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

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