Module Database Search



MODULE DESCRIPTOR
Module Title
Fundamentals of Data Warehousing
Reference CMM721 Version 3
Created February 2024 SCQF Level SCQF 11
Approved April 2017 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To provide key concepts and principles of databases, data capture, cleansing and preparation for business analytics, data warehousing techniques and to develop specialised knowledge in areas that demonstrate the interaction and synergy between research and practice of distributed large-scale data stores.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Make informed judgements on data warehousing design principles, practices and standards.
2 Criticise methodologies that are at the forefront in data warehousing design and development.
3 Prepare an appropriate Extract Transform and Load (ETL/ELT) process, using a state-of-the-art tool for a given business requirement.
4 Generate a data warehousing solution by adopting and extending methods that are informed by current research and industry practices.

Indicative Module Content
Database concepts: ER Diagrams, Normalization. Data processing: Data Capture, data cleaning, data conformation, data integration, data federation and data virtualisation. Introduction to data warehousing: benefits of data warehousing and types of data warehousing including conventional, spatial and temporal data warehouses. Data warehousing concepts: Architecture of a data warehouse including bus architecture and Corporate Information Factory architecture (CIF). Data warehousing and design: Data warehouse design using Dimensional Model, Star Schema, Snowflake Schema. Slowly Changing Dimensions (SCD).

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 student will progress through a sequence of exercises to develop sufficient knowledge and skills in artificial intelligence.

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 submission worth 100% of total module assessment.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in Component 1.
B The student needs to achieve a B in Component 1.
C The student needs to achieve a C in Component 1.
D The student needs to achieve a D in Component 1.
E The student needs to achieve an E in Component 1.
F The student needs to achieve an F in Component 1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 CONOLLY, T. and BEGG, C., 2015. Database Systems: A Practical Approach to Design, Implementation and Management. Pearsons.
2 KIMBALL, R. and ROSS, M., 2013. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 3rd ed. John Wiley & Sons Inc.
3 SILVERS, F., 2008. Building and Maintaining a Data Warehouse. CRC Press.
4 KIMBALL R. and ROSS M., 2008. The Data Warehouse Lifecycle Toolkit. Wiley.
5 INMON, W.H., STRAUSS D. and NEUSHLOSS G., 2008. DW 2.0: The Architecture for the Next Generation of Data Warehousing (Morgan Kaufman Series in Data Management Systems). Morgan Kaufmann.

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