Data Science teaching and assessment guide

wanderooswarrenAI and Robotics

Nov 21, 2013 (3 years and 4 months ago)

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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.


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 Inte
lligence: 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; simulat
ion; 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.

Impleme
nt 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 neces
sary. 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:





100%


Learning Outcomes
demonstrated:




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 meth
ods which enable student to
demonstrate the learning outcomes for the Module:


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 equivale
nce).

Weighting:





100%


Learning Outcomes
demonstrated:




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 dat
a 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 qualitat
ive 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 o
f 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 fo
r 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:


Coursework exercise to conduct a qualitative investigation
leading to production of a survey data collection
instrument.


Coursework exercise to design, present and defend a
formal professional analysis of narratives concerning a
current public is
sue

Weighting:



50%



50%



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, competiti
on, life cycle, cross
-
fertilisation and the niche space.


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 su
pport



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 refle
ctive discussion. An individually written report
reinforces the
workshops and allows deeper exploration of the
subject matter
.

The teaching w
ill include a number of real data analysis case studies from research and
consultancy projects.

Assessment methods which enable student to
demonstrate the learning outcomes for the Module:


A written report of

approx. 5000 words

describing and
analysing t
he data ecology in an application area of the
student’s choosing.

Weighting:




100%


Learning
Outcomes
demonstrated:


All







Module Title:


Spatial Data Analysis

Pre
-
requisite:

None

Pre
-
cursor:
None

Co
-
requisite:

None

Excluded combinations:
None

Is this module part of the Skills
Curriculum?
No

University
-
wide option:
Yes

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.


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



S
patial 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

th
e 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 usin
g as appropriate, case studies
and
journal articles. This module will require students to actively participate in class discussions
and will em
phasise practical approaches
.

Assessment methods which enable student to
demonstrate the learning outcomes for the Module:



Coursework: portfolio of completed practical exercise as a
written report.

Essay: critical evaluation of an application area (approx.
2,5
00

words)


Weighting:




50
%


5
0%

Learning
Outcomes
demonstrated:


1,2,3,5,6,8


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 decision
-
making 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
-
ma
king



Mathematics and statistical foundations of decision
-
making



Use of computer based tools in decision
-
making



Analysis of case studies



Probabilities of uncertain events



Utilities vs. consequences



Maximisation models of expected utility

Learning Outcomes
for the Module

At the end of this Module, students will be able to:


Knowledge
and t
hinking skills

1.

U
nder
stand

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 u
se 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.



Self directed negotiated learning will be used in preparation for lectures/seminars and
for carrying out the coursework
.

Assessment me
thods 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:




100%



Learning
Outcomes
demonstrated:


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
Curriculum?
No

University
-
wide option:
Yes

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.


Main Topics of Study:



Evaluation of work
-
based projects



Evidence gathering in organisational settings



Use of
appropriate
techniques and
tools
for
monitor
ing,

analysis, simulation and
decision
-
support



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



Online supervision



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:




100%



Learning
Outcomes
demonstrated:


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:




The data science research agenda



Data science within
professional R&D agenda



Research design: methods, evidence, validity



Communicating a rationale for a research topic



Resourcing research

Learning Outcomes for the Module

At the end of this Module, students will be able to:


Knowledge and thinking skills


1.

U
nderstand 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.

Commu
nicate 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



Online supervision



Interactive website support

Assessment methods which enable student to
demonstrate the
learning outcomes for the Module:


A

research

plan
(approx. 5,000 words)
using
F
orm

REG
available from
http://www.uel.ac.uk/qa/pgr/index.htm



Weighting:




100%


Learning
Outcomes
demonstrated:


All