7AAVBCS3 Big Data in Practice: Collaboratories, Tools and Methods
Module convenor: Dr Mark Coté
Teaching pattern: Six four-hour workshops
This module will provide students with the opportunity to explore an array of tools, methods and practices in big data. While this module is for non-data scientists, we will hold a series of half-day co-laboratories to give students an opportunity to dig deeper into data through practical methods. This module will be organised around four themes with an introductory and concluding lecture and seminar. The remaining four sessions will be half-day sessions.
Draft teaching syllabus
Syllabus will typically include:
- Theme I: Hacking the Mobile Ecosystem for Data
- Theme II: Big Humanities and Heritage
- Theme III: Social Media and Cultural Analytics
- Theme IV: Data Visualisation of Cities and Space
This module aims to:
- build upon Core Module I, putting theories from media and communication studies, social theory, and the digital humanities into practice
- to develop innovative interdisciplinary practice-based methods of study that cross technological and cultural perspectives
- to run a series of joined lab sessions so students can position big data in the larger context of digital culture and society
- to enable students to develop practices and applications in a collaborative manner as a key skill in the digital industries
Learning outcomes entail
- an advanced and systematic understanding of selected techniques for the practical application of cultural, political, economic and material features of big data
- the ability to assess critically interdisciplinary practices propounded by different scholars and experts on the major issues raised by big data, and to undertake substantial investigation into different methods for working with big data
- the capacity to formulate their own arguments and questions about big data practices in a perspective encompassing broader issues around digital culture and society
- Blanke, T., Greenway, G., Pybus, J. and Coté, M. (2014) Mining Mobile Youth Cultures, 2nd IEEE International Conference on Big Data, Washington
- David Bollier, (2012) The Promise and Peril of Big Data
- Lev Manovich (2013) “The Algorithms of Our Lives,”
- Alexander Pentland, (2012) Reinventing society in the wake of big data
- Adam Perer (2010) “Finding Beautiful Insights in the Chaos of Social Network Visualizations.” In Beautiful Visualization: Looking at Data through the Eyes of Experts
- Farida Vis (2013) A Critical Reflection on Big Data: Considering APIs, Researchers and Tools as Data Makers
- Nathan Yau. (2013) Data Points: Visualization that Means Something
- Nielsen, R. (2013) Hadoop: The Engine That Drives Big Data.
- Rogers, Richard (2012). Digital Methods. Cambridge, MA: MIT Press.
The module is assessed based on a single assignment of 4,000 words (100%). Students can choose whether:
- to write a report on a mini project in order to apply some of the practical skills you learned in the module, or
- to write an essay on a question that relates to the module content. In the essay you are asked to go beyond what you have learned and show that you understand the wider implications of the module material.
The modules run in each academic year are subject to change in line with staff availability and student demand so there is no guarantee every module will run. Module descriptions and information may vary depending between years.