University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Industrial Analytics (CMSE11352)

Course Outline
SchoolBusiness School CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryThis course provides knowledges of analytical frameworks in industry level. Students will develop a better understanding of competition pattern in various industries, with a special emphasis on firms' strategic behaviour. Based on the theoretical frameworks, students will also learn how to empirically extract information from industrial-level data set.
Course description Industry analysis has a long history which can be traced back from Michael Porter's five-force analysis. Nowadays, this practice is widely adopted in a lot of business activities including business consultancy, strategic analysis, etc. This course aims at training students in the field of industrial analytics using a variety of methodologies. To be more specific, this course covers the typical theories of industrial organisation along with a range of techniques to analyse an industry, assess business models, identify competition patterns, and propose appropriate strategies for high-level managers.
The objective of this course is to enhance students' understanding of the importance of adopting a series of sound methodological steps in industrial analytics and to provide them with a variety of modelling and analysis techniques along with hands-on experience in using them. The course provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field. The course will require students to work in groups on realistic projects in different business settings involving industry analysis and profiling, business models assessment, identification of competition patterns, and design of appropriate strategies for high-level managers, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.
Aims, Nature, Context

In the era of big data, the importance of industrial analysis skills for graduates in business can hardly be overstated.

Industry data availability has been improved during the past decades, which make industry-level data analytical skill development in UK universities has been a hot topic of discussion in a number of recent works. The objective of this course is to:
- Provide an introduction to industrial organization and industrial analysis techniques;
- Illustrate selected state-of-the-art tools (Stata, Python and R) typically adopted in the context of industrial analysis;
- Illustrate typical frameworks used in industrial analysis.
- Let students familiarize with industrial analysis tools and techniques in small projects. There will be some recommended topics but students are also allowed to develop their own project independently.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 117 )
Assessment (Further Info) Written Exam 30 %, Coursework 70 %, Practical Exam 0 %
Feedback All students will be given informal but instant feedback on their classroom activities.

At least one formative feedback or feedforward event for every assessment component in the course in time to be useful in the completion of summative work on the course.

Feedback on formative assessed work will be provided within 15 working days of submission, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.

Feedback will comprise individual feedback on student assignments and overall exam mark feedback in the form of a report.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Industrial Analytics2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand what the market structure and market power are
  2. Describe key trade-offs in common industrial analysis, such as price, quality, reputation, etc.
  3. Describe how firms differentiate with each other: vertical & horizontal differentiation. understand common tools for analyzing these (Hotelling, Bertrand, Stackelberg models...)
  4. Describe the situation of strategic policy commitment
  5. Analyse business situation using industrial and firm level data
Reading List
Lynne Pepall, Dan Richards and George Norman, Industrial Organisation: Contemporary Theory and Empirical Applications. 5th Edition, 2013, ISBN-13: 978-1-118-25030-3
Recommended Reading
Jean Tirole, The Theory of Industrial Organization, 1988, ISBN-13: 978-0262200714
Don E.Waldman, Elizabeth J. Jensen, "Industrial Organization: Theory and Practice", 2013, ISBN-13: 978-1292039985
Oz Shy, "Industrial Organization: Theory and Application", 1996, ISBN-13: 978-0262691796
Geoff Harcourt, Clive W. J. Granger, "Empirical Modeling in Economics: Specification and Evaluation", 1999, ISBN-13: 978-0521778251
Additional Information
Graduate Attributes and Skills A. Academic knowledge
1. understand what the market structure and market power are
2. describe key trade-offs in common industrial analysis, such as price, quality, reputation, etc.
3. understand what a monopolistic firm is and where the monopoly power comes from
4. describe how firms differentiate with each other: vertical & horizontal differentiation. understand common tools for analyzing these (Hotelling, Bertrand, Stackelberg models...)
5. describe the situation of strategic policy commitment
6. understand vertical and horizontal relations
7. understand network and auctions mechanism

B. Intellectual skills
1. discuss relevant strategies that should be adopted under certain industrial circumstances
2. select the most appropriate framework for a given industry and conduct analysis
3. discuss advantages and drawbacks of popular analytic frameworks

C. Professional/subject specific/practical skills
1. use state-of-the-art tools in conducting industrial analysis
2. learn cooperating in teams to conduct practical analysis
3. develop appropriate programming skills for industrial analysis

D. Transferable skills
1. report writing skills
2. presentation skills
3. cooperating skills
4. quantitative analytical skills
5. self-awareness through written reflection
Course organiserDr Tong Wang
Tel: (0131 6)51 5551
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information