MSc Data Science (Social Analytics) / Course details

Students are strongly recommended to join this course only if they have prior training in social network analysis, such as SOCY60361 (Social Network Analysis) in semester 1 or the undergraduate course SOST30022 (Network analysis) in semester 2.  

The unit aims to:  

1. Present the rationale for statistical network modelling.  

2. Define network models.  

3. Provide an overview of the most popular statistical models for network analysis.  

4. Demonstrate how these models can be applied to empirical data.  

The course involves lectures and computer tutorials. The lecture component provides theoretical and methodological frameworks for learning about the analysis of social network data and the key pathways from theory to subjecting research questions to empirical scrutiny. The tutorials are linked to the lectures and serve to give a concrete and hands-on perspective on the material taught. Furthermore, the tutorials give students training in specific methodologies and embed practical skills. The tutorials have an immediate goal of equipping students with the necessary skills and knowledge to complete the assignment. Blackboard resources are used to enable students to access teaching data and data sources. Students are provided with video materials of lectures and software tutorials. 

A1. Critically engage with the theoretical foundations of network analysis and use them to formulate empirical questions relevant to network analysis. 

A2. Design and develop network studies. 

A3. Understand the variety of network data. 

A4. Assess the applicability of network-analytical techniques to a given dataset. 

A5. Understand the motivation behind the statistical modelling of networks. 

A6. Critically understand and evaluate network-analytical research, reflect upon related methodology in a theoretically-informed way. 

A7. Understand network-analytical research questions in multidisciplinary contexts, and efficiently operationalise them. 

B3. Critically discuss network-analytical literature applying complex statistical models and identify the most appropriate statistical model for a given research problem. 

B4. Examine network structures using descriptive measures, and statistically model the mechanisms for social network formation and evolution. 

B6. Master advanced methods for data visualization. 

B7. Report results of social network analysis in written form. 

C2. Design and develop tailored research projects on a variety of real-world problems. 

C4. Collect, manage, and analyse online and offline datasets, and efficiently approach data analysis and management. 

C5. Produce state-of-the-art data visualization with high impact and informativity. 

C6. Be proficient in software that handles quantitative and network data. 

D1. Develop new or enhanced skills to identify and use diverse social network data and use such data to inform research projects and interventions in a variety of contexts. 

D2. Understand and mediate multidisciplinary environments and liaise across different intellectual and practical contexts 

D3. Work collaboratively, both face-to-face and online. 

D4. Accurately and effectively work with numbers and use advanced computational software. 

Written assignment 100% 

The word count must not exceed 2000 words. The essay must include tables with (1) network’s descriptive statistics, (2) models' parameters and (3) results of goodness of fit tests. 

Feedback available via Turnitin

Essential:  

Borgatti S., Everett M, Johnson J. (2018). Analysing Social Networks 2nd Ed, Sage, London  

Lusher D., Koskinen J., and Robins G. (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press  

Additional:  

Hanneman R.A. and Riddle M. (2005). Introduction to Social Network Analysis. Available at https://faculty.ucr.edu/~hanneman/nettext/  

Robins G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Sage.  

Wasserman S. and Faust K. (1994). Social Network Analysis, Cambridge University Press 

For Information and advice on Link2Lists reading list software, see:  

http://www.library.manchester.ac.  

uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/  

Scheduled activity hours 30 hours (mixed lecture/tutorial format)