Postgraduate Course: Methods and Tools for Business Analytics (CMSE11337)
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This course aims to equip students with tools and frameworks to tackle common decision problems in Business Analytics. The course develops skills in understanding, framing and structuring a managerial problem, as well as building, verifying, validating and using decision support models to inform better and more effective decision making. In terms of the specific modelling and analysis techniques studied in this course, the course will cover both single- and multiple-criteria decision making and support techniques, namely: linear and integer programming, goal programming, decision analysis, the Analytic Hierarchy Process (AHP) and discrete event simulation. Spreadsheet modelling using Microsoft Excel will also be covered at the outset, to ensure all students possess the skillset required in quantitative modelling of business decisions in the form of electronic spreadsheets. Working on an extensive case study project and realistic examples, students will learn how to model and analyse decision problems with: commercial software for discrete event simulation (Arena) and the AHP (Transparent Choice), spreadsheet modelling tools (Excel) and statistical analysis (Minitab), thus complementing learning experiences of other software packages on the programme (e.g. SPSS, SPSS Modeller, etc.).
Aims, Nature, Context
This course builds on knowledge gained in the core courses of the MSc Marketing and Business Analysis programme, particularly: Marketing Decision Analysis, Business Statistics and the quantitative component of Marketing Research. Methods and Tools for Business Analytics (MTBA) complements these other courses and minimises overlap of materials.
Teaching will involve a combination of lectures, tutorials and independent study. The key issues and techniques will be presented in lectures. Tutorial examples will re-enforce learning through practical experience as well as offering the opportunity to obtain immediate feedback from the lecturer should any problems arise. Furthermore, tutorials will also offer the opportunity to develop technical modelling skills. Students will also be expected to engage in self-study, both to consolidate the learning of core work and to familiarise themselves with the broader literature. A key component of the learning experience is represented by the coursework, where students will apply one of the techniques studied in the course, discrete event simulation, to a realistic case study, thereby simulating a realistic case of management consulting project where simulation is employed.
A key component of the learning experience is represented by the coursework, where students will apply the techniques studied in the course to a real world problem provided by a real client. Which techniques are most suitable to the specific assigned problem will depend on a number of factors and will be to a great extent the choice of the students working on each project, based on the requirements provided by the client, the availability of data, etc.
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Marketing Decision Analysis (CMSE11120)
||Other requirements|| For Business School PG students only, or by special permission of the School. Please contact the course secretary.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2017/18, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 20,
Fieldwork Hours 50,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||The feedback consists of: continuous (formative) feedback on practical tutorial work; (summative and formative) feedback in the coursework/project; and (summative) feedback in the written exam.
Students will gain in-process formative feedback on their understanding of the material when they discuss their answers to the tutorial/workshop questions posed during the computer labs. Students are also strongly encouraged to ask questions in lectures to assess their knowledge and understanding of the subject as the course progresses. Finally, the lecturer will be available to provide one-on-one formative feedback to individual students who wish to meet, in which case a time slot must be booked via email by contacting the lecturer directly. Overall, it is envisaged that about 2 hours per student will be allocated by the course organizer and lecturer to continuous formative feedback, spread throughout the whole duration of the course and based on a timeline that will vary from student to student, upon request.
Feedback on the coursework will be provided within 15 working days of submission, or in time to be of use in subsequent assessments within the course, that is the final written examination, whichever is sooner. Summative exam marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
On completion of this course, the student will be able to:
- Understand and critically discuss the nature of problem solving and decision making (and its support through quantitative techniques) in Business Analytics
- Understand and critically assess various methods for structuring management decision problems.
- Understand and apply the techniques for modelling decision problems, linear programming, integer programming, decision treess and payoff tables, the Analytic Hierarchy Process and discrete event simulation.
- Understand and critically assess the relative merits of the techniques mentioned in Learning Outcome 3.
|The official course material is released in the form of slide sets. These will be made available prior to each session (lecture,tutorial), as the course progresses. This official material, especially if integrated by good quality own notes by the student, should be enough both to prepare for the exam and to carry out a successful coursework, provided the student also engages, at the same time, with the practical aspect of the course in the computer lab. |
The following textbooks provide useful references for different parts of the course. Specific references to these sources are given in the official course slides whenever a piece of theory or a particular example or tutorial is taken or developed from these sources. These textbooks are available in The Hub for consultation and integration with all other course material.
* Pidd, M. (2014), Tools for Thinking - Modelling in Management Science (Third Edition), Wiley
* Anderson, D.R., Sweeney, D, Williamns, A.W. and Wisniewski, M. (2014), An Introduction to Management Science - Quantitative Approaches to Decision Making (Second Edition), South-Western Cengage Learning
* Albright, S.C and Winston, W.L. (2004), Spreadsheet Modeling and Applications - Essentials of Practical Management Science, South-Western Cengage Learning
* Law, A.M. (2014) Simulation Modeling and Analysis (5th edition), McGraw-Hill
* Kelton, W.D., Sadowski, R.P. and Zupick, N.B. (2014) Simulation with Arena (6th edition) McGraw-Hill
* Walkenbach, J. (2016) Microsoft® Excel® 2016 Bible, Wiley
|Graduate Attributes and Skills
Students will develop skills such as:
* the ability to build models to support management decision making;
* the ability to critically validate models for management decision making;
* the ability to interpret results from decision-making models in light of possible courses of action for a given business analytics problem or situation;
* the ability to understand methods of model solution.
Subject Specific Skills
Students will gain:
* an appreciation of methods involved in business decision modelling;
* experience in applying model building methods to realistic examples;
* experience in using commercial software to tackle management decision problems.
By the end of the course students will be expected to:
* be able to plan and carry out analyses based on construction and solution of appropriate models;
* be able to employ analytical and problem-solving skills;
* show that they can report results in a concise way;
* have enhanced their skills in using commercial software products.
|Course organiser||Dr Maurizio Tomasella
|Course secretary||Ms Emily Davis
Tel: (0131 6)51 7112