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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Industrial Analytics (CMSE11352)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits15 ECTS Credits7.5
SummaryThe course will provide students the foundations of econometric analysis using the industry and firm level data. In the era of big data, the importance of industrial analytic skills for graduates in business can hardly be overstated.
Course description Academic 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.


Outline Content
Block 1: Price discrimination
Block 2: Price elasticity and brand competition
Block 3: Demand and Supply System
Block 4: Merge and acquisition
Block 5: Structure estimation of Customer Demand


Student Learning Experience
Students will learn basic empirical analysis of Industrial organisation from 5 two-hour workshops and 4 1-hour lectures. The lecture will cover introduction of basic econometric methods and tools, and students will apply these methods to solve industrial-level and market-level analytical problems in workshops. The final exam will ask students to propose methods to solve a real-world analytical problem, and interpret analytical results.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2019/20, Not available to visiting students (SS1) Quota:  None
Course Start Block 4 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 04, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 133 )
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) Individual Written Examination (100% weighting)
Assesses Learning Outcomes 1 to 4.

The exam will be based on lecture materials and workshops. It will ask students to propose methods to solve a real-world analytical problem, and interpret analytical results.
Feedback Feedback on formative assessed work will be provided in line the Taught Assessment Regulation turnaround period, 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 will be communicated to students during semester. All assessments will be marked according to the University Common Marking Scheme.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand how to use data analytical techniques help decision making in a business environment
  2. Describe how firms differentiate with each other: vertical & horizontal differentiation. understand common tools for analysing these models empirically
  3. Identify the effectiveness of firms┐ strategies
  4. Have an overview of structural estimation
Reading List
Recommended Textbook:
Lynne Pepall, Dan Richards and George Norman, "Industrial Organization: Contemporary Theory and Empirical Applications". 5th Edition, 2013, ISBN-13: 978-1-118-25030-3


Recommended Readings:
Nevo, A. (2000). A practitioner's guide to estimation of random-coefficients logit models of demand. Journal of economics & management strategy, 9(4), 513-548.
Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.
Don E.Waldman, Elizabeth J. Jensen (2013), "Industrial Organization: Theory and Practice", ISBN-13: 978-1292039985
Oz Shy (1996), "Industrial Organization: Theory and Application", ISBN-13: 978-0262691796
Geoff Harcourt, Clive W. J. Granger (1999), "Empirical Modeling in Economics: Specification and Evaluation", ISBN-13: 978-0521778251
Additional Information
Graduate Attributes and Skills Research & Enquiry:
On completion of the course, students should be able to:
-Understand how to use data analytical techniques help decision making in a business environment
-Describe how firms differentiate with each other: vertical & horizontal differentiation. understand common tools for analysing these models empirically
-Identify the effectiveness of firms' strategies

Personal & Intellectual Autonomy:
On completion of the course, students should be able to:
-Use empirical analysis for better decision making
-Discuss advantages and drawbacks of popular analytic techniques
-Use state-of-the-art tools in conducting industrial analysis
-Develop appropriate programming skills for industrial analysis

Communication skills
On completion of the course, students should be able to:
-Explain the implications of formal/quantitative models to general audiences
-Use formal/quantitative models to elaborate strategic concerns in managerial and economic problems
KeywordsNot entered
Contacts
Course organiserDr Tong Wang
Tel: (0131 6)51 5551
Email: Tong.Wang@ed.ac.uk
Course secretaryMiss Lauren Millson
Tel: (0131 6)51 3013
Email: Lauren.Millson@ed.ac.uk
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