO2 PLO3 PLO4 PLO5 PLO6 PLO7 PLO8
Autonomy &
Responsibility
Role in
context
Self Development
PLO9 PLO10 PLO11 PLO12 PLO13 PLO14
Knowledge K1 x x
Skill S1 x
Skill S2 x
Skill S3 x
Competence C1 X
Competence C2 X
Competence C3 X
Competence C4 x
Syllabus
There is no fixed syllabus content. The content is the outcome of the research
process, and is dependent on the topic chosen for investigation. Guidance on how to
conduct the research will be provided by personal supervision, and will consequently
be tailored by the research advisers to each student’s personal needs.
The following schedule provides some guidelines for supervision schedule (the
student and supervisor are to finalize the schedule in the contract). The module
typically spans over two terms (26 weeks)
Breakdown by week:
Week 1 Establish starting position, plan the research, define the research topic
Week 3 decide on the method of research, consider the outcome of the work
Week 11 Progress report, including surveyed literature, data collection,
preliminary analysis
Week 20 Submit first draft of the dissertation for supervisor feedback
Week 25 Submission of dissertation to be marked
Assessment
100% by written dissertation of not more than 25,000 words in length, that comprises
the major assessed component of this module (excluding appendices, references, etc.).
Module Text(s)
There is no specific text for this module. The student is expected to read some items
from the suggested reading list below as well as seek other sources that are relevant to
the dissertation.
Recommended Reading
1. J. Sharp and K. Howard. The management of a student research project. 3rd
edition. Aldershot: Gower, 2002. 0-566-08492
69
2. J. Giltrow. Academic writing; writing and reading across the disciplines. 3rd
edition. Peterborough, Ontario: Broadview, 2002. 1-55111-3953
3. K. Rudestam and R. Newton. Surviving your dissertation; a comprehensive
guide to content and process. 2nd edition. Sage, Newbury Park, California.
2000. 0761919627
4. K. Turabian. A manual for writers of research papers, theses and dissertations.
2nd edition. U of Chicago P., 2002.
5. S. Bailey. Academic Writing; A Practical Guide for Students.
RoutledgeFalmer, Oxon, 2003.
6. L. Cooley and J. Lewkowicz. Dissertation Writing in Practice; Turning Ides
into Text. Hong Kong University Press, Hong Kong, 2003.
7. R. Murray. How to Write a Thesis. Open University Press, Berkshire, 2002.
8. R. Murray. Writing for Academic Journals. Open University Press, Berkshire,
2009.
9. W. Booth, G. Colomb and J. Williams. The Craft of Research. The University
of Chicago Press, Chicago, 2003.
Module Title People, Culture and Organisation
Module Code MGT503
Credits 20
Pre-requisites None
Co-requisites None
Module Description
To gain knowledge and understanding on a wide range of people and culture topics relevant to a project manager. To gain awareness and understanding of a range of perspectives and underpinning techniques for analysing problems. To experience the application of theoretical ideas to work situations through personal reflection. To gain understanding of the theory and practice of creative approaches to problem solving. To create a future learning agenda for personal development. To gain experience and understanding of qualitative concepts and measures with respect to people, culture, and organisations.
Instruction and Assessment
Study Format Hours
Lectures 36
Revision/Tutorials 0
Coursework Assignment 54 1 Private Study 108
Examination 2
Total 200
Assessment Weightings (%)
Assessment %
Written Examination 50
70
Assessed Assignment 50
Oral Presentations 0
Term
Module Coordinator Dr Mohammed Dulaimi
Office Hours 4-6 pm on the class date
1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library searches and module information reviews, etc.
Intended Learning Outcomes
The module provides opportunities for learners to achieve the following outcomes:
Knowledge
1. Systematic understanding of knowledge, and a critical awareness of current problems and/or new insights in managing conflict, cultural diversity, communication, ethics, change, and innovation.
2. Comprehensive understanding of theoretical concepts of teams, leadership, motivation, organisational culture, cross-cultural management, organisational problems, conflict and negotiation.
3. Critical understanding of the changes taking place in developing economies. 4. Systematic understanding of the impact, in a Middle East context, of theoretical
concepts related to leadership and effective management of project teams.
Intellectual skills
5. Assess and compare fundamental principles underpinning effective teams, motivation and leadership, culture, ethics at work, perceptions of different business and national cultures to change in the UAE and region.
6. Reflect on own experience of communication in own work context, cross-cultural factors in a commercial environment and on opportunities for innovation in their own teams.
7. Critically analyse complex issues associated with the changing nature of work caused by changes in technology and practices.
Subject practical skills
8. Apply principles of teams, leadership and motivation, cultural models, of the analysis of conflict in the project and project environment, good communication and barriers to the process, managing knowledge sharing, ethics, managing change and innovation.
Transferable skills
9. Develop a synthesis of theoretical constructs in a practical application with respect to common project management problems.
10. Apply a range of communication skills in an organisational and people-focused context.
Syllabus
Breakdown by week:
71
1. Introduction: psychological contract and motivation. Introduction to theoretical
principles of people and organisations and the role of the project manager in relation to projects and project organisation.
2. Project leadership. Theoretical concepts underpinning motivation and leadership approaches. Consideration of situational variables and their influence on project managers’ behaviour and effectiveness. Leading and Motivating the Project Team Exercise, case studies to examine what courses of action Project Managers can take to deal with motivation problems.
3. Team building and conflict management. High performance teams. The design of teams. The concept of the integrated project team (IPT) in terms of basic organisation structures, and to consider the concept of virtual teams and issues of authority. Conflict and negotiation in projects. Causes of conflict. Power, responsibility and authority. Analysis and solutions to conflict. Sources of influence.
4. Organisation structures. Examine the project life-cycle as a problem for organisation design; Consider the wide range of possibilities when designing organisation structures; Develop the post-contingency concept; Introduce an organisation effectiveness assessment model. Linkage with organisational culture in reference to projects. Effective Teams Exercise, The application of team working theory and practice in project management (Integrated project team, virtual teams, and high performance teams).
5. Communications and knowledge sharing. Communication is dealt with as a symptom of organisational health and a cause of dysfunction. Theoretical models of communication are presented. Issues and approaches to communications in project management are addressed. Cover the importance of knowledge sharing in the context of multicultural teams. Addresses the issue of ethics in the context of project management.
6. Aspects Organisational Culture in projects and project organisations. Theoretical models of culture with respect to industrial and client organisations. Linkage with leadership and a stress on the significance of culture on performance at individual through corporate levels. Introduction to recent research findings on the importance of cross-cultural aspects of project management. Cross-cultural factors and issues in projects and project organisations.
7. Management of change. Identification and management of organisational change in the context of projects.
8. Management of innovation. Understanding the need for innovation and the role of project managers in promoting innovation. Exercise: Developing a climate of innovation in projects.
72
9. Empowerment: Managing Cultural Change. Managing Cultural change to create empowered project teams. Understand how project managers can be empowered to deliver successful projects.
Assessment
1. A two-hour examination will count 50% of the module final assessment. 2. Assessed assignment will count 50% of the final assessment. The assignment is
practice based requiring students to address a people related issue/problem and propose, based on a literature review exercise, a set of recommendations.
No Assessment Handed Due
1 Assignment – 50% Week 1 Week 11
2 Exam – 50% - During Exam Period (Week 12 &13)
Assignment
Students are required to submit a 2000 word report as per the stated deadline. The report should provide a detailed review of current knowledge on a particular people related issue linked to issue at the student workplace with particular emphasis on the applicability of established theoretical frameworks to local problems and issues. The report should use the developed framework to critically analyse the adopted approaches in the selected case and present recommendations for improvement. The report requires students to collect data through investigating documents and conducting interviews and/or surveys to support their analysis and recommendations.
Exam
Final exam which worth 50% of final mark will be based on topics covered in the lectures, seminars/workshops, and recommended readings and notes.
Module Text
1. McKenna, E. (2006) Business Psychology and Organisational Behaviour: A student
Handbook, 3rd Edition, Psychology Press.
Indicative Key Reading
1. Dwivedulaa, R, and Christophe N. Bredilletc, C. (2010) Profiling work motivation of project workers, International Journal of Project Management, 28(2), pp. 158-165.
2. Baiden, B and Price, A (2010) The effect of integration on project delivery team
effectiveness, International Journal of Project Management.
73
3. Müller, R. and Turner, R. (2010) Leadership competency profiles of successful project managers, International Journal of Project Management, Volume 28, Issue 5, July 2010, Pages 437-448
4. Kotter and Schlesinger (1979) “Choosing strategies for change”, HBR, March-April, pages 106-114.
5. Mohammed Dulaimi (2007) “Case Studies on Knowledge Sharing Across Cultural
boundaries”, Journal of Engineering, Construction, and Architectural Management, 14(6), pp. 550-567, Emerald Publishing Ltd, UK.
6. Lane and Lubatkin (1998) Relative Absorptive Capacity and Interorganizational Learning,
Strategic Management Journal, 19, pp. 461-477. 7. Mullins, L. J (2005) Management and Organisational Behaviour, 7th ed, FT Prentice Hall.
ISBN 0-273-68876-6. 8. Hargadon, A and Sutton, R (2000) Building an Innovation Factory, HBR, June, pp.
May/June, pp. 157-166
Recommended Reading
1. Bessant, J. and Tidd, J. (2007) Innovation and Entrepreneurship, Wiley, England.
2. Clegg, S., Kornberger, M. and Pitsis, T. (2005) Managing and Organisations: An
introduction to Theory and Practice, Sage, ISBN 0-7619-4389-7.
3. Child, J (2005) Organisation: Contemporary Principles and Practice, Blackwell Publishing. ISBN 1-4051-1658-7.
4. Robbins, S. (2005) Organisational Behaviour, Prentice Hill.
5. Pettinger, R (2000) Mastering Organisational Behaviour, Palgrave
6. Hammuda, I, and Dulaimi, M (1999) “A framework for customer-oriented organisation in the UK construction industry”, CIB W65, Customer Satisfaction: A focus for research and practice in construction, Volume 1, pages 388-398, South Africa, September.
7. Hammuda, I and Dulaimi, M (1996) "Empowering the Organisation: A comparative study of different approaches to Empowerment", CIB Beijing International conference, China, 21-24 October.
8. Dulaimi, M and Langford, D (1999) “Job Behaviour of Construction Project Managers: Determinants and Effectiveness”, The American Society of Civil Engineers (ASCE); Journal of Construction Engineering and Management, July/August.
9. Emmitt, S. and Gorse, C. (2003) Communication for Engineers, Blackwell Publishing.
10. Dulaimi, M and Ang, A F (2009) “Elements Of Learning Organisations in Singapore’s Construction Industry”, Emirates Journal of Engineering Research, 14 (1), pp 83-92.
74
11. Hofseted, G, and Hofstede, G (2004) Cultures and Organizations: Software of the Mind,
McGraw Hill.
12. Handy, C (1993) Understanding Organisations, Oxford Press University.
13. Hammuda, I and Dulaimi, M (1997) The Theory and application of Empowerment: A comparative study of the different approaches to Empowerment in Construction, Service, and Manufacturing Industries, International Journal of Project Management, 5(5), pp. 289-296
14. Dulaimi, M and Kumaraswamy, M (2000) Procuring for Innovation: The Integrating Role of
Innovation in Construction Procurement, Proceedings of the Association of Researchers in Construction Management, Glasgow, UK.
75
Module Title Planning, Execution and Control
Module Code MGT504
Credits 20
Pre-requisites None
Co-requisites None
Module Description
This module is designed to provide knowledge and a higher level of understanding of planning, execution and control processes in the management of projects. This covers concepts, models, and methodologies of planning and control of project cost and time.
Instruction and Assessment
Study Format Hours
Lectures 36
Revision/Tutorials 8
Coursework Assignment 54 1Private Study 100
Examination 2
Total 200
Assessment Weightings (%)
Assessment %
Written Examination 50
Assessed Assignments (Interim Assignment Report and Full Assignment Report)
50
Oral Presentations 0
Term Summer 2013
Module Coordinator
Dr Arun Bajracharya
Office Hours 2-5pm on the class date
1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library searches and module information reviews, etc.
Learning Outcomes
The module provides opportunities for learners to achieve the following outcomes:
Academic knowledge
1. Understanding of theories of project planning and control, work scope documentation and work break down structure.
2. Understanding of requirements management, issue management, planning techniques and methods to model resources.
3. Specific understanding of project scheduling tools. 4. Knowledge of the basic theoretical concepts of project budgeting, cost estimating,
monitoring and control. 5. Knowledge and understanding of change and configuration management.
Intellectual skills
6. Determine the level of planning applicable in a range of circumstances and be able to select the most appropriate method for a given situation.
76
7. Understand the importance and application of work content and scope management, requirements and issue management, resource management, cost management, and project budgeting.
8. Explain how to manage change and how project progress may be monitored and controlled.
Subject practical skills
9. Application of planning and monitoring techniques, including work content and scope management, bar charts, network analysis methods, resource management, and cost and earned value management.
10. Application of techniques of requirements and issue management; 11. Application of project progress monitoring and control and change control.
Transferable skills
12. Apply planning techniques to a range of situations 13. Develop control systems based on effective planning tools and models.
Syllabus
Breakdown by week: 1. Course introduction. The need for planning and control. Work scope documentation
and work breakdown structure.
2. Requirements management: Capturing, analysing, and testing the project requirements, Preparing baseline requirements.
3. Project scheduling: Bar chart, network analysis – PERT and CPM.
4. Resource management: Resource classification and modelling.
5. Project budgeting and cost control: Project cost planning, Project budgeting, Monitoring
and control of project costs.
6. Cost estimating and forecasting: Techniques of project cost estimating, learning curve and S-curve forecasting.
7. Earned value management: Development of baseline models for performance
measurements, Variance and trend analysis.
8. Issue management: Identification and review of issues, Tools and techniques for addressing issues.
9. Change control: Theory and practice of change control, Configuration management.
77
Assessment
One assessed practical assignment will count 50% of the final assessment. There will be an interim submission and then a final submission. The final examination counts 50% of the final assessment.
No Assessment Handed Due
1 Interim Assignment Report on Planning, Execution and Control in Project Management (10%).
Week 1 Week 6
2 Full Assignment Report on Planning, Execution and Control in Project Management (40%).
Week 1 Week 12
3 Exam – 50% - During Exam Period (Week 12 &13)
Module Texts
1. Gardiner, P. D. (2005), Project Management: A Strategic Planning Approach, Palgrave
Macmillan.
2. Gray, C. F. And Larson E. W. (2008), Project Management: The Managerial Process, McGraw Hill, New York.
Indicative Key Reading
1. Buttrick, R. (2005), Project Workout: A Toolkit for Reaping the Rewards of All Your
Business Projects, FT Prentice Hall, London.
2. British Standards Institution (2003), BS ISO 10007:2003, Quality Management Systems, Guidelines for Configuration Management, BSI, London.
3. Cappels, T. (2000), Financially Focussed Project Management, J. Ross Publishing, Fort
Lauderdale, FL. 4. Fleming, Q. W. And Koppelman, J. M. (2000), Earned Value Project Management, PMI,
Newton Square, PA. 5. Forsberg, K., Mooz, H. And Cotterman, H. (2000), Visualising Project Management: A
Model for Business and Technical Success, Wiley, New York. 6. Lester, A, (2007), Project Management Planning and Control, Elsevier, Oxford. 7. Lewis, J. P. (2005), Project Planning, Scheduling and Control, McGraw Hill, New York. 8. Maylor H. (2005), Project Management, FT Prentice Hall, London. 9. Rad, P. F. (2001), Project Estimating and Cost Management, Management Concepts,
Vienna.
78
10. Robertson, S. and Robertson, J. (1999), Mastering the Requirements Process, Addison Wesley, Boston, MA.
11. Schwindt, C. (2005), Resource Allocation in Project Management, Springer, Berlin.
12. Taylor, J. C. (2005), Project Cost Estimating Tools, Techniques and Perspectives, St Lucie
Press, Boco Raton, FL.
13. Venkataraman, R. R. and Pinto, J. K. (2008), Cost and Value Management in Projects, John Wiley and Sons.
Recommended Reading
1. Block, E. B. (1971), Accomplishment/Cost: Better Project Control, Harvard Business
Review, May/Jun, 49 (3), 110-125.
2. Cooper, K. and Lee, G. (2009), Managing the Dynamics of Projects and Changes at Fluor, Kenneth Cooper and Fluor Corporation.
3. Elton, J. and Roe, J. (1998), Bringing Discipline to Project Management, Harvard Business
Review, Mar/Ap, 76 (2),153-160. 4. Fleming, Q. W. and Koppelman, J. M. (2003), What's Your Project's Real Price Tag?,
Harvard Business Review, Sep, 81(9), 20-23. 5. Gardiner, P. D. and Stewart, K. (2000), Revisiting the Golden Triangle of Cost, Time and
Quality: The Role of NPV in Project Control, International Journal of Project Management, 18, 251-256.
6. Leach, L. P. (2003), Critical Chain Project Management, Artech House, Norwood, MA. 7. Lee, Z, Ford, D.N. and Joglekar, N. (2007), Resource Allocation Policy Design for Reduced
Project Duration: A Systems Modeling Approach, Systems Research and Behavioral Science, 24 (6), 551-566.
8. Matta, N. E. and Ashkenas, R. N. (2003), Why Good Projects Fail Anyway, Harvard
Business Review, Sep, 81 (9), 109-115. 9. Pena-Mora, F. and Park, M. (2001), Dynamic Planning for Fast-Tracking Building
Construction Projects, Journal of Construction Engineering and Management, 127 (6), 445-454.
10. Royer, I. (2003), Why Bad Projects Are So Hard to Kill, Harvard Business Review, Feb, 81
(2), 48-57. 11. Shapiro, A. and Lorenz, C. (2000), Large-Scale Projects as Complex Systems: Managing
Scope Creep, The Systems Thinker, 11 (1), February, Pegasus Communications, 1-5. 12. Staw, B. M. and Ross, J. (1987), Knowing When to Pull the Plug, Harvard Business
Review, Mar/Apr, 65 (2), 68-75.
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13. Sterman, J. D. (1992), System Dynamics Modelling for Project Management, A Working Paper, Sloan School of Management, MIT, Cambridge, MA, http://web.mit.edu/jsterman/www/SDG/project.pdf.
14. Taylor, T. and Ford, D.N. (2008), Managing Tipping Point Dynamics in Complex Construction Projects, ASCE Journal of Construction Engineering and Management. 134 (6), 421-431.
15. Wateridge, J. (1999), The Role of Configuration Management in the Development and Management of IS/IT Projects, International Journal of Project Management, 17 (4), 237-241.
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