MSc Data Science (Applied Urban Analytics)

This module is a combination of technical and non-technical topics all related to critical externalities to the data analytics process. The primary aim of the module is to demonstrate that data science cannot be carried out in a vacuum that a whole range of extrinsic considerations affect our ability to carry out the research that we wish to carry out. However, appropriate management of these externalities can lead to higher quality as well more responsible research.

The unit aims to:

• Develop a basic understanding of the technical processes of anonymisation, disclosure control and data linkage.
• Develop an awareness of the issues around the use of data in research.
• Develop fundamental skills in data husbandry.

Students should be able to:

• Understand the ethical issues surrounding the use of data in research.
• Understand the concepts and technical vocabulary of anonymisation and statistical disclosure.
• Demonstrate a basic understanding of data provenance
• Be able to prepare a dataset for analysis
• Make informed decisions about linkage/integration of data and carry out a basic data linkage.
• Conduct a basic anonymisation process with a dataset.
• Identify an appropriate collection of data sources for a project and to identify the issues in using those data sources.

Lectures will introduce specific ideas in relation to data management, the ethics and disclosure of data and linkage in relation to research. Interactive exercises will involve a mixture of solo and group work. Laptop based practicals will allow the students to apply those ideas and to manage data and be able to make informed decisions about linkage/integration of data and to apply anonymisation processes to data.

• Exercise (500 words, 10%)
• Group presentation of analysis plan (11%)
• Report (1,000 words, 44%)
• Essay (1,000 words, 35%)

Christen, P. (2012). Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection. Springer Science & Business Media

Elliot, M., Mackey, E., O'Hara, K., & Tudor, C. (2020). The Anonymisation Decision-Making Framework: 2nd edition - UKAN publications; Manchester.

García S., Luengo, J., & Herrera F. (2015). Data preprocessing in data mining. Springer

Moreau, L., & Groth, P. (2013) Provenance: An Introduction to PROV. Available at https://tinyurl.com/PROV-BOOK [accessed 25/9/2019]