Data Wrangling and Social Media Analytics

This course explores advanced modern analytical techniques to extract and understand real-world datasets which are messy. To develop practical knowledge of core concepts and techniques for obtaining meaningful information from real world unstructured data (such as text and social media data) and apply them using modern implementations in R, Python and SAS. In particular, a focus on text and social media data with the use of SAS, a statistical software. A focus will be data wrangling techniques for non-standard, big, messy data: natural language processing, networks and longitudinal data. Analytics tools: to review and select appropriate analytics tools to analyse social media-sourced text and non-text data. Including both SAS and R. Data Wrangling and Data Types. Identify, assess, infer from different data types especially messy data. To be able to analytically analyse and use visualisation tools across a range of different sources including social media data sources, and present outcomes appropriately in support of business intelligence, exploratory data analysis, research or investigation purposes; Web Analytics: analysing social media-sourced text and non-text data to address a business question; and User analytics: interpreting users' interaction with social media sites, such as comments, likes and upvotes, and user activity metadata.