msc advanced computer science and it management … › pgt › 2015 › welcome... ·...
TRANSCRIPT
MSc Advanced Computer Science and IT Management
Manchester Business School courses
SEMESTER 1
Title BMAN 70391 Managing Projects
Credit Rating 15
Level 7
Semester 1
Course Coordinator(s) Dr Eunice Maytorena-sanchez
Methods of Delivery Lectures and case studies
Lecture Hours 30 (3 hours per week over 10 weeks including case analysis sessions)
Seminar Hours -
Private Study Hours 120 hours
Total Study Hours 150 hours
Pre-requisites -
Co-requisites -
Dependent Courses -
Assessment Methods
and Relative Weightings
Individual assessment, quiz/test (15%)
Individual assessment, 3000 word essay (85%)
Aims
To introduce students to the fundamental concepts, processes, tools and
techniques employed in project management practice and critically apply
and assess these in real-world situations.
Learning Outcomes: students should be able to
Academic Knowledge and Understanding
Understand the fundamental principles and processes available for
supporting the management of projects; Understand the issues associated with project management practices;
Understand what skills are required to effectively manage projects. Intellectual Skills
Identify and apply appropriate concepts and frameworks in the analysis of a project situation;
Critically analyse and assess a project situation and make recommendations for improvement;
Undertake critical research of project management issues in a rigorous manner.
Subject Practical Skills Define, design, plan, execute, terminate and develop a project and its
management in a wide range of work contexts. Transferable Skills
Development of communication, organisational, managerial, team
building, leadership and coping skills.
Syllabus Overview
The Management of Projects in Context • Why Projects, why are they important –value creation perspective
• Projects characteristics – uniqueness, complex, uncertain etc. • Project lifecycles
• Project and Project Management success/failure • Project Management Maturity
Conception and Definition • Organisational strategy and Projects • Selecting the project (s)
• Defining the project mission and scope • Managing Stakeholders
Assembling the Project Team • The Project Manager (skills, attributes, traits) - leadership
• Building and managing the team/coalition (selection, characteristics, effectiveness)
• Conflict and negotiation
Design and Planning • Planning the Project –project plan, action plan, WBS, LRC
• Estimating project times –networks, duration, CP, Gantt Charts • Estimating project costs – creating a budget, learning curves, problem
with estimates
Implementation • Managing the Schedule
• Managing the Budget • Managing Resources
• Managing Quality: Conformance and Performance • Managing Risk and Uncertainty • Managing Information Flows (PMIS)
• Planning-Monitoring-Controlling cycle – reporting, control systems, managing scope creep and baseline change
• Dealing with problems
Delivery and Termination • Evaluation • Termination and Closure
Reading List
Maylor, H. 2010. Project Management. 4th ed. Financial Times/Prentice Hall.
Additional reading material for each session: case studies, journal articles,
book chapters.
Title BMAN60111 IS Strategy and Enterprise
Systems
Credit Rating 15
Level 7
Semester 1
Course Coordinator(s) Chris Holland
Methods of Delivery Lectures, case studies, electronic case studies,
video, guest lecturers and industry presenters
Lecture Hours 30
Seminar Hours 0
Private Study Hours 120
Total Study Hours 150
Assessment Method Individual Essay (50%) plus Exam (50%)
Aims
It is now widely recognised that information is the lifeblood of companies. IT and Information Systems (IS) were long considered as a separate part in
organisations that merely provided some infrastructure and maybe some supporting mechanisms for certain business activities. Recently, it has been
recognised that IT and IS form an integral part of organisations. The introduction of Chief Information Officers (CIOs) is evidence of this trend.
In addition, organisations start to recognise that IT and IS should be closely linked to business strategy and objectives in order to achieve a competitive
advantage. The focus to date has been on automating transactional-based systems in all the business areas of the company, such as production and
logistics.
The challenge for managers over the next decade is to build intelligence
into their organisations that combine the best elements of integrated transaction-based systems such as Enterprise Resource Planning (ERP), and
banking systems, with knowledge-based systems that support individual and group decision making, and enable the communication, storage and
leverage of ideas and concepts across global enterprises. The management of ‘big data’ is also a new strategic challenge for companies.
The aim of the course unit is to develop an understanding of key information systems strategy concepts and contemporary developments in
IS strategy for competitive advantage, Internet marketing and global systems. Emphasis will be placed on the combination of theory and practice
through the strategic analysis of case studies and examples of big data sets in a range of markets. In lectures and discussion, theory frameworks will
be illustrated with international examples and data from banking, telecommunications, grocery, retailing, sports marketing and
manufacturing.
Learning Outcomes
Academic Knowledge and Intellectual Skills
Comprehend key strategic concepts including competitive positioning, the role of IT in a resource based view of the firm, the
debate on IT and competitive advantage, the distinction between
planned and emergent strategies. Understand the theory of electronic markets and how strategy
concepts can be applied to develop an Internet marketing strategy Have knowledge of business computing architectures such as ERP
and supply chain systems, including implementation and cost structure models
Subject practical skills
Apply the concepts of IT strategy to evaluate a company’s use of
IT in the context of its overall business strategy. This includes the use of ERP as a vital component of a firm’s internal IT
infrastructure Analyse the relationships between business and IT strategies and
apply these concepts to a range of companies including Amazon,
CISCO, TESCO, schwab.com and Vodafone Be able to synthesise external market data with internal
performance data and the managerial implications of the resource profile for large-scale IT project implementation
Have an appreciation of the use of ‘big data’ in a commercial context, e.g. to be able to relate sales data to online search data in
the US automotive industry Have an appreciation and understanding of state of the art
technology use in leading companies such as CISCO, Alibaba, Vodafone, TESCO and Capital One.
Transferable skills
Develop a coherent analysis of multiple sources of data, derived
from case study and evaluation of online market data and online
panel data Contribute to the development, implementation and evaluation of
an Internet marketing strategy in a competitive context
Syllabus
1. IT as a supporting mechanism for organisations and as part of
business strategy, including the distinction between IT infrastructure, transaction systems and business intelligence
2. Legacy systems and Enterprise Resource Planning (ERP) systems, including vendor positioning.
3. What is strategy, business strategy and IS strategy? This will include strategy frameworks that cover competitive positioning, resource
based view of the firm and the role of IT, strategic alignment and IT
for competitive advantage 4. The use of information systems to support Customer Relationship
Management (CRM) as part of an information-driven strategy 5. International strategies: the balance between global and local country
strategies 6. The theory of electronic markets, application of concepts to B2B
markets and online sales models in consumer marketing 7. Development of Internet marketing strategy in consumer markets
and the combined use of internal performance data with external market information on competitors
8. Web 2.0 developments in business and consumer markets
9. A number of case studies will be discussed in class, see below for details.
Reading List and Information Resources
IS Strategy Reading List Organised by Subject Themes / Lectures. C.P.
HOLLAND 2015. Technology and Business Trends
Articles and Book Chapters Chapter 1. Introduction to digital business and e-commerce, Chaffey,
Dave “Digital Business and E-commerce management, Strategy, Implementation and Practice (2015).
• Information technology and disruption, Economist article on Marc Andreessen
• McKinsey. Clouds, big data, and smart assets: Ten tech-enabled business trends to watch: http://www.itglobal-
services.de/files/100810_McK_Clouds_big_data_and%20smart%20assets.pdf
• Internet of Things A provocative article in The Economist
• SAS Internet of Things Blog Post
• Smartphone on Wheels. Another article from The Economist that shows the potential of connecting together devices to form a new
ecosystem, in this case centred on the car • The customer journey,
http://www.mckinsey.com/insights/high_tech_telecoms_internet/brand_success_in_an_era_of_digital_darwinism?cid=other-eml-alt-mkq-
mck-oth-1502
VIDEOS • Deloitte US. Tech Trends 2014: Inspiring Disruption
• Deloitte Tech Trends 2013 Summary • IBM Business Tech Trends 2014
• Interview with Erik Brynjolfsson: Productivity Paradox
• The new digital entrepreneurs, Marc Andreessen
Amazon Case Study
• VIDEO: Jeff Bezos on Amazon
Could Amazon buy Alibaba? This was a question posed in class. See http://www.businessweek.com/articles/2014-09-25/alibaba-ipo-pours-
shares-into-shrinking-pool-of-stock, which shows that Alibaba is worth more than the combined value of Amazon and eBay.
Chen, Daniel Q., et al. "Information systems strategy:
reconceptualization, measurement, and implications." MIS quarterly 34.2
(2010): 233-259. This is a rather theoretical article but it does explain the concept of strategy alignment rather well, see Figure 1. In Amazon
and other companies, the relationship between business and technology is recursive. Business places new demands on the technology, and
technology creates new opportunities for the business.
Eisenmann, T., G. Parker and M. Van Alstyne (2006), “Strategies for Two-Sided Markets”, Harvard Business Review, October. This explains
why market platforms such as Amazon and Alibaba become so powerful in a winner takes all scenario.
Business Computing and ERP
READ THE CISCO CASE STUDY FOR THIS LECTURE Silicon Valley on the Rhine. Business Week 1997. Article in Business
Week on the rapid growth of SAP R/3. This is still a very good
introduction to Enterprise Resource Planning (ERP) systems and explains why they are so important to businesses large and small. The technology
is now delivered through the cloud but the concepts are exactly the same.
Marston, Sean, et al. "Cloud computing—The business
perspective." Decision Support Systems 51.1 (2011): 176-189. This article gives a very good overview of cloud computing and brings the
discussion up to date. For contemporary business articles on this topic, see Business Week.
Holland, Christopher P., and Ben Light. "A critical success factors model
for ERP implementation." IEEE software 16.3 (1999): 30-36. A management guide for implementation.
Hong, Kyung-Kwon, and Young-Gul Kim. "The critical success factors for ERP implementation: an organizational fit perspective." Information &
Management40.1 (2002): 25-40. An organisational perspective on implementation.
SAP 2014 CONFERENCE. See
http://events.sap.com/teched/en/session/13492 for the 2014 SAP event. It’s more like a concert than a business conference, but there’s some
very good and contemporary content about enterprise computing and technology.
Introduction to Big Data Business Applications using Online Panel Data
McKinsey Global Institute, “Big Data: The next frontier for innovation, competition, and productivity”,
http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
Analyse the data in the US Auto comScore and Market Sales Data Project. Do this individually and then prepare a team presentation in
class. Note, the project is in the folder titled “Case studies and US Auto Big Data Project”
Title BMAN 73291 Games Businesses Play
Credit Rating 15
Level 7
Semester 1
Course coordinator Dr Luciana Nicollier
Method of Delivery
Lecture Hours 20 (10 sessions x 2 hours each)
Seminar Hours 10 (10 sessions x 1 hour each)
Private Study Hours 120
Total Study Hours 150
Pre- Requisites Some basic knowledge of mathematics (e.g.,
maximization of simple functions) and statistics (how to compute expectations) is recommended.
Co-Requisites -
Dependant Courses -
Assessment Methods
and Relative Weightings
Group Case Studies (2 x 20% each) The students
will be asked to use the concepts learned in the lectures to analyse a real business situation. See
details in the Syllabus.
Final Exam (60%) See details regarding the
structure of the exam in the Syllabus.
Aims
The main goal of the module is to enhance the student’s ability of thinking
strategically in complex, interactive situations. Complementing this goal are
the following ones:
- To introduce students to the basic concepts of game theory and, more importantly, to help them developing the skills needed to go from theory
principles to business recommendations. - To help students developing their ability to think ahead and to take into
account other people’s possible responses to their actions - To give them the tools required for understanding game theory
applications in different settings, including scientific articles and consultancy reports.
Learning Outcomes
The module is organized in three parts, with each of them having a specific
learning outcome: 1. A first set of lectures aims at understanding the difference between games
(as strategic interaction) and decisions, as well as the basic concepts of game theory.
2. A second group of lectures presents those concepts in a more formal and analytical way and relates them to the main equilibrium concepts. At this
point the students should be able to predict and analyse the outcome(s) of
several types of games. The first group-homework, together with
examples and cases discussed in the lectures and seminars will help them
to develop the skills necessary to apply the theory principles to real business situations.
3. Finally, a few lectures will work on specific topics oriented to learn how to “shape the game”. Students will learn how to use game theory in
bargaining, taking advantage of the information they have (or do not have), and in making strategic moves that give the firm advantage in the
market.
Syllabus
- Lectures 1-2: Introduction and Basic concepts - Lectures 3-6: Simultaneous and Sequential Move Games; Equilibrium
Concepts; Mixed Strategies - Lectures 7-8: Uncertainty and Information; Screening and Signalling
- Lectures 9: Credibility, Commitment, Threats, Reputation - Lecture 10: Bargaining
Reading List
Required Textbook:
Dixit, A., et al., “Games of Strategy”, WW Norton, 2008 (Third Edition)
Optional Textbooks:
Gibbons, R., “Game Theory for applied Economists”, Princeton University Press, 1992
Ghemawat, P. “Games Business Play. Cases and Models”, The MIT Press, 1997.
Further Readings:
See attached Syllabus for a detailed list of readings by topic.
Course Title BMAN 60101 Mathematical Programming and
Optimisation
Member(s) of staff
responsible
Dong-Ling Xu – lectures and workshops
Ludmil Mikhailov – lectures and workshops
Credit rating 15
Semester 1
Level 7
Methods of delivery Lectures/Workshops
Lecture hours 33
Seminar hours 3
Private study 114
Total study hours 150
Dependent Courses BMAN60092
Risk, Performance and Decision Analysis
Pre-requisites N/A
Co-requisites N/A
Assessment methods and relative
weightings
50% Exam (closed book, 2.5 hours) 50% Coursework (35% for individual assignment
and 15% for related group presentation)
Aims
This course covers mathematical modelling, including: linear, non-linear and
dynamic programming. Emphasis will be placed on the use of Excel and Solver. The aim is to familiarise students with the application of
mathematical programming methods.
Learning outcomes
At the end of the unit students should be familiar with several mathematical
programming approaches and decision problems to which they can be applied. They should be should be able to model appropriate decision
problems and solve them using, where appropriate, Excel.
Syllabus
The following topics will be covered:
Introduction to Modelling
Introduction to Linear Programming formulation, graphs Slack, surplus variables, duality, Excel solver
LP Applications Integer and binary linear programming
Non-linear optimisation Dynamic Programming
Reading list
The CORE text is: HILLIER, F and LIEBERMAN, G (2004 or any later edition), Introduction to
Operations Research with CD-Rom, McGra Hill
Other readings:
(some of these may be available via the Blackboard site for this unit)
Burke, E. K. and Kendall, G. (2005/6) Search Methodologies Introductory
Tutorials in Optimization and Decision Support Techniques, Springer
Garner, S.G and Gass, S, I. (1999) Stigler’s diet problem revisited. OR Chronicle 1- 13
Hastings, N.A.J (1988) Dynamic Programming with Management
Applications, The Butterworth Group, England
Johnson, D and McGeogh, L.A. (1995) The Traveling Salesman Problem: A
Case Study in Local Optimization in: Local Search in: Combinatorial Optimization, Aarts E and Lenstra J.K (eds.), John Wiley and Sons, London,
1997, 215-310.
Orman A.P and Williams, H.P (2004) A Survey of Different Integer
Programming Formulations of the Travelling Salesman Problem. LSE Working
Paper: LSEOR 04.67
Suman, B and Kumar, P (2006) A survey of simulated annealing as a tool for
single and multiobjective optimization. Journal of the Operational Research Society No. 57, 1143–1160.
Waters, D. (2007) Quantitative Methods for Business. 4th ed. Prentice Hall.
Williams, H. P. (1999) Model building in mathematical programming,
Chichester, John Wiley & Sons
NOTE: additional references/readings will be given in lectures
Title BMAN 71641 Social Media and Web
Analytics
Credit Rating 15
Level 7
Semester 1
Course Coordinator(s) Dr. Weigang Wang
Methods of Delivery Lectures and lab sessions
Lecture Hours 30
Seminar Hours 3 (one 3-hour session for lab testing in a computer lab)
Private Study Hours 117
Total Study Hours 150
Assessment Methods and Relative
Weightings
Examination (60%): Multiple short questions (60%) plus an essay question (40%). Calculators
not permitted Coursework (40%): Group report on measuring
user/ customer experience of an online group decision support service using both
experimentation and web analytical methods
Aims
The aim of this course unit is to showcase the opportunities that exist today to leverage the power of the Web and social media; to develop students’
expertise in assessing web marketing initiatives, evaluating web optimisation efforts, and measuring user experience; and to equip students with skills to
collect, analyse and derive actionable insights from web clickstream, social media chatter, usability testing and experiments. A key feature of this
course is the use of hands-on software tools for analysing web and social
media interactions.
Learning Outcomes
Academic knowledge
Be able to understand social media, web and social media analytics, and
their potential impact
Be able to understand usability, user experience, and customer experience
Be able to understand the relationship between the experiences and ROI
Intellectual skills Be able to understand usability metrics, web and social media metrics Be able to identify key performance indicators for a given goal, identify
data relating to the metrics and key performance indicators Be able to analyse and interpret the data generated from usability testing,
questionnaire surveys, or collected from Web and social media tracking tools
Subject practical skills
Be able to design and conduct usability studies
Be able to use various data sources and collect data relating to the metrics and key performance indicators
Be able to use ready-made web analytics tools (Google Analytics)
Be able to understand a statistical programming language (R) and use its graphical development environment (Deduce) for data exploration and
analysis
Transferable skills
Be able to demonstrate group working skills and academic writing skills
The experiment design and web analytics skills may also apply to other projects
Syllabus
1. Introduction
Web and social media (Web sites, web apps, mobile apps and social media)
Usability, user experience, customer experience, customer sentiments, web marketing, conversion rates, ROI, brand reputation, competitive
advantages
Web analytics and a Web analytics 2.0 framework (clickstream, multiple outcomes analysis, experimentation and testing, voice of customer,
competitive intelligence, Insights)
2. Background
Data (Structured data, unstructured data, metadata, Big Data and Linked Data)
Lab testing and experiment design (selecting participants, within-subjects
or between subjects study, counterbalancing, independent and dependent variable; A/B testing, multivariate testing, controlled experiments)
Data analysis basics (types of data, metrics and data, descriptive statistics, comparing means, correlations, nonparametric tests, presenting
data graphically)
3. Measuring user experience
Usability metrics (performance metrics, issues-based metrics, self-
reported metrics)
Planning and performing a usability study (study goals, user goals, metrics and evaluation methods, participants, data collection, data
analysis)
Typical types of usability studies and their corresponding metrics
(comparing alternative designs, comparing with competition, completing a task or transaction, evaluating the impact of subtle changes)
4. Web metrics and web analytics
PULSE metrics (Page views, Uptime, Latency, Seven-day active users) on business and technical issues;
HEART metrics (Happiness, Engagement, Adoption, Retention, and Task
success) on user behaviour issues;
On-site web analytics, off-site web analytics, the goal-signal-metric
process
5. Social media analytics
Social media analytics (what and why)
Social media KPIs (reach and engagement)
Performing social media analytics (business goal, KPIs, data gathering, analysis, measure and feedback)
6. Data analysis language and tools
Ready-made tools for Web and social media analytics (Key Google Analytics metrics, dashboard, social reports )
Statistical programming language (R), its graphical development
environment (Deducer) for data exploration and analysis, and its social media analysis packages (RGoogleTrends, twitteR)
7. Cases and examples
User experience measurement cases
Web analytics cases
8. Group work and hands on practice
Usability study planning and testing; and data analysis using software tools (Google Analytics, Google Sites, R and Deducer)
Reading List
(B) Avinash Kaushik, Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity, John Wiley & Sons; Pap/Cdr edition (27 Oct
2009)
(B) Tom Tullis, Bill Albert, Measuring the User Experience: Collecting,
Analyzing, and Presenting Usability Metrics, Morgan Kaufmann; 1 edition (28 April 2008)
(B) Jim Sterne, Social Media Metrics: How to Measure and Optimize Your Marketing Investment, John Wiley & Sons (16 April 2010)
(B) Brian Clifton, Advanced Web Metrics with Google Analytics, John Wiley &
Sons; 3rd Edition edition (30 Mar 2012)
Title MCEL40021 Entrepreneurial
Commercialisation of Knowledge
Credit Rating 15
Level 7
Semester 1
Course Coordinator(s) Dr Matthew McCaffrey
See online description at http://courseunits.humanities.manchester.ac.uk/Undergraduate/MCEL400
21/Display
SEMESTER 2
Title BMAN 60092
Risk, Performance and Decision Analysis
Credit Rating 15
Level 7
Semester 2
Course Coordinator(s) Prof Jian-Bo Yang (JBY) – lectures Prof Dong-Ling Xu (DLX) – lectures
Dr Yu-Wang Chen (YWC) – seminars
Methods of Delivery Lectures/Workshops
Lecture Hours 34
Seminar Hours 9
Private Study Hours 107
Total Study Hours 150
Pre-requisites BMAN60101
Mathematical Programming and Optimisation
Co-requisites N/A
Dependant Courses N/A
Assessment Methods
and Relative Weightings
50% Exam (close book, 2.5 hours)
50% Coursework (35% individual report and 15% group presentation)
Aims
This course unit covers risk, performance and decision modelling and analysis, including risk modelling and assessment, both single and multiple
criteria decision modelling and analysis, data envelopment analysis and
multiple objective optimisation. Emphasis will be placed on the integrated applications of these methods and tools to performance and efficiency
analysis and planning. The aim is to familiarise students with the applications of decision modelling and performance analysis methodologies.
Learning Outcomes
At the end of the course unit students should be familiar with concepts, methods and tools for decision tree analysis, multiple criteria decision
analysis, data envelopment analysis and multiple objective optimisation, which they can apply to support decision making and deal with performance
assessment and efficiency analysis problems. They should also be able to use appropriate software tools such as Excel and IDS Multicriteria Assessor.
Syllabus
The following topics will be covered:
Risk analysis and modelling. Decision analysis under risk and uncertainty
(maximum expected monetary decision criterion, decision tree analysis
and Bayes’ Theorem)
Certain monetary equivalent, utility theory and modelling of decision maker’s preferences.
Concepts, classification, problem structuring and model building for
multiple criteria analysis and performance assessment using financial and non-financial criteria.
Preference modelling and weight assignment
Performance assessment using the evidential reasoning approach
Methods and tools for multiple criteria decision analysis
Definition, measurement, and assessment of efficiency
Data Envelopment Analysis models and tools for efficiency assessment
Concepts, methods and tools for Multiple Objective Linear Programming
(MOLP)
Goal Programming (GP) and interactive MOLP methods for setting performance targets
Reading List
Belton, V., Stewart, T. J. (2002), Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers: Dordrecht.
Cooper, W. W, Seiford, L. M. and Tone, K. (2007), Data Envelopment analysis: a comprehensive text with models, applications, references and
DEA Solver software. 2nd edition, Springer.
Hillier, F. and Lieberman, G. (2010), Introduction to Operations Research
with CD-Rom. McGraw Hill.
Keeney, R.L. and Raiffa, H. (1993), Decision with Multiple Objectives:
Preference and Value Tradeoffs. Cambridge University Press.
Liu G. P., Yang J. B. and Whidborne, J. F. (2002), Multiobjective Optimisation and Control. Engineering Systems Modelling and Control Series,
Research Studies Press Limited, Baldock, Hertfordshire, England.
Saaty, T. L. (1988), The Analytic Hierarchy Process. University of Pittsburgh,
1988.
Sen, P. and Yang, J. B. (1998), Multiple Criteria Decision Support in
Engineering Design, Springer. London, ISBN 3540199322.
Xu, D. L. and Yang, J. B. (2003), Intelligent decision system for self-
assessment, Journal of Multiple Criteria Decision Analysis, Vol.12, 43-60.
Xu, D. L., McCarthy, G. and Yang, J. B., (2006) Intelligent decision system
and its application in business innovative capability assessment, Decision Support Systems, Vol.42, pp.664-673.
Yang, J. B. (2001), Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty, European Journal of
Operational Research, Vol. 131, No.1, pp.31-61.
Yang, J. B. and Xu, D. L. (2002), On the evidential reasoning algorithm for multi-attribute decision analysis under uncertainty, IEEE Transactions on
Systems, Man, and Cybernetics Part A: Systems and Humans, Vol.32, No.3, pp.289-304.
Yang, J. B., Wang, Y. M., Xu, D. L. and Chin, K. S. (2006), The evidential
reasoning approach for MCDA under both probabilistic and fuzzy
uncertainties, European Journal of Operational Research, Vol. 171, No.1, pp.309-343.
NOTE: additional references/readings will be given in lectures
Title BMAN 60422 Data Analytics for Business Decision Making
Credit Rating 15
Level 7
Semester 2
Course Coordinator(s) Dr Yu-Wang Chen
Methods of Delivery Lectures/Lab sessions
Lecture Hours 40 (2 hours lecture and 2 hours lab session per
week, over 10 weeks)
Seminar Hours
Private Study Hours 110
Total Study Hours 150
Pre-requisites N/A
Co-requisites N/A
Dependant Courses N/A
Assessment Methods and Relative
Weightings
50% Exam (close book, 2 hours) 50% Coursework (25% individual report and
25% group report and presentation)
Aims
The aim of this course is to provide students with an understanding of data analytics for business decision making. It will discuss a wide range of data
analytical techniques, including classification, clustering, predictive modelling, text mining, and visual analytics. Emphasis will be placed on the
use of an industry-leading software tool, SAS.
Learning Outcomes
At the end of the course unit, student should be able to:
Understand the fundamentals of data analytics and its application to business and management decision making,
Understand a variety of data analysis techniques, such as data classification and clustering, prediction and forecasting, association rule
mining & text mining, etc.,
Discuss how visual analytics can be used to understand big data, extract insights and identify patterns,
Demonstrate the ability to use specialised software tools, such as SAS, to
analyse large sets of data in real-world problems.
Syllabus
The following topics will be covered:
Introduction to data, relations and the fundamentals of data analytics
Data preparation, data pre-processing, and quality analysis
Data analysis – feature selection, classification and clustering
Data analysis – predictive and forecasting modelling
Data analysis – association rule mining & text mining
Visual analytics
Big data analytics
Reading List
Thomas A. Runkler, Data Analytics: Models and Algorithms for Intelligent
Data Analysis, Springer, 2012.
Max Bramer, Principles of Data Mining, Springer, 2013.
Michael R. Berthold, David J. Hand, Intelligent Data Analysis: An Introduction, Springer, 2007.
Paolo Giudici, Silvia Figini, Applied Data Mining for Business and Industry, 2nd Edition, 2009.
Gerhard Svolba, Data Quality for Analytics Using SAS, SAS Institute, 2012
Frank J. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money,
Wiley, 2012
Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S. Hopkins and Nina
Kruschwitz, Big Data, Analytics and the Path from Insights to Value,
MITSloan Management Review, Vol.52, No.2, 2011.
INFORMS Analytics Magazine, http://www.analytics-magazine.org/
NOTE: additional references/readings will be given in lectures
Title BMAN 70142 Simulation & Risk Analysis
Credit Rating 15
Level 7
Semester 2
Course Coordinator(s) Dr Julia Handl
Other Staff involved Dr. Nathan Proudlove
Methods of Delivery Lectures / Project workshops
Lecture Hours 30
Seminar Hours 0
Private Study Hours 120 hours
Total Study Hours 150 hours
Pre-requisites --
Co-requisites --
Dependant Courses --
Assessment Methods and Relative
Weightings
Coursework project (35% [20% individual management report, 15% team technical
report]), team presentation (15%), plus closed-
book exam (50%)
Aims
Analysing systems dominated by randomness and/or interactions or feedback between their constituent elements particularly challenging. Problems of this
type include operational risk analysis, revenue management and improving
operational process flow in service or manufacturing. This unit will focus on application of approaches developed to model such systems, including the
basics of queuing theory, Markov processes, risk management, and in particular computer-based simulation.
Learning Outcomes
At the end of the module students should be familiar with the concepts and types of tools and techniques commonly used in analysing the performance
of and risk in complex operational systems. They should be able to consider different approaches and their assumptions, advantages and disadvantages.
Students should be able to formulate, use and understand models of problem situations including, where appropriate, state-of-the-art software tools.
Syllabus
Overview of analytics approaches in analysing complex systems
Simulation concepts and approaches: spreadsheet-based, discrete-event
and system dynamics approaches and software tools
Risk analysis in risk management
Basic queuing theory models and operations management concepts in flow management
Introduction to Markov processes
Reading List
Main texts:
Pidd, M. (1998). Computer simulation in Management Science (5th ed),
Wiley.
Pidd, M. (2009), Tools for thinking (3rd ed), John Wiley & Sons, Chichester. (ebook available via library)
Hillier, F. and Lieberman, G.J. (2009), Introduction to operations research (9th ed), McGraw-Hill Education.
Savage, S.L. (2009), The Flaw of Averages, John Wiley & Sons. (ebook available via library)
Slack, N., Chambers, S., and Johnston, R. (2009), Operations management: principles and practice for strategic impact (6th ed),
Pearson Education Limited, Harlow
Supplementary reading:
Aven, T. (2003). Foundations of risk analysis – a knowledge and decision-
oriented perspective, Wiley.
Aven T (2008). Risk analysis: assessing uncertainties beyond expected
values and probabilities. John Wiley & Sons: Chichester, UK.
Aven T (2008). Risk analysis. John Wiley and Sons: Chichester, UK.
Bedford T and Cooke R (2007). Probabilistic risk analysis: foundations and methods. Cambridge University Press: Cambridge, UK.
Additional background references may be listed with the material for the
sessions - these are for interest and to provide more depth for interested students.
Title BMAN 71652
Information and Knowledge Management
Credit Rating 15
Level 7
Semester 2
Course Coordinator(s) Prof Peter Kawalek
Methods of Delivery
Lecture Hours 20
Seminar Hours
Private Study Hours 130
Total Study Hours 150
Pre-requisites
Co-requisites
Dependant Courses
Assessment Methods
and Relative Weightings
30% group presentation 70% individual coursework
Aims
Information and Knowledge are major and exponentially growing resources within the modern organisation, be it in the private or public sector, SME or
multinational corporation. The effective management of both information and knowledge is therefore of strategic importance to all successful business or
public sector organisations.
The aims of this module are therefore:
To explore these growing organisational information and knowledge resources
To identify how they are strategically and operationally managed and exploited effectively within and between organisations.
To develop skills in the techniques of information and knowledge management
On successful completion of this course unit, students should be able
to:
Appreciate the roles of information and knowledge as essential
organisational resources that require strategically planning, managing and exploiting effectively.
Understand the difference and the relationship, within organisations,
between codified technologically mediated information and non-codified humanly mediated information
Understand how formalised information is strategically planned for and
managed, both within an organisation and in its external relationships with customers and other organisations.
Understand the nature of Knowledge and how it is deployed and managed
within the modern organisation
Demonstrate an understanding of the various technologies that can be
used to implement Knowledge Management systems within such organizations
Learning Outcomes
Academic knowledge An appreciation of what is meant by formalised and technologically
mediated information and non-formalised information and their relationship to organisational effectiveness.
An understanding of knowledge, what it is used for and how it is managed within the modern organisation
An understanding of the tools and techniques assocatiated with the management of knowledge within organisations
Intellectual skills An ability to critique the concept of information and knowledge
management and the solutions proposed for it. An ability to understand the role of knowledge within modern organisations
and how its management adds value to performance
Subject practical skills Assess and evaluate organisational information and knowledge resources
and linking these with appropiate strategies Develop an ability and understanding of how to undertake the
management of information & knowledge within an organisational setting
Transferable skills
Use the concepts, tools and techniques of information and knowledge management strategy, planning and solutions within MSc project and
dissertation Develop the appropriate analysis and consultancy skills
Syllabus
Information and Knowledge Management – models and definitions
The information management cycle
Strategies and Systems for effective information and knowledge
management
Exploring and exploiting information and knowledge resources within organisations
The role of Information and Knowledge systems
How knowledge is managed within organisations – policies, strategies, tools and techniques
Learning Organisations, Communities of Practice
Reading List
Chaffey, D & White G. (2011) Business information management, Pearson, Harlow, 2nd edition
S.Newell, M.Robinson, H.Scarborough & J.Swan (2010) Managing Knowledge, Work and Innovation, Palgrave Macmillan.
K. Grant, R. Hackney & D. Edgar (2010) Strategic Information Systems
Management, Cengage.
Title MCEL40042 Business Feasibility Study
Credit Rating 15
Level 7
Semester 2
Course Coordinator(s) Jonathan Styles
See online description at http://courseunits.humanities.manchester.ac.uk/Undergraduate/MCEL400
42/Display