Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Module Offerings
5126COMP-JAN-CTY
Aims
To investigate data warehousing in context of business intelligence.
To implement the principle models of data warehousing.
To utilize the process of extract, transform & loading in the construction of data warehousing.
To utilize data mining in the pursuit of effective knowledge discovery and decision making.
Learning Outcomes
1.
Evaluate the role of data warehousing in supporting business intelligence.
2.
Implement effective business intelligence solutions using the principle models of data warehousing.
3.
Demonstrate the extract, transform & loading process in preparing data.
4.
Demonstrate effective use of data mining methodologies & technologies.
Module Content
Outline Syllabus:Introducing Business Intelligence & Data Warehousing
• Decision Making
• OLTP vs OLAP
Semantic Models
• Multi-Dimensional Model
• Tabular Model
Platforms & Tools
Extract, Transform & Loading
Tabular Modelling
• DAX Statements & Expressions
Multi-Dimensional Modelling
• Measures & Dimensions
• MDX Scripting & Querying
Modelling, Visualising & Reporting
• PowerBI
• Reporting Services
Module Overview:
To investigate data warehousing in context of business intelligence. To implement the principle models of data warehousing. To utilize the process of extract, transform & loading in the construction of data warehousing. To utilize data mining in the pursuit of effective knowledge discovery and decision making. This module explores two principle models of data warehousing & mining, the long recognised multi-dimensional model and the more recently recognised tabular model. Beginning with a study into the key factors that characterise and differentiate business intelligence systems from database systems, the module continues by exploring the methodologies and technologies that support these two models. This module thusly represents the logical follow-on to NQF5s Database Systems module.
To investigate data warehousing in context of business intelligence. To implement the principle models of data warehousing. To utilize the process of extract, transform & loading in the construction of data warehousing. To utilize data mining in the pursuit of effective knowledge discovery and decision making. This module explores two principle models of data warehousing & mining, the long recognised multi-dimensional model and the more recently recognised tabular model. Beginning with a study into the key factors that characterise and differentiate business intelligence systems from database systems, the module continues by exploring the methodologies and technologies that support these two models. This module thusly represents the logical follow-on to NQF5s Database Systems module.
Additional Information:This module explores two principle models of data warehousing & mining, the long recognised multi-dimensional model and the more recently recognised tabular model. Beginning with a study into the key factors that characterise and differentiate business intelligence systems from database systems, the module continues by exploring the methodologies and technologies that support these two models. This module thusly represents the logical follow-on to NQF5’s Database Systems module.