Tackling Sustainability Challenges using Data and Models - 2024 entry

This module is central to the ethos of applied data science. It puts data science and modelling at the heart of tackling the major global challenges faced by modern society. The United Nations sustainable development goals of, for example, “zero hunger”, “clean water and sanitation” and “affordable clean energy” all involve massive data sets and complex modelling. More immediate concerns such as environmental emergencies and global pandemics are shining an international spotlight on “big data” and the important work of data scientists. This module aims to equip students for the digital workplace. It is all about making an impact on the contemporary big sustainability problems using multiple, inter-woven approaches. This will be realised by working with researchers, businesses, policy makers, app developers, NGOs and industry as “clients”. Collaborating in teams with expertise from multiple disciplines and developing highly desirable team-working skills, students will work on mini-projects that address the questions and issues raised by these clients and stake holders . Using locally curated and global, open sourced data, teams will develop data analytics, models and solutions for their clients. Students will communicate their findings in reports, posters, and through digital media.

The syllabus is developed around five colloquia. These colloquia are delivered by clients/experts from the engineering, environmental, human health and life sciences, or business/industry/partner organisations. The exact details of each colloquium will vary from year to year because one key aim is to keep abreast of contemporary issues from a data science and modelling perspective. These colloquia will be representative of the scope of the engineering, environmental, human health and life sciences. Each colloquia will then be followed by multi-disciplinary team work. By working in multi-disciplinary teams, you will understand the importance of data, data science and modelling in coming to informed responses to various challenges.

Sample themes for purposes of illustration:

1. Using Data and Models in Predictive Healthcare Technology (e.g. Deep Learning Acute Kidney Injury prediction from NHS data, Malaria/Dengue modelling for World Health Organisation treatment recommendations);
2. Textual Data and AI (e.g. towards a digital consultant psychiatrist – a.k.a Chat Bot);
3. Integrating Renewable Energies using data across multiple time and spatial scales;
4. Geographical Information Systems and Ecosystem Services;
5. Micro-climates and Climate Change Mitigation/Adaptation;
6. Social Housing – Cluster analysis of behavioural patterns in use of domestic appliances;
7. Agri-tech. Using Big Data to deliver sustainable food.

Deferral – if you miss an individual assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment. If you miss a group work assessment for certificated reasons judged acceptable by the Mitigation Committee, you will complete an individual assignment pro-rata (percentage according to the missed component).

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to resubmit the original assessment as necessary. The mark given for a re-assessment taken as a result of referral will be capped at 50%.

Basic reading:

• There is no set reading list. The colloquium lead will distribute relevant materials at the beginning of each theme.

Web-based and electronic resources:

• ELE – College to provide hyperlink to appropriate pages

Other resources:

• N.A.

Reading list for this module: