Module Database Search



MODULE DESCRIPTOR
Module Title
Data Warehousing
Reference CMM701 Version 5
Created February 2024 SCQF Level SCQF 11
Approved May 2016 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To provide key concepts and principles of data warehousing techniques and to develop specialised knowledge in areas that demonstrate the interaction and synergy between research and practices of distributed large-scale data stores.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Appraise data warehousing design principles, practices and standards.
2 Evaluate methodologies that are at the forefront in data warehousing design and development.
3 Design a multidimensional analysis of business data to interpret results from complex trend analysis using state-of-the-art modelling algorithms.
4 Create a data warehousing solution by adopting and extending methods that are informed by current research and industry practices.

Indicative Module Content
Data Capture, data cleaning, data conformation, data integration, data federation and data virtualisation. Concepts and benefits associated with data warehousing. Conventional, spatial and temporal data warehouses. Architecture of a data warehouse. Data warehouse design. Tools for Data warehousing. State of the art in data warehousing, including data warehousing in the cloud. Data warehousing with big data Case studies.

Module Delivery
Key concepts are introduced and illustrated through lectures and directed reading. The understanding of students is tested and further enhanced through interactive tutorials. In the laboratories, the students will progress through a sequence of exercises to further their understanding and gain practical experience in data warehousing. For on-campus learners, teaching and learning will be facilitated hands-on at lecture halls and labs. For online learners teaching and learning will be facilitated in real-time via virtual classrooms using voice and video, collaborative tools, and remote assistance tools.

Indicative Student Workload Full Time Part Time
Contact Hours N/A 48
Non-Contact Hours N/A 102
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL N/A 150
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4
Description: Coursework involving the development of a data warehousing application and defence.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighing of C1. An overall minimum grade D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1.
B The student needs to achieve a B in C1.
C The student needs to achieve a C in C1.
D The student needs to achieve a D in C1.
E The student needs to achieve an E in C1.
F The student needs to achieve an F in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None except for course entry requirements.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 CONNOLLY, T. and BEGG, C., 2015. Database Systems: A Practical Approach to Design, Implementation and Management. Pearsons.
2 KIMBALL, R., ROSS, M., 2013. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition). John Wiley & Sons, Inc.
3 MOHANTI, S., 2013. Big Data imperatives- enterprise big data warehouse, BI implementations and analytics. Apress.
4 SILVERS, F., 2008. Building and maintaining a data warehouse. CRC Press.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781