Download 2012/2013 Programme Specification Data Programme Name Data

2012/2013 Programme Specification Data
Programme Name
Data Warehousing and Data Mining
Programme Number
Programme Award
P10967
MSc
QAA Subject Benchmark
Statements
n/a
Programme Aims
•
Programme Learning
Outcomes: Knowledge and
To provide graduates in computing (or closely related
subjects) with in-depth knowledge, advanced skills and
understanding in the areas of Data Warehousing and Data
Mining and a range of techniques, conceptual models and
tools to develop into professionals in the areas of ‘Data,
Information and Knowledge Management’, ‘Knowledge
Discovery’, ‘Business Intelligence and Decision Support
Systems’, potentially working in a wide range of application
areas.
• To provide students with in-depth knowledge advanced skills
and understanding of Distributed Data Systems, their
structure, functionalities and the middleware that holds them
together: these are required to underpin the learning in the
Data Warehousing area, and to ensure high level
professional standards across the whole range of data and
information management technologies.
• To provide underpinning knowledge understanding and skills
in the areas of probability, statistics and machine learning
algorithms which underpin the Knowledge Discovery
enterprise.
• To provide students with high-level operational skills in the
use of state-of the art software for KD/DM and DW/DSS,
based on understanding of basic principles and the use of
real-world case studies.
• To provide students with independent exploratory and
research skills, linked with abilities to synthesise, integrate
and critically analyse and compare all features of the
KD/BI/DW area.
To provide a choice of specialist options which will either expand
the bounds of the programme, provide supporting techniques,
tools or concepts, or supply a relevant application field.
A Knowledge and understanding of:
Understanding
1. Design and security issues and architectures and network
technologies for building, deploying and managing data
warehouses, data mining, data visualisation and decision
support computing systems
2. Distributed data management and practice for modern
computer systems
3. Data mining technologies for modern computer systems
4. Advanced modelling techniques for building modern
computer systems involving Data Warehouses.
5. Business, industrial and commercial context of building
data warehouses and data mining software systems
6. Social, ethical, legal and professional issues.
Programme Learning
Outcomes: Intellectual Skills
Programme Learning
Outcomes: Subject Practical
Skills
Programme Learning
Outcomes: Transferable/Key
Skills
B Intellectual skills:
1. Synthesis of information from a variety of different sources
2. Discuss the issues surrounding the Integration of theory
and practice
3. Plan, conduct and write up an academic piece of research
4. Design data mining and data warehousing systems and
solutions to meet user requirements
C Subject Practical skills:
1. Critically evaluate new and emerging technologies in terms
of their suitability for BI and DW software development
purposes.
2. Analyse, design, build and deploy data warehousing
systems using a variety of current application technologies
and architectures
3. Evaluate and select appropriate technologies and tools for
building and deploying modern computer systems
4. Manage the data mining development process in an
individual or team context
5. Plan, design and deploy the necessary data mining
technologies to support a software system
6. Design, build and manage Business Intelligence computer
and communications systems fit for a given business
purpose.
D Transferable/ key skills:
1. Abstract essential information from unstructured sources to
understand the technology at a sufficient level to be able to
keep themselves up to date and converse with computing
professionals
2. Demonstrate research skills and make effective use of online and written documentation,
Programme Learning
Outcomes: Graduate
Attributes
Teaching and Learning
Methods
3. Effective communication and group working skills.
n/a
A Teaching and learning:
Knowledge acquisition is acquired through a combination of
formal lecture presentation, classroom problem solving
sessions, and guided laboratory work. This will be supported by
self-study using other materials and the use of on-line
resources. Students will be encouraged to explore other
information sources and documentation than those provided as
part of their substantial case-study based course-works.
B Teaching and learning:
Intellectual skills are developed first by example through
lectures and tutorials involving class discussion, and then
through both practical and theoretical/research-based
coursework, laboratory sessions, and on-line interaction, for all
courses including the project.
C Teaching and learning:
These skills are developed gradually through the course. Each
week students will be given practical exercises, individually or
in teams, to help them build both skills and confidence. These
will range from simple exercises through to more complex
activities that will require the application of a range of
technologies, tools and techniques to develop a data
warehouse which is suitable for purpose, and a data mining
process which reveals information and knowledge relevant to
the business aims or questions.
D Teaching and learning:
Throughout the programme students will be required to
participate in group discussions and work collaboratively with
their peers and individually. Research skills are developed
through both the taught courses and the project. Because of
the rapidly changing nature of the subject, students will be
continually made aware of the need to update their knowledge
and will practice this regularly through laboratory exercises.
Assessment Methods
A Assessment Methods:
The assessment methods are defined in each course
description. Each course will use a combination of coursework
and examination. The project will be assessed as an individual
piece of work.
B Assessment Methods:
Intellectual skills are assessed by formal examinations as well as
the coursework and as the final outcomes of the project.
C Assessment Methods:
Some of the weekly exercises will contribute to assessment. In
addition, students will undertake other assignments that will allow
them to integrate the skills they have acquired. They will be
required to assess and report on the success of their solutions.
D Assessment Methods:
Laboratory sessions will be monitored by the tutor and students
will be required to reflect on collaborative processes as well as
content of their work. A number of assignments will require
students to effectively undertake research, but it will be
predominantly assessed through the project.