Advanced Social Data Science

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Develop R code and apply this to answer a research question
• Select and apply appropriate machine learning methods for predictive tasks.
• Use and implement innovative methods to collect web based social data
• Understand ethical issues around new forms of data collection and analysis
• Appropriately use a range of different methods of analysis to answer a research question which uses social data

The content will include:

• Collecting data from the web, possibly including the use of APIs, scraping, and potentially social media
• Ethics of collecting data from new sources
• Key concepts and techniques in machine learning

oThe distinction between supervised vs unsupervised learning

oThe use of cross-validation for model selection

• Methods for the analysis of text data
• Analysis (initial ideas of potential topics)

oCoding using R

oAnalysis of SQL databases using R

oMachine Learning Text Analysis

oData visualisation

Teaching and learning methods

This module will be taught using a blended learning approach, with flipped learning for the main lectures. Videos on each method will be recorded and placed online for students to watch before the lectures. There will be one full group lecture each week, to revise the method for that week and to conduct interactive questions to gauge understanding.

There will also be a computer workshop each week in order to apply the methods and to learn the selected computer programme.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.