Postgraduate Course: Web and Social Network Analytics (CMSE11427)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides students both theoretical and practical fundamentals of analysing web data from various aspects, including web page analysis, social network analysis, market basket analysis, and recommendation system design. |
| Course description |
Outline Content
1. Web and web analysis evolution
2. Analysing web as a graph
3. Online social network and its analysis
4. Unsupervised techniques for analysis
Student Learning Experience
Weekly lectures and hands-on programming exercises (computer lab) in Python which enables students to implement the methodologies covered in class.
|
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 9,
Seminar/Tutorial Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
85 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes
|
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessment within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Analyse a company's website and web presence
- Make recommendations towards improving the visibility of a company on the web
- Use unsupervised learning techniques for modelling customer and product recommendations
|
Reading List
Liu B. Web Data Mining Exploring Hyperlinks, Contents, and Usage Data / Bing Liu. Second edition. Springer; 2011.
Easley D. Networks, Crowds, and Markets: Reasoning About a Highly Connected World / David Easley, Jon Kleinberg. (Kleinberg J, ed.). Cambridge University Press; 2010.
Avinash Kaushik. Web Analytics: an Hour a Day. John Wiley & Sons; 2007.
Aggarwal CC, ed. Social Network Data Analytics. 1.. ed. Springer US: Imprint: Springer; 2011.
Kaushik A. Web Analytics 2.0: the Art of Online Accountability & Science of Customer Centricity. Wiley; 2010.
Leskovec J. Mining of Massive Datasets / Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. Third edition. (Rajaraman A, Ullman JD, eds.). Cambridge University Press; 2020. |
Additional Information
| Graduate Attributes and Skills |
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore
and solve them responsibly.
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Practice: Applied Knowledge, Skills and Understanding
After completing this course, students should be able to:
Work with a variety of organisations, their stakeholders, and the communities they serve - learning from them, and aiding them to achieve responsible, sustainable and enterprising solutions to complex problems.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to
quality. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Zexun Chen
Tel: (0131 6)50 8074
Email: Zexun.Chen@ed.ac.uk |
Course secretary | Miss Leah Byrne
Tel:
Email: lbyrne4@ed.ac.uk |
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