Postgraduate Course: Media and Web Analytics (CMSE11353)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | This is an option course for the new MSc in Business Analytics programme. The course will provide students with the foundations of media analytics to respond to the job market needs and shall cover concepts, applications, modelling and analysis techniques of both social media (e.g., Facebook, Twitter, LinkedIn, YouTube) data and web data. |
| Course description |
This course aims at training students in the field of media analytics to respond to the job market needs using a variety of methodologies to generate intelligence and assist with business decision making including statistical, stochastic, and artificial intelligence modelling and analysis frameworks with business applications in several areas.
The objective of this course is to enhance students¿ understanding of the importance for businesses to analyse social media and web data to make better decisions and to provide them with a variety of modelling and analysis techniques commonly used by both academics and practitioners 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 media analytics, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2017/18, 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 %
|
| Additional Information (Assessment) |
Examination
Final exam 30% weighting
Coursework
Term projects 60% weighting
Presentations 10% weighting
- Term projects (60% of the mark including a peer assessment component worth 10%) in which students will have to undertake a project on media analytics involving the modelling and analysis of a social media or web set of data to address some relevant business research questions, report on findings, formulation of recommendations and managerial guidelines.
- Presentations (10% of the final mark) involving communication of viable media analytics based solution to a business decision problem and the methods used to obtain them to demonstrate their ability to address real world problems and to convince their line managers or sponsors to base their plans on the proposed solution
- Exam(s) (30% of the final mark) |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Discuss the concept and methods of media analytics using the proper terminology
- Identify and properly describe decision problems related to media analytics in different business settings
- Choose the right models and analyses, implement them, and compare the performance of different models and analyses empirically
- Formulate managerial guidelines and make recommendations
- Communicate solutions effectively and efficiently to a critical audience
|
Reading List
SUGGESTION:
Gohar F. Khan (2015) Seven Layers of Social Media Analytics: Mining Business Insights from Social Media Text, Actions, Networks, Hyperlinks, Apps, Search Engine, and Location Data (ISBN-10: 1507823207).
|
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | BS-MWA |
Contacts
| Course organiser | Dr Johannes De Smedt
Tel: (0131 6)51 1046
Email: |
Course secretary | Mr Peter Newcombe
Tel: (0131 6)51 3013
Email: |
|
|
University Homepage