module specification

The module provides an introduction to relational data modelling and multidimensional data modelling techniques for data analytics. It enables students to acquire skills in advanced SQL and OLAP operations (OLAP cube, rollup, drill-down, slice and dice and pivot). The module is designed to help students with practical skills in preparing data for analysis which usually takes 50%-70% of data analytical project time.  Big Data analytics platforms will also be introduced.

The aims of this module are to:

• provide students with an understanding of key data modelling and OLAP concepts and techniques;
• enable students to acquire knowledge of data warehouse and multidimensional data models;
• develop students expertise in advanced SQL and OLAP operations;
• develop students with practical skills in preparing data for analysis;
• appreciate big data analytics platforms.

• Relational data modelling (Operational database, Relational model concepts, ERD, Normalisation)
• Multidimensional data modelling (Data warehouse, Dimensional model concepts, Dimensional modelling process, Dimension Normalization)
• SQL data manipulation (SQL Fundamentals, Basic SQL, Restricting and Sorting Data, Single-Row Functions, Join, Aggregating Data, Subqueries, Views, Pivoting data)
• OLAP operations (OLAP Fundamentals, OLAP cube, Roll-up, Drill-down, Slice and dice,   Pivot (rotate))
• Introduction to Big Data Analytics Platforms (Revolutionary, Apache Hadoop, NoSQL, In-memory analysis)

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (2 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week):
Practical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills in SQL, Data modelling and OLAP operations.
.
- Blended learning:
Using the University’s VLE and online tools to provide deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

Students will be expected and encouraged to produce reflective commentaries on the learning activities and tasks that they carry out to complete their work.

On successful completion of this module the student should be able to:
[LO1]  Demonstrate a clear understanding of the various fundamental concepts of data modelling, data warehouse, and OLAP.
[LO2]  Apply a sufficient level of knowledge and proficiency in relational and multidimensional data modelling techniques for data analytics.
[LO3]  Demonstrate competence in advanced SQL and OLAP operations.
[LO4]  Gain a considerable exposure to the practical skills in preparing data for analysis.
[LO5]  Develop an enhanced awareness of some current developments in big data analytics platforms for data analytics.
[LO6]  Analyse, appraise and apply legal, social, ethical, professional issues for developing systems.

1. Connolly, T. & Begg, C. (2014). Database Systems - A Practical Approach to Design, Implementation and Management (6th ed.), Pearson Education.
2. Steve Ries. (2013). Oracle Database 11g: DBA: A Real-World Certification Guide McGraw-Hill.
3. John Paredes. (2009). The Multidimensional Data Modeling Toolkit: Making Your Business Intelligence Application Smart with Oracle OLAP.
4. Ralph Kimball and Margy Ross. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 3rd Edition. John Wiley & Sons.

on-line resources:
Oracle OLAP User's Guide
http://oracle.su/docs/11g/olap.112/e10627.pdf [Last accessed: 10/12/2015]