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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

mailto:[email protected]

http://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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Core: Complete all of the following modules INF501 Informatics Research Methods 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


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:





MGT504 Planning, Execution and Control 20

MGT503 People, Culture and Organisation 20

Concentration: Business Intelligence (Student will be required to take all modules)



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






Electives: (Student will be required to take two out of the six modules)


20


INF513 Machine Learning (pre-requisite INF504, Data Mining & Exploration)

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.


http://scholar.google.com/citations?user=keLKdlgAAAAJ

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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.


http://homepages.inf.ed.ac.uk/sabdalla/

https://scholar.google.com/citations?user=R7ngExMAAAAJ&hl=en

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)


https://scholar.google.com/citations?user=XKaB180AAAAJ&hl=en

http://www.buid.ac.ae/

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.

21

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

http://wise.cgu.edu/hypomod/

http://math.berkeley.edu/~hutching/teach/proofs.pdf

26

Assessment Lectures 28


Laboratories 8


Total 200

Assessment

Weightings (%)

Assessment %

Written Examination 40


Oral Presentations 0

Term 2, 2016-2017

Module Coordinator Dr Khaled Shaalan


[email protected]

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


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


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


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


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


Laboratories 8


Total 200

Assessment

Weightings (%)

Assessment %




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


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



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



Introduction

to

Computational Linguistics


Module Learning Outcomes


Autonomy & Responsibility

Role in context

Self Development


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


1 Week 1 Week 4 Assignment 1– 20% K1, K2, C1, C3


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 (%)




Total 100

Term 2, 2013-2014

Module

Coordinator

Dr Sherief Abdallah

[email protected]

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


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.


Self-Development



Module Learning Outcomes V.S. Program Learning Outcomes

INF504 Data Mining and Exploration


Module Learning


Autonomy & Responsibility

Role in context

Self Development


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.


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


University in Dubai. Please read the university policies and procedures carefully in the university student




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:

http://binf.gmu.edu/mmasso/ROC101.pdf

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


Laboratories 0

Exams 2

1Private Study 100

Total 200

37

Assessment

Weightings (%)

Assessment %




Term Term3

Module

Coordinator

Dr Sherief Abdalllah

[email protected]

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


Learning Outcomes


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



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



Knowledge

Engineering Knowledge Skill Competence

Module Learning


Autonomy &

Responsibility

Role in

context

Self Development


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


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


Laboratories 4


Total 200

Assessment

Weightings (%)

Assessment %




Term

Module

Coordinator

Dr Khaled Shaalan

[email protected]



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



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


Knowledge

Management Knowledge Skill Competence

Module

Learning


Autonomy &

Responsibility

Role in

context

Self Development


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









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


Laboratories


Total 200

Assessment

Weightings (%)

Assessment %



Oral Presentation 20

Term Term 3

Module

Coordinator

Dr Cornelius Ncube

[email protected]



Learning Outcomes


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


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


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








and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval )

44


and 70% (50% group project report+ 20% group presentation) by a project report and

presentation


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

http://www.amazon.co.uk/gp/product/0321482751






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


Laboratories 0


Total 200

Assessment

Weightings (%)

Assessment %




Term Term 2

Module

Coordinator

Dr Cornelius Ncube

[email protected]




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.




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


Autonomy &

Responsibility

Role in

context

Self Development


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








and procedures. (see policy 9.3.2 Frame Work for Research Ethics Approval )

47


7. The assessed assignment will contribute 50% of the final assessment.


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


Laboratories 0


Total 200

Assessment

Weightings (%)

Assessment %




Term 1

Module

Coordinator

Dr Cornelius Ncube

[email protected]



Learning Outcomes


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



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


Autonomy &

Responsibility

Role in

context

Self Development


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


1 Week 2 Week 6 An Executive Summary that

gives a summary of your

Business Plan – 40%

K1, K2, S1, C1









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


Laboratories/Tutorials


Total 200

Assessment

Weightings (%)

Assessment %




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



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%


Assignment Handed Due Topic









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


Laboratories/Tutorials -


Total 200

Assessment

Weightings (%)

Assessment %




Term 2

Module

Coordinator

Dr Cornelius Ncube

Office Hours By Appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts,


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



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%


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









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


Laboratories 0


Total 200

58

Assessment

Weightings (%)

Assessment %




Term 3, 20XX-20XX

Module

Coordinator

Prof. Sherief Abdallah

[email protected]


1Private Study covers time spent reading over lecture notes, texts, recommended texts,


Learning Outcomes


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.



Role in Context

Self-Development




Learning from

Data Knowledge Skill Competence

Module Learning

Outcomes (MLOs)


Autonomy &

Responsibility

Role in

context

Self Development


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.


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



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


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

http://www.inference.phy.cam.ac.uk/itprnn/book.pdf

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


Laboratories 0


Total 200

Assessment Weightings

(%)

Assessment %




Term 2

Module Coordinator Prof Khaled Shaalan

[email protected]

Office Hours by appointment 1Private Study covers time spent reading over lecture notes, texts, recommended texts, preparation for examination, library


Learning Outcomes


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.



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


Management

Information

Systems


Module Learning

Outcomes


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


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


Laboratories 0

Presentations and exam 1

Private Study1 195

Total 200

Assessment

Weightings (%)

Assessment %




Term 2 & 3

64

Module Coordinator Prof Sherief Abdallah

[email protected]

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.



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


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.

http://www.google.com/advanced_search?hl=en

http://www.isd77.k12.mn.us/resources/cf/SciProjIntro.html

http://helios.hampshire.edu/~apmNS/design/RESOURCES/HOW_READ.html#faculty

http://helios.hampshire.edu/~apmNS/design/RESOURCES/HOW_READ.html#faculty

http://teacher.nsrl.rochester.edu/phy_labs/AppendixE/AppendixE.html#Heading2

67

Instruction and

Assessment

Study Format Hours

Lectures 0


Laboratories 0

Presentations and exam 16

Private Study 584

Total 600

Assessment

Weightings (%)

Assessment %


Assessed Dissertation 100


|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


Learning Outcomes


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.



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


Autonomy &

Responsibility

Role in

context

Self Development


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 %


70

Assessed Assignment 50


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.

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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.

http://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=27983&_origin=article&_zone=art_page&_linkType=scopusAuthorDocuments&_targetURL=http%3A%2F%2Fwww.scopus.com%2Fscopus%2Finward%2Fauthor.url%3FpartnerID%3D10%26rel%3D3.0.0%26sortField%3Dcited%26sortOrder%3Dasc%26author%3DDwivedula,%2520Ravikiran%26authorID%3D35195681800%26md5%3Dc7206dc8d2a98ba7e5442f1d42482483&_acct=C000024058&_version=1&_userid=6991156&md5=fc1c449ff9770667a5904f3bdab79ba5

http://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=27983&_origin=article&_zone=art_page&_linkType=scopusAuthorDocuments&_targetURL=http%3A%2F%2Fwww.scopus.com%2Fscopus%2Finward%2Fauthor.url%3FpartnerID%3D10%26rel%3D3.0.0%26sortField%3Dcited%26sortOrder%3Dasc%26author%3DDwivedula,%2520Ravikiran%26authorID%3D35195681800%26md5%3Dc7206dc8d2a98ba7e5442f1d42482483&_acct=C000024058&_version=1&_userid=6991156&md5=fc1c449ff9770667a5904f3bdab79ba5

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9V-4XWMGSB-1&_user=6991156&_coverDate=02%2F28%2F2010&_alid=1570610390&_rdoc=2&_fmt=high&_orig=search&_origin=search&_zone=rslt_list_item&_cdi=5908&_sort=r&_st=13&_docanchor=&view=c&_ct=448&_acct=C000024058&_version=1&_urlVersion=0&_userid=6991156&md5=f8a09b0bcdb3aee78ee346a6cdd1df11&searchtype=a#aff2

http://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=27983&_origin=article&_zone=art_page&_linkType=scopusAuthorDocuments&_targetURL=http%3A%2F%2Fwww.scopus.com%2Fscopus%2Finward%2Fauthor.url%3FpartnerID%3D10%26rel%3D3.0.0%26sortField%3Dcited%26sortOrder%3Dasc%26author%3DBredillet,%2520Christophe%2520N.%26authorID%3D6506622264%26md5%3Da68a4ebed3d4fe8c292ee17a5a70eeb0&_acct=C000024058&_version=1&_userid=6991156&md5=fa8030fc2eb0a2ffb3ab9b757a98d2e2

http://www.sciencedirect.com/science?_ob=RedirectURL&_method=outwardLink&_partnerName=27983&_origin=article&_zone=art_page&_linkType=scopusAuthorDocuments&_targetURL=http%3A%2F%2Fwww.scopus.com%2Fscopus%2Finward%2Fauthor.url%3FpartnerID%3D10%26rel%3D3.0.0%26sortField%3Dcited%26sortOrder%3Dasc%26author%3DBredillet,%2520Christophe%2520N.%26authorID%3D6506622264%26md5%3Da68a4ebed3d4fe8c292ee17a5a70eeb0&_acct=C000024058&_version=1&_userid=6991156&md5=fa8030fc2eb0a2ffb3ab9b757a98d2e2

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9V-4XWMGSB-1&_user=6991156&_coverDate=02/28/2010&_alid=1570610390&_rdoc=2&_fmt=high&_orig=search&_origin=search&_zone=rslt_list_item&_cdi=5908&_sort=r&_st=13&_docanchor=&view=c&_ct=448&_acct=C000024058&_version=1&_urlVersion=0&_userid=6991156&md5=f8a09b0bcdb3aee78ee346a6cdd1df11&searchtype#hi

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9V-500Y5M4-1&_user=6991156&_coverDate=05/05/2010&_alid=1570619628&_rdoc=3&_fmt=high&_orig=search&_origin=search&_zone=rslt_list_item&_cdi=5908&_sort=r&_st=13&_docanchor=&view=c&_ct=1103&_acct=C000024058&_version=1&_urlVersion=0&_userid=6991156&md5=875dd7515ebd839766560e415b9d8091&searchtype#hi

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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.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9V-4XDFDPB-1&_user=6991156&_coverDate=07%2F31%2F2010&_alid=1570613012&_rdoc=2&_fmt=high&_orig=search&_origin=search&_zone=rslt_list_item&_cdi=5908&_sort=r&_st=13&_docanchor=&view=c&_ct=466&_acct=C000024058&_version=1&_urlVersion=0&_userid=6991156&md5=f370bcf0d62a25ecdf067f8b32674966&searchtype=a

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9V-4XDFDPB-1&_user=6991156&_coverDate=07%2F31%2F2010&_alid=1570613012&_rdoc=2&_fmt=high&_orig=search&_origin=search&_zone=rslt_list_item&_cdi=5908&_sort=r&_st=13&_docanchor=&view=c&_ct=466&_acct=C000024058&_version=1&_urlVersion=0&_userid=6991156&md5=f370bcf0d62a25ecdf067f8b32674966&searchtype=a

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 %


Assessed Assignments (Interim Assignment Report and Full Assignment Report)

50


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.

79

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.

http://web.mit.edu/jsterman/www/SDG/project.pdf

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