MTHM604 - Tackling Sustainability Challenges using Data and Models (2021)

Back | Download as PDF
MODULE TITLETackling Sustainability Challenges using Data and Models CREDIT VALUE30
MODULE CODEMTHM604 MODULE CONVENERProf Stuart Townley (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

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 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 address 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.

AIMS - intentions of the module
In this module you will develop an interdisciplinary perspective on data science and modelling. Your learning will follow a three-stage cycle of (1) colloquia, followed by (2) group work, followed by (3) presentation or poster to a client and general audience. In more detail: (1) Contemporary, client/expert-led colloquia will introduce a “state of the art” challenge from ecology and conservation, environment and human health, renewable energy and sustainability science; (2) Each colloquium will be followed by break out-sessions with you working in small multi-disciplinary groups, with guidance from the module leader and classroom assistants to further your understanding of data science and modelling; (3) Finally, you will present findings from the group work back to peers/clients for discussion and feedback. Each of these three stages will be repeated five times to provide a balance of applications, data sources, and data science and modelling approaches. You will also gain important experience of planning and carrying out research projects and leading multi-disciplinary groups.
 
The module runs across 11 weeks in 5 times two-week blocks, plus one week. Each two-week block will develop one of the five themes. Each first week comprises a colloquium style lecture to introduce the themes and identify any deliverables if relevant, followed by supervised group work and formative assessment. Each second week involves further group and individual work, consolidation and preparation of summative assessment. During the intensive group work sessions, students will take turns in leading the activity of their group. It is recommended that group membership will vary over the course of the module. Mid-way through this first week, the group lead will make an informal executive overview of work carried out by their team. At the end of this first week the team will also make a short group presentation of progress (formative/non-assessed). The group lead will provide an executive report at the end of this first week (assessed, 20%). In the second week students will prepare a group presentation on the theme (assessed, 40% for overall module group work). In week 11, students will prepare an individual report based on the work from one or more of the themes (assessed, 40%).
 
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

Module Specific Skills and Knowledge:

1

Use data to build an understanding of a variety of problems and challenges;

2

Work together in multi-disciplinary teams to produce balanced and informed reports on various problems and challenges;

Discipline Specific Skills and Knowledge:

3

Identify the appropriate data sciences and modelling approach(es) and source suitable data for a given problem or challenge;

4

Use a variety of data science and modelling approaches to address problems and challenges;

Personal and Key Transferable/ Employment Skills and Knowledge:

5

Communicate data-informed solutions to a wide range of clients and end-users;

6

Plan complex, multi-disciplinary tasks involving accessing complex data sets, storing data, version control of models and codes, and preparation of professional reports;

7

Lead a multi-disciplinary team to harness a wide range of skills and expertise.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

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.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 55.00 Guided Independent Study 245.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category

Hours of study time

Description

Scheduled Learning and Teaching Activities

10

Expert/client led colloquium/lectures

Scheduled Learning and Teaching Activities

30

Supervised group work

Scheduled Learning and Teaching Activities

5

Student presentations to a mixed audience of peers and experts

Scheduled Learning and Teaching Activities

10

Supervised individual work

Guided Independent Study

245

Reading, computer lab based activity, report writing, preparation for presentations

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment

Size of the assessment e.g. duration/length

ILOs assessed

Feedback method

Informal group presentation and executive summary

5 x 5 minutes – within the guided project work

1-7

Oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment

 

% of credit

Size of the assessment e.g. duration/length

ILOs assessed

Feedback method

Group Presentations

40

5 x 10 minutes (or equivalent)

1-5

Oral/Written

Executive Report by group lead

20

10 minutes (or equivalent)

6, 7

Oral

Individual Report

40

3000 words (or equivalent)

1-6

Written


 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original form of assessment

Form of re-assessment

ILOs re-assessed

Time scale for re-assessment

Any

Coursework (100%)

All

Individual Report (5000 words or equivalent)

 

RE-ASSESSMENT NOTES

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%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

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:

There are currently no reading list entries found for this module.

CREDIT VALUE 30 ECTS VALUE 15
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 14 December 2020 LAST REVISION DATE Friday 18 June 2021
KEY WORDS SEARCH Interdisciplinarity; Data science; Computational Modelling; Ecology; Renewable Energy; Environmental Science; Expert-led learning; Grand Challenges