Module Title:
Applied Research Tools and Techniques
Main Aim(s) of the Module:
This module aims to develop an understanding of a range of advanced tools and techniques
relevant to doctoral research in the Technology area. The relevant tool and techniques that a
student will focus on (depending on their research topic) will be stipulated through an Individual
Learning Agreement.
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Topics of Study available:
Mathematics: combinatorial problems; cryptography; time-series.
Statistics: exploratory data analysis (EDA); parametric and non-parametric significance
testing, including analysis of variance (ANOVA); factor analysis; cluster analysis; linear
and logistic multiple regression; meta-analysis.
Artificial Intelligence: neural networks; agent technologies; fuzzy sets, numbers & logic.
Data mining: memory-based reasoning (MBR); cluster detection; link analysis; decision
trees; regression modelling; neural networks; clickstream analysis.
Dynamic modelling; simulation; sensitivity analysis.
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Understand and critically apply a selection of advance techniques relevant to the student’s
topic of research.
2. Interpret the results of using such techniques.
3. Critically evaluate the work of others using such techniques as reported in the literature.
Subject-based practical skills
4. Competently use the relevant software tools in a research context.
5. Implement research designs that require advanced tools and techniques.
Skills for life and work (general skills)
6. Critically evaluate published research reporting the use of advanced tools.
7. Apply appropriate suites of tools in problem solving.
Teaching/ learning methods/strategies used to enable the achievement of learning
outcomes:
Lectures and seminars will focus on the theoretical issues, developing key skills and on reflection.
Workshops will be for practical instruction of software tools where necessary. Self directed
learning will be in preparation for lectures/seminars and for carrying out the coursework. Topics
will be offered in blocks to suit students’ Individual Learning Agreements.
Assessment methods which enable student to
demonstrate the learning outcomes for the
Module:
Coursework
Individual Learning Agreement to apply one or more
tools/techniques in an investigative context (5000
words equivalence)
Weighting:
Learning Outcomes
demonstrated:
100%
All
Module Title:
Research Methods for Technologists – the doctoral process
Main Aim(s) of the Module:
This module aims to develop a deep understanding of how to plan and carry out doctoral level
research. Particular focus will be on identifying and critically justifying a suitable topic and
research design with reference to the existing corpus of research and its boundaries.
Main Topics of Study:
 Knowledge production through research.
 Preparing a proposal for doctoral research: focus, justification, design.
 Systematic reviews: qualitative meta synthesis, quantitative meta analysis.
 Digital technologies and research; experimentation in silico.
 Quantitative, qualitative and mixed methods.
 Structure, evidence and validity in research.
 Working with resources: avoiding plagiarism.
 Legal issues in conducting research: ethics, data protection, health and safety.
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Have a deep understanding of and apply critical thinking skills to a researchable topic.
2. Critically evaluate relevant methodologies.
3. Understand and apply techniques of systematic review.
Subject-based practical skills
4. Critically justify, with reference to the relevant literature:
 a topic of doctoral research that represents a significant knowledge gap;
 a set of research questions/hypotheses;
 an appropriate research design.
5. Critically evaluate alternative approaches to undertaking this investigation.
Skills for life and work (general skills)
6. Conduct structured searches for existing research outputs.
7. Critically evaluate and summarise published research.
8. Be able to initiate doctoral level research.
Teaching/ learning methods/strategies used to enable the achievement of learning
outcomes:
Lectures and seminars will focus on the theoretical issues, developing key skills and on reflection.
Self directed learning will be in preparation for lectures/seminars and for carrying out the
coursework.
Assessment methods which enable student to
Weighting: Learning Outcomes
demonstrate the learning outcomes for the Module:
demonstrated:
Coursework
An initial, exploratory systematic review of the literature of
a selected research topic using on-line databases and
other appropriate resources (5,000 words equivalence).
100%
All
Module Title:
Creating and Analysing Qualitative Data
Main Aim(s) of the Module:
To ensure that all students have an understanding of:
 When and why it is appropriate to collect data qualitatively
 Issues with qualitative methods
 Pros and cons of different qualitative data collection approaches
 Uses of qualitative research
 How to design, plan and manage qualitative data collection
 How to design and evaluate a range of qualitative data collection instruments
 Methods of analysing qualitative data
Main Topics of Study:
 The history of arguments for qualitative approaches to data collection
 The role of qualitative procedures in all forms of research
 Different methods for collecting data collectively
 Evaluating which qualitative methods to use
 How to plan a qualitative data collection protocol
 Constructing valid qualitative data collection tools and procedures
 Choices of methods for analysing survey data
 Computerised analysis of qualitative data
 How to judge and critically appraise research findings based on qualitative data surveys
 How to communicate qualitative research findings
Learning Outcomes for the module
At the end of this module, students will have: developed:
Knowledge of how:
1. The arguments for and against qualitative methods in research have developed
2. To design and manage qualitative data collection research effectively
3. To conduct such research ethically
Thinking skills
4. To evaluate design choices
5. To assess the quality of published qualitative research
6. To interpret qualitative research outputs
Subject-based practical skills
7. To design qualitative protocols
8. To design qualitative data collection instruments
9. To undertake basic qualitative data analysis using various media for analysis
10. To present qualitativee data analysis professionally
Skills for life and work (general skills)
11. To undertake project management
12. To work effectively in a team setting
13. To produce high quality documentation
Teaching/ learning methods/strategies used to enable the achievement of learning
outcomes:
 Interactive lectures
 Seminars
 Practical design and analysis workshops
 Tutorials
 Private study
 Formative assessment
Assessment methods which enable student to
demonstrate the learning outcomes for the Module:
Weighting:
Coursework exercise to conduct a qualitative investigation
leading to production of a survey data collection
instrument.
50%
50%
Coursework exercise to design, present and defend a
formal professional analysis of narratives concerning a
current public issue
Learning
Outcomes
demonstrated:
1,4,,6,7,8,
9,10,11,12, 13
1, 2,
3,4,5,7,11,12,13
Module Title:
Data Ecology
Main Aim(s) of the Module:
This module aims to develop a critical understanding of data from an ‘ecological’ perspective.
This will focus on an understanding the environment of production, dissemination, harvesting
and use of data in the data value chain as well as the development of niche areas from a
perspective of evolution, competition, life cycle, cross-fertilisation and the niche space.
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Main Topics of Study:
Understanding data from an ecological perspective
Elements of the data value chain: from data acquisition, data management, data analysis
to data visualisation and decision support
Data quality and metadata issues
Technological impacts
Organisational impacts
Data and society
Data and the environment; carbon footprint
Legal and security issues of data, personal data and privacy
Big data, open data and transparency agendas
Business models
Location-based Services and App-based applications
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Understand the concept of the data value chain and its components
2. Understand the nature, key issues and dependencies within the data ecology
3. Critically evaluate data application areas from a data ecological perspective
4. Understand the complex symbiosis of data with government, business and society
Subject-based practical skills
5. Specify the value chain and map out the ecological components and interactions of
data application areas
6. Evaluate the quality of data sets and create metadata
7. Estimate the carbon footprint of data centres and specific data applications
Skills for life and work (general skills)
8. Critically evaluate published research and reporting of data issues
9. Apply problem solving
A combination of the following teaching/ learning methods/strategies will be used to
enable the achievement of learning outcomes:
Predominantly delivered through themed workshops incorporating components of lecture,
practical exercises and reflective discussion. An individually written report reinforces the
workshops and allows deeper exploration of the subject matter.
The teaching will include a number of real data analysis case studies from research and
consultancy projects.
Assessment methods which enable student to
Weighting: Learning
demonstrate the learning outcomes for the Module:
Outcomes
demonstrated:
A written report of approx. 5000 words describing and
analysing the data ecology in an application area of the
100%
All
student’s choosing.
Module Title:
Spatial Data Analysis
Pre-requisite: None
Pre-cursor: None
Co-requisite: None
Excluded combinations: None
Is this module part of the Skills
University-wide option: Yes
Curriculum? No
Location of delivery: UEL
Main Aim(s) of the Module:
This module aims for students to understand the concept and theory of spatial data analysis,
and develop the skill and problem-solving ability by applying a range of spatial analysis
techniques. Both proprietary and open source software will be used.
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Main Topics of Study:
The concept of spatial data structure
Spatial operations such as technology of buffering, overlay and spatial query
Introduction of GIScience and the GIS software functionality
Models: Boolean logic, fuzzy logic, Bayesian methods
Spatial analysis of point events data
Spatial analysis of network data
Spatial analysis of area and tessellation data
Issues in spatial analysis: data quality, modifiable areal units, spatial autocorrelation,
spatial regression
Geosimulation modelling
Visualisation and spatial decision support
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Understand the concept and theoretical knowledge of spatial data analysis
2. Develop the problem-solving ability for spatial phenomenon
3. Interpret the results of spatial analysis
4. Critically evaluate different approaches and solutions using knowledge learnt.
Subject-based practical skills
5. Understand and critically apply a selection of techniques for analysing spatial data
6. Competently use the GIS software tools and relevant spatial analysis techniques
7. Able to design a technical solution for spatial analysis applications
Skills for life and work (general skills)
8. Apply spatial reasoning skills for a range of data projects
9. Critically evaluate spatial data and analysis results for decision making
A combination of the following teaching/ learning methods/strategies will be used to
enable the achievement of learning outcomes:
This module will be delivered through a series of lectures and laboratory sessions. Lectures
will deliver the theoretical aspects of the module using as appropriate, case studies and
journal articles. This module will require students to actively participate in class discussions
and will emphasise practical approaches.
Assessment methods which enable student to
demonstrate the learning outcomes for the Module:
Weighting:
Learning
Outcomes
demonstrated:
Coursework: portfolio of completed practical exercise as a
written report.
Essay: critical evaluation of an application area (approx.
2,500 words)
50%
1,2,3,5,6,8
50%
All
Module Title:
Advanced Decision Making
Main Aim(s) of the Module:
This module aims to develop a deep understanding of ways of making decisions that are
based strongly on data and information. Particular focus will be on mathematical decisionmaking models including some use of computer-based support. Various cases will be
examined most of which will be business organisation centred.
Main Topics of Study:
 Models used in decision-making
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Mathematics and statistical foundations of decision-making
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Use of computer based tools in decision-making
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Analysis of case studies
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Probabilities of uncertain events
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Utilities vs. consequences
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Maximisation models of expected utility
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Understand at depth mathematical logic based decision-making.
2. Design decision-making models
3. Assign probabilities to uncertain events; assigning utilities to possible consequences;
and making decisions that maximize expected utility
Subject-based practical skills
4. Make appropriate use of software-based decision making tools
5. Critically evaluate alternative decision models.
Skills for life and work (general skills)
6. Conduct decision-making exercises.
7. Critically evaluate and analyse data.
8. Compose decision-making based reports.
A combination of the following teaching/ learning methods/strategies will be used to
enable the achievement of learning outcomes:
 Lectures and seminars will focus on the analysis of data and information, designing
and using models to make decisions.
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Self directed negotiated learning will be used in preparation for lectures/seminars and
for carrying out the coursework.
Assessment methods which enable student to
demonstrate the learning outcomes for the Module:
Coursework
Utilise a negotiated case study and related problem to
solve a particular decision-making challenge (5,000 words
equivalence).
Weighting:
Learning
Outcomes
demonstrated:
100%
All
Module Title:
Work-based Project Review
Pre-requisite: None
Pre-cursor: DSM001
Co-requisite: None
Excluded combinations: None
Is this module part of the Skills
University-wide option: Yes
Curriculum? No
Location of delivery: UEL
Main Aim(s) of the Module:
This module aims to provide students the opportunity to apply new knowledge and skills to
critically evaluate, from a Data Science perspective, current and/or past work-based
project(s) – preferably with which the student has been associated – within the context of the
literature and best practice and to suggest potential research questions.
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Main Topics of Study:
Evaluation of work-based projects
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Evidence gathering in organisational settings
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Use of appropriate techniques and tools for monitoring, analysis, simulation and
decision-support
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Identifying best practice; making actionable recommendations
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Professionally control and monitor their work-based projects
2. Formally evaluate the degree of success of work-based projects and the scope for
improvement
3. Produce sophisticated decision-making based around work-based situations
4. Situate work-based projects within the research literature
Subject-based practical skills
5. Iteratively improve their work through evaluation and recognition of best practice
6. Produce professional evaluation documents for work-based projects
7. Measure the success of projects
Skills for life and work (general skills)
8. Monitor and control work-based situations as they arise
9. Produce refined communication and forward thinking within a work-based setting
10. Manage, using a range of techniques and tools
A combination of the following teaching/ learning methods/strategies will be used to
enable the achievement of learning outcomes:
 On-campus lectures at the commencement of the module
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Online supervision
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Interactive website support
Assessment methods which enable student to
demonstrate the learning outcomes for the Module:
One assignment: – A report of the completed critical
evaluation of work-based project(s). (approx. 5000 words)
Weighting:
Learning
Outcomes
demonstrated:
100%
All
Module Title:
Planning for Doctoral Research
Main Aim(s) of the Module:
This module aims to develop a critical understanding of how to develop a plan for doctoral
research including focusing on a topic, developing and justifying research questions and
appropriate methodologies. This progresses on from SDD002 and aims to put this learning
into practice as the final taught stage to undertaking doctoral research and writing a thesis.
Main Topics of Study:
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The data science research agenda
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Data science within professional R&D agenda
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Research design: methods, evidence, validity
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Communicating a rationale for a research topic
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Resourcing research
Learning Outcomes for the Module
At the end of this Module, students will be able to:
Knowledge and thinking skills
1. Understand the data science research agenda, its fluidity and the interplay with
professional R&D agenda.
2. Understand complex issues around research design and knowledge production
3. Critically evaluate the relevant literature to develop justification for research questions
and methods
Subject-based practical skills
4. Plan and design a programme of doctoral research
5. Carry out a focused literature review in order to justify the research questions and
methods proposed
6. Identify pertinent resourcing issues
7. Communicate plans for research
Skills for life and work (general skills)
8. Planning and scheduling of projects
9. Evaluate and justify competing approaches and methods for research
10. Write research and project proposals
A combination of the following teaching/ learning methods/strategies will be used to
enable the achievement of learning outcomes:
 On-campus lectures at the commencement of the module
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Online supervision
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Interactive website support
Assessment methods which enable student to
demonstrate the learning outcomes for the Module:
A research plan (approx. 5,000 words) using Form REG
available from http://www.uel.ac.uk/qa/pgr/index.htm
Weighting:
Learning
Outcomes
demonstrated:
100%
All