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.
This course will cover the approaches that are common in new data mining applications. I.e., unstructured or big data, typically generated by Web 2.0 and Web 3.0 environments, require different analytics techniques, such as web analytics, recommender systems, the analysis of links and the establishing of search engines, text mining, sentiment analysis, and so on.
These techniques are still quickly developing, and are widely used in industry to cater for a new age of analytics.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2017/18, Not available to visiting students (SS1)
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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 )
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| Assessment (Further Info) |
Written Exam
30 %,
Coursework
70 %,
Practical Exam
0 %
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| 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)
The assessment consists of the group works¿ reports, for which individual scores will be based on peer assessment, and the presentation of that report. The report is due 1 week before the presentations, which take place in the last week of teaching.
The exam will be an individual, written, closed-book exam during the exam diet.
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| Feedback |
Feedback on the reports will be provided before the final exam, and students can verify intermediate results and ideas with the lecturer to scope their report. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | Media and Web Analytics | 2:00 | |
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
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Reading List
- Web Analytics 2.0 (Avinash Kaushik)
- Social Network Data Analytics (Charu C. Aggarwal)
- An Introduction to Information Retrieval (Manning, Raghavan, Schütze)
- Mining Text Data (Charu C. Aggarwal, ChengXiang Zhai)
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Additional Information
| Graduate Attributes and Skills |
Knowledge and understanding:
- A critical understanding of the principal theories, concepts and principles of analytics techniques that support media and web analytics, mainly text and unsupervised learning techniques.
- A critical understanding of a range of specialised theories, concepts and principles that apply to media and web analytics.
- A critical awareness of current issues in a subject/discipline/sector and one or more specialisms through informing by state-of-the-art research.
Applied knowledge, skills, and understanding
- In using a range of specialised skills, techniques, practices and/or materials that are at the forefront of, or informed by forefront developments.
- In applying a range of standard and specialised research and/or equivalent instruments and techniques of enquiry.
- In planning and executing a significant project of research, investigation or development. In demonstrating originality and/or creativity, including in practices.
Generic cognitive skills:
- Develop original and creative responses to problems and issues.
- Deal with complex issues and make informed judgements in situations in the absence of complete or consistent data/information.
Communication, ICT, and numeracy skills:
- Communicate, using appropriate methods, to a range of audiences with different levels of knowledge/expertise.
- Communicate with peers, more senior colleagues and specialists.
- Use a wide range of ICT applications to support and enhance work at this level and adjust features to suit purpose.
- Undertake critical evaluations of a wide range of numerical and graphical data.
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| Keywords | BS-MWA |
Contacts
| Course organiser | Dr Johannes De Smedt
Tel: (0131 6)51 1046
Email: Johannes.DeSmedt@ed.ac.uk |
Course secretary | Mr Peter Newcombe
Tel: (0131 6)51 3013
Email: Peter.Newcombe@ed.ac.uk |
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