Postgraduate Course: Media and Web Analytics (CMSE11353)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| 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
| Not being delivered |
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)
¿ Mining Massive Datasets (Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman)
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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 | Miss Lauren Millson
Tel: (0131 6)51 3013
Email: Lauren.Millson@ed.ac.uk |
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