This module will address topics of:

Theory & Knowledge Exchange
Types of data (structured, unstructured etc)
Data structures and data types
Relational data modelling
Entity Life Histories
Entity Relationship Modelling
Normalisation
OLTP
Relational databases and SQL fundamentals
Introduction to concepts of Big Data
Multidimensional data modelling
Data warehouses
Schemas and dimensional normalisation
OLAP operations, Slice, Dice, Roll-up, Drill-down Pivot
Preparing data and ensuring quality
NOSQL databases

Technology & Resources
NOSQL data stores
Relational Database Management Tool

Practical Content
Creation and querying a database (group work)
Preparation of raw data for Data warehousing

PRACTICAL DEMONSTRATION: Students will be provided with a realistic case study for which they are required to work in a group to produce an Entity Relationship Diagram normalised to 3NF, and then create, and populate the database, and undertake several prescribed and non-prescribed queries to test the effectiveness of the database model (Learning Outcomes 1 to 3).

CLASS-TEST: Students will prepare notes on several topics related to multidimensional data modelling. The notes may be taken into the examination and be used to help them demonstrate their understanding of multidimensional data modelling concepts and associated operations (Learning Outcomes 1 and 4).

All teaching sessions will blend theory and practical learning. Students will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples within the same sessions. In addition, students will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.

1. Understand the nature of data, data types and data structures.

Knowledge and Understanding

2. Understand the fundamental concepts of relational data modelling.

Analysis, Problem solving

3. Apply the techniques of data modelling to the construction and interrogation of a relational database.
Application

4. Understand the fundamental concepts of multidimensional data modelling, data warehouses and OLAP operations.

Knowledge and Understanding, Learning

Standard PCs
Database software
Analysis modelling software

All texts and electronic resources will be updated and refreshed on an annual basis and available for students via the online Study Links resource platform. All reference materials will be collated and curated and aligned to Equality, Diversity & Inclusion indicators.

Core Text/Resource:

Mauri, D, Coriani, S et. al (2020), Practical Azure SQL Database for Modern Developers: Building Applications in the Microsoft Cloud, Apress; 1st ed. Edition, ISBN-10: 1484263693
Connolly, T.M. & Begg, C.E. (2014), Database systems: a practical approach to design, implementation and management, Sixth, global edn. ASIN:1292061189
Kimball and Ross. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modelling. 3rd Edition. John Wiley & Sons, ASIN: B01BNWGH4Q

Optional Text/Resource:

Quinn, M.J. 2017, Ethics for the information age, 7th edn, Pearson, Harlow, ASIN : 0134296540

All resources will be updated regularly and available via a module KeyLinks online function.

The module introduces relational and multidimensional data modelling concepts. It provides useful practice and experience of using SQL. Students will learn about data warehouses and OLAP operations (OLAP cube, rollup, drill-down, slice and dice and pivot). They will also gain practical skills in ensuring that data is ‘cleaned’ in preparation for data warehousing.