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Session: 2022/23
Last modified: 21/07/2022 16:07:06
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Title of Module: Data Warehouse Environment |
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Code: COMP10002 |
SCQF Level: 10
(Scottish Credit and Qualifications Framework) |
Credit Points: 20 |
ECTS: 10
(European Credit Transfer Scheme) |
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| School: | School of Computing, Engineering and Physical Sciences |
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| Module Co-ordinator: | Carolyn
Begg |
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| Summary of Module |
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This module examines the factors that have led to the emergence and popularity of business intelligence (also known as business analytics or data analytics) and the underlying technologies such as the data warehouse.
This module compares and contrasts the major methodologies for designing the data warehouse such as those proposed by R.Kimball and W.Inmon. The issues associated with each data warehouse methodology are discussed.
This module examines the new and established technologies that can form the data warehouse/BI environment including the warehouse, Online Analytical Processing (OLAP), data mining and dashboards.
This module considers the major players in the BI/DW environment such as SAS, SAP, Microsoft, Oracle and open-source providers such as BIRT (Business Intelligence and Reporting Tools).
This module includes practical classes using a BI tool such as Tableau. Students are exposed to BI scenarios that may require investigation of emerging BI technologies and/or exploration of data sets.
This module explores new ideas and emerging trends associated with the data warehouse/BI environment such as in-memory analytics, self-service BI and sentiment analysis.
- The purpose of this module is to re-visit topics relating to business intelligence/data analytics in more depth that will have been covered in earlier database modules.
- The purpose of the module is to emphasize the increasingly important role played by data and information as a corporate asset and to gain a greater appreciation of the potential to exploit this asset and to understand the necessary technologies.
- This module will work to develop a number of the key 'I am UWS' Graduate Attributes to make those who complete this module: Universal (Critical Thinker, Ethically-minded, Research-minded), Work Ready (Problem-Solver, Effective Communicator, Ambitious) and Successful (Autonomous, Resilient, Driven).
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| Module Delivery Method |
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| Face-To-Face | Blended | Fully Online | HybridC | HybridO | Work-based Learning |
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Face-To-Face
Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.
Blended
A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations
Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.
HybridC
Online with mandatory face-to-face learning on Campus
HybridO
Online with optional face-to-face learning on Campus
Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.
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| Term(s) for Module Delivery |
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(Provided viable student numbers permit).
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| Term 1 | | Term 2 | | Term 3 | |
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| Learning Outcomes: (maximum of 5 statements) |
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On successful completion of this module the student will be able to:
L1.
Demonstrate a critical understanding of the principal theories, concepts and issues associated with the data warehousing environment.
L2.
Demonstrate knowledge that covers and integrates most of the principal methodologies, techniques and tools associated with the data warehousing environment.
L3.
Offer professional level insights, interpretations and solutions to problems and issues associated with the development of a data warehousing environment for a given case study. |
| Employability Skills and Personal Development Planning (PDP) Skills |
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| SCQF Headings |
During completion of this module, there will be an opportunity to achieve
core skills in:
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| Knowledge and Understanding (K and U) |
SCQF Level 10.
Understanding of the current and future significance of the data warehouse environment for modern businesses.
A critical understanding of the main methodologies and techniques associated with the development of the data warehouse environment.
Detailed knowledge and understanding of the technologies that form the data warehouse environment. |
| Practice: Applied Knowledge and Understanding |
SCQF Level 10.
Execute a defined project, which involves an investigation of a company’s requirements for business intelligence and presents an evaluation of how current systems serve the company together with a set of feasible and relevant routes for further development. |
| Generic Cognitive skills |
SCQF Level 10.
Demonstrate some originality and creativity in dealing with professional level issues such as those presented by coursework.
Make recommendations where data/information is limited or comes from a range of sources such as internal company documentation and/or data files. |
| Communication, ICT and Numeracy Skills |
SCQF Level 10.
Communicating effectively and appropriately in writing in the production of a business intelligence evaluation consultancy report. Interpret complex primary materials such as internal company documentation and/or data files. |
| Autonomy, Accountability and Working with others |
SCQF Level 10.
Exercise autonomy and initiative in simulating the role of a professional business intelligence analyst providing services to a real company. |
| Pre-requisites: |
Before undertaking this module the student should have
undertaken the following:
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Module Code:
COMP09003
COMP08002
| Module Title:
Business Intelligence (Comp)
Database Development
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| Other: | Or similar modules |
| Co-requisites | Module Code:
| Module Title:
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* Indicates that module descriptor is not published.
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| Learning and Teaching |
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| This module is mostly taught using the traditional approach of lecturing to groups of students. Lectures will be periodically supplemented with seminars by external speakers to re-visit and illustrate the more complex aspects of the syllabus. Lab (PC)-based classes complement the lectures by providing an environment to encourage the learning of one or more business intelligence/data analytics tools which are becoming increasingly popular and are likely to form part of the data warehouse environment. |
Learning Activities
During completion of this module, the learning activities undertaken to
achieve the module learning outcomes are stated below:
| Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities) |
| Lecture/Core Content Delivery | 20 |
| Tutorial/Synchronous Support Activity | 8 |
| Laboratory/Practical Demonstration/Workshop | 20 |
| Independent Study | 152 |
| 200
Hours Total
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**Indicative Resources: (eg. Core text, journals, internet
access)
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The following materials form essential underpinning for the module content
and ultimately for the learning outcomes:
*Successful Business Intelligence: Unlock the Value of BI & Big Data, 2nd Edition by Cindi Howson (2014) McGraw-Hill Osborne.
Access to a BI tool such as Tableau or SAS Studio.
Internet access to Moodle to allow student access to all teaching material, including slides, labs and coursework.
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(**N.B. Although reading lists should include current publications,
students are advised (particularly for material marked with an asterisk*) to
wait until the start of session for confirmation of the most up-to-date
material)
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| Engagement Requirements |
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In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure |
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Supplemental Information
| Programme Board | Computing |
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| Assessment Results (Pass/Fail) |
No
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| Subject Panel | Business & Applied Computing |
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| Moderator | Frances McCormick |
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| External Examiner | T Gaber |
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| Accreditation Details | This module is accredited by BCS as part of a number of specified programmes. |
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| Version Number | 2.15 |
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| Assessment: (also refer to Assessment Outcomes Grids below) |
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| The first summative assessment is submitted towards the end of module and is in two parts - Part 1 (20%) is submitted in the form of a report that requires research and writing skills and Part 2 (20%) is submitted in the form of a report (and data file) that requires the learning of a BI tool, application of basic data analysis, creation of appropriate visualizations of results and discussion of findings. The complete assignment is worth 40%. |
| Formative assessment is available using on-line practice tests (on Moodle) - that allow students to test their progress and understanding of the syllabus. The summative component of assessment is a class test worth 60% (individual) and this takes place towards the end of the module. |
(N.B. (i) Assessment Outcomes Grids for the module
(one for each component) can be found below which clearly demonstrate how the learning outcomes of the module
will be assessed.
(ii) An indicative schedule listing approximate times
within the academic calendar when assessment is likely to feature will be
provided within the Student Handbook.)
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Assessment Outcome Grids (Footnote A.)
Footnotes
A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above
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Note(s):
- More than one assessment method can be used to assess individual learning outcomes.
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Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.
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| Equality and Diversity |
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Nothing in the module should present difficulties for students on the basis of their gender, ethnicity, or sexual orientation. In relation to students with special needs, when a student discloses a disability the individual module tutor, in consultation with the enabling support co-ordinator, will agree any appropriate adjustments to be made. Students should note that the language of instruction is English and that they will need to have a reasonable grasp of the language in order to keep abreast of the teaching materials and in submitting assessed work.
UWS Equality and Diversity Policy |
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(N.B. Every effort
will be made by the University to accommodate any equality and diversity issues
brought to the attention of the School)
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