THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2021/2022

Information in the Degree Programme Tables may still be subject to change in response to Covid-19

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

Postgraduate Course: Economics and Business Analytics (CMSE11414)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits15 ECTS Credits7.5
SummaryThe objectives of the course are to give students both knowledge and understanding of selected key concepts in business economics and management science and the knowledge to understand when these concepts may be useful to make decisions. The basic skills to implement these techniques will also be developed. The first part of this course places special focus on the factors that influence the firms' economic environment, which, in turn, determines the level, growth and sustainability of profits. These include consumer demand, technology and costs, competition, entry, pricing strategies, and government regulations. The second part of this course focuses on the science behind decision-making, covering a wide range of quantitative methods typically employed to that end, such as forecasting and optimisation, and data visualization.
Course description The Economics part of the course comprises the following topics:
1) Quantitative Demand Analysis (drivers of demand and the econometric estimation of demand functions).
2) Productivity and Efficiency Analysis (Economies of Scale and Data Envelopment Analysis)
3) Market Structure, Conduct and Performance (The functioning of the market and the SCP Paradigm)
4) Pricing with Market Power (Price Discrimination and Versioning)
5) Network Economics

The Business Analytics part of the course comprises the following topics
6) Forecasting and Optimization (Time-series analysis and Linear Programming)
7) Decision Analysis (Single and multi-criteria methods, Location Analysis)
8) Data Visualization
9) Introduction to Data Mining (Clustering)

Student Learning Experience:
The weekly lectures will explore core concepts while the tutorials focus on the practical applications. Real-life examples will be discussed in class.

Tutorial/seminar hours represent the minimum total live hours - online or in-person - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, lecture, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2021/22, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Seminar/Tutorial Hours 15, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 132 )
Additional Information (Learning and Teaching) Seminar/Tutorial hrs are the min total live hrs, online or in-person, students can expect to receive
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 50% coursework (individual) assessing Learning Outcome 1
50% coursework (individual) assessing Learning Outcome 2

Feedback Formative feedback: Students will gain feedback on their understanding of the material when they discuss their answers to the questions in the tutorials. Students may also ask questions in Lectures to assess their knowledge.

Summative feedback: Summative feedback on the assessment will be provided in line with the published feedback deadlines.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Apply economic concepts for the quantitative analysis of demand, pricing, productivity or efficiency as well as the qualitative understanding of market stucture and its implications on firm performance.
  2. Apply tools of business analytics (including data visualization) to the analysis of future demand, resource optimization, optimal business location or customer segmentation.
Reading List
Baye, M. Managerial Economics and Business Strategy. McGraw Hill. (7th ed)

Anderson, D.R. Sweeney, D.J., Williams, T.A. and Wisniewski, M. (2009). An Introduction to Management Science: Quantitative Approaches to Decision Making. South Western

Resource List:
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/lists/26181448760002466?auth=SAML
Additional Information
Graduate Attributes and Skills After completing this course, students should be able to:

-use certain economic principles to recommend decisions to managers or evaluate certain decisions that managers make in the light of economic principles and models;

-analyse the demand, cost, and competitive conditions facing an organisation and their impacts on pricing, productivity and/or efficiency;

-identify situations in which model-building methods can be applied in the management decision process;

-build and solve certain types of model in Excel and interpret their solutions;

-use several analytics software packages;

-design effective data visualizations.
KeywordsNot entered
Contacts
Course organiserDr Augusto Voltes-Dorta
Tel: (0131 6)51 5546
Email: Augusto.Voltes-Dorta@ed.ac.uk
Course secretaryMiss Mary Anne Boeff
Tel: (0131 6)50 8072
Email: MaryAnne.Boeff@ed.ac.uk
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