Module Database Search



MODULE DESCRIPTOR
Module Title
Data Warehousing
Reference CMM531 Version 5
Created January 2023 SCQF Level SCQF 11
Approved April 2015 SCQF Points 15
Amended June 2023 ECTS Points 7.5

Aims of Module
To introduce the main concepts and key components of business intelligence and data warehousing techniques and applications.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Critically evaluate the main components of a business intelligence solution.
2 Apply a methodology for designing a business intelligence solution.
3 Analyse the key techniques of data warehousing applications and OLAP.
4 Design, implement and evaluate a data warehousing application.

Indicative Module Content
Business Intelligence systems and types of decisions managers face. Data Visualisation and Dashboard Techniques. 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. In the laboratories the students will progress through a sequence of exercises to further their understanding and gain practical experience of business intelligence and data warehousing techniques and tools.

Indicative Student Workload Full Time Part Time
Contact Hours 30 30
Non-Contact Hours 120 120
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 150
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: This coursework will consist of developing a business intelligence and data warehouse solution.

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 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 KIMBALL,R., ROSS,M., 2013. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition). John Wiley & Sons, Inc.
2 GUILLEVIN, T., 2019. Getting started with Tableau: effective data visualization and business intelligence. Apress.
3 VAISMAN, A., 2014. Data warehouse systems: design and implementation. Springer.
4 SHARDA R., 2017. Business Intelligence, Analytics and Data Science: A Managerial Perspective on Analytics. Pearson.
5 TANIAR, D., 2021. Data warehousing and analytics: fuelling the data engine. Springer.
6 DECKLER, G., POWELL, B., 2021. Microsoft Power BI Cookbook. Packt Publishing.

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