The CDH Social Data School, led by Cambridge Digital Humanities in association with the Minderoo Centre for Technology and Democracy, is an online intensive teaching programme structured around the life-cycle of a digital research project, covering principles of research design, data collection, cleaning and preparation, methods of analysis and visualisation, and data management and preservation practices.
This year’s Data School includes modules exploring the challenges to data protection principles in a world where networked surveillance is fast becoming the norm.
We will also analyse the social and cultural impact of recent advances in machine learning driven systems for classifying and generating images and texts, and discuss and deploy data-intensive methods for the analysis of disinformation on social media platforms.
Key topics covered include:
- Ethical digital research design and the digital project lifecycle
- Data protection and surveillance in a networked world
- How Machine Learning shapes the media
- Data exploration, structuration and preparation
- Social Network visualisation and analysis
- Machine Learning and computer vision: a critical and experimental introduction
The Social Data School takes place entirely online this year.
Delivery methods will include seminar presentations, small group discussions, demonstrations and experimental lab sessions. Participants will be expected to spend time between sessions working on practical exercises in their own time and are encouraged to interact with each other via the course virtual learning environment and social space to support learning.
Software tools and methods covered:
- Openrefine + extensions (such as Named Entity Recognition): data cleaning, filtering and aggregation
- Regex: more efficient filtering
- Voyant tools : text visualisation without programming
- Gephi: social network visualisation and analysis
- Jupyter notebooks: webscraping, adding text as a column
- Google Colab notebooks for automated text generation and image processing
No prior knowledge of programming languages is required.
The objectives of the CDH Data Schools are:
- To democratise access to tools and methods for digital data collection, analysis and reporting;
- To foster the development of ethical practices in digital research; and
- To encourage dialogue between academia, news media, civil society, the public sector and industry about the social, legal, ethical and policy implications of digital research methods
The Data Schools leverage expertise in digital research, providing practical training and knowledge exchange across sectors, professions and disciplines. In past editions, most participants were affiliated with civil society groups, news media organisations and academia.
Preference will be given to individuals from organisations whose access to digital data-driven training is limited due to lack of human or financial resources. In order to stimulate peer learning, the desired composition of the class is diverse, with participants from different fields, and thus selection will be guided by this objective.
About the organisers
Cambridge Digital Humanities is committed to democratising access to digital methods and tools and is offering the following subsidised participation fees to encourage applications from those who do not normally have access to this type of training. The fees include all teaching costs.
- Standard rate: £245
- Small organisations/university staff/staff journalists: £145
- Students/unemployed/community or unfunded projects/freelance journalists/participants resident in the Global South: £45
In addition, a small number of bursaries are available to those who can demonstrate financial need.
While we encourage applications from everyone, we particularly welcome applications from women and black and minority ethnic candidates as they have historically been under-represented in the technology and data science sector.
Because most members of the University of Cambridge are eligible to attend our existing teaching programme, they are not able to apply for the Social Data School.
If you think there is a valid reason for an exception to this rule, please contact email@example.com.