Postgraduate Course: Data Analytics for Web and Social Media (CMSE11690)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course is an introductory course that lays the groundwork for understanding digital data analysis. It provides a high-level overview of how to evaluate websites and analyse user clickstream data, setting the stage for grasping the fundamentals of network and social media analysis. Students will be introduced to essential concepts such as network basics and simple recommendation system design, along with techniques like basket analysis to identify underlying patterns in consumer behaviour. This course is designed to spark interest in the field by balancing theoretical insights with practical applications, offering a concise yet robust exploration of digital analytics. It serves as an ideal starting point for those looking to build a foundation in data analytics within the dynamic context of the web and social media landscape |
| 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, participants will develop essential analytical skills that are critical for digital marketing and customer behaviour analysis. This course is ideal for beginners seeking a comprehensive yet accessible 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 in Python which enables students to implement the models and methods covered in class.
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Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 8,
Seminar/Tutorial Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
86 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Essay (Individual) - 1,200 words (+10% maximum) - Assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on assessment within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Collect data from a web as well as social media and carry out the relevant analysis.
- 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.
Manning CD. Introduction to Information Retrieval. (Raghavan P, Schutze H, eds.). Cambridge University Press; 2008. https://contentstore.cla.co.uk/secure/link?id=95e4596e-5280-ec11-94f6-a04a5e5d2f8d
Aggarwal CC, ed. Social Network Data Analytics. 1.. ed. Springer US: Imprint: Springer; 2011.
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Additional Information
| Graduate Attributes and Skills |
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.
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.
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. |
| Keywords | Web Analytics,Complex Network,Social Network,Social Media |
Contacts
| Course organiser | Dr Zexun Chen
Tel: (0131 6)50 8074
Email: Zexun.Chen@ed.ac.uk |
Course secretary | |
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