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 | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course deals with the various aspects that are related to analysing web data. First of all, the analysis of a web page is covered. Next, the Internet will be approached as a graph, where various analyses regarding the importance, visibility and role of each node within the net. Finally, various methodologies for handling data stemming from ecommerce such as purchase data are covered in light of frequent item set analysis (market basket analysis), and recommender systems. |
| Course description |
Outline Content:
1. Web analytics
2. The web as a graph
3. Social networks
4. Unsupervised techniques
Student Learning Experience:
Weekly lectures and hands-on programming exercises in Python which enables students to implement the methodologies covered in class.
Assessment:
Project will consist of students using all skills that they acquired to analyse a set of datasets describing a real-world online business. Students will need to show proficiency in the techniques they learned and an ability to communicate their findings in non-technical terms.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | Students MUST also take:
Predictive Analytics and Modelling of Data (CMSE11428)
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| Prohibited Combinations | |
Other requirements | For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary. |
Course Delivery Information
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| Academic year 2020/21, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 10,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Individual Project report (with accompanying calculations). 100% covers LO1, 2, 3.
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| Feedback |
The assessments will be marked according to the University common marking scheme. Feedback on formative assessed work will be provided in line with the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester. |
| 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
Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity, Avinash Kaushik
Social Network Data Analytics, Charu C. Aggarwal
Introduction to Information Retrieval, Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
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Additional Information
| Graduate Attributes and Skills |
After completing this course, students should be able to:
A. Knowledge and Understanding:
1. show a thorough understanding of the landscape of web analytics both from a corporate, as well as a technical perspective
2. show a thorough understanding of the web both from a web analytics, as well as a graph perspective
3. analyse nodes in a social network towards their importance and influence
4. demonstrate a thorough understanding of market basket and recommender system techniques
B. Practice: applied knowledge, skills and understanding:
1. construct a recommender system and market basket analysis out of purchase data
2. provide a comprehensive overview of the various corporate applications of social network analysis
3. show a thorough understanding of the various techniques used in a business environment to support web analytics
C. Communication, ICT and numeracy skills:
1. collect data from websites
2. construct social network models in Python
3. analyse website and social network data with Python
D. Generic Cognitive Skills:
1. demonstrate report writing skills;
2. demonstrate presentation skills;
3. demonstrate business understanding and problem-solving skills;
4. demonstrate awareness of group dynamics.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Pawel Orzechowski
Tel:
Email: porzecho@ed.ac.uk |
Course secretary | Ms Emily Davis
Tel: (0131 6)51 7112
Email: Emily.Davis@ed.ac.uk |
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