These lab materials are shared across two courses with identical content:
| Course | Code | Programme |
|---|---|---|
| Web and Social Network Analytics | CMSE11427 | Postgraduate |
| Data Analytics for Web and Social Media | CMSE11690 | Postgraduate |
Level: SCQF Level 11 (Postgraduate) Credits: 10 SCQF / 5 ECTS School: Business School, University of Edinburgh
This introductory course provides foundational skills in digital data analysis, covering web evaluation, clickstream analysis, network analysis, and recommendation systems. The labs combine theory with hands-on Python programming.
| Week | Topic | Key Skills |
|---|---|---|
| 0 | Python Fundamentals | Variables, data structures, pandas, numpy, matplotlib, sklearn basics |
| 1 | Web & Web Analytics | HTML scraping, BeautifulSoup, Selenium, APIs, clickstream analysis |
| 2 | Search Engines & Web Graph | LLM-based scraping, site mapping, PageRank algorithm |
| 3 | Online Social Networks | Graph theory, NetworkX, centrality metrics, community detection |
| 4 | Unsupervised Techniques | K-Means, DBSCAN clustering, matrix factorization, recommendation systems |
Week 0: Python Foundation (if you are not familiar with Python)
↓
Week 1: Data Extraction (Web Scraping)
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Week 2: Understanding Web Structure (Graphs, PageRank)
↓
Week 3: Network Analysis (Social Networks)
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Week 4: Pattern Discovery (Clustering, Recommendations)
- Complete Week 0 first - Python skills are essential for all subsequent weeks
- Use developer tools - Browser inspector is your friend for web scraping
- Read the documentation - Official docs for sklearn, networkx, and pandas are invaluable
- Work through APCs - They directly prepare you for the final assessment
- Experiment with parameters - Open questions encourage exploration
- Consider performance - Large datasets may require subsetting
Last updated: January 2026