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 is an introductory level course that lays the groundwork for understanding digital data analysis. Designed for beginners, it provides a high-level overview of essential analytical techniques used to assess web performance and social media dynamics. The course introduces key concepts such as web evaluation, clickstream analysis, and the basics of network and social network analysis, while also offering insights into basket analysis and the design of simple recommendation systems. With a balanced mix of theory and practical application, this course equips learners with the foundational skills needed to navigate the evolving landscape of digital analytics and drive informed decision-making in a data-centric world. |
| Course description |
This course is designed to provide a solid foundation in analysing digital platforms. This course introduces students to the core concepts of web evaluation, where they learn to assess website performance and user engagement. The curriculum then explores web clickstream analysis, equipping learners with the tools to interpret user navigation and behaviour.
Fundamental principles of network analysis are also covered, setting the stage for understanding the dynamics of online social networks. Students will gain insights into the structure and influence of digital communities, learning how relationships and interactions can be mapped and analysed. Additionally, the course introduces basket analysis to reveal purchasing patterns and trends, along with the basics of designing recommendation systems using simple machine learning techniques.
Through a blend of lectures, hands-on lab sessions, and a practical project, students will develop essential analytical skills that are critical for digital marketing and customer behaviour analysis. This course is ideal for beginners seeking a introduction to the rapidly evolving fields of web and social media analytics.
Outline content
1. Web & Web Analytics
2. Search Engines and Web 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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | Students MUST also take:
Predictive Analytics and Modelling of Data (CMSE11428)
|
| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students only, or by permission of course organiser. Please contact the course secretary. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, 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
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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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