module specification

CC7181 - Data Modelling and OLAP Techniques for Data Analytics (2017/18)

Module specification Module approved to run in 2017/18
Module title Data Modelling and OLAP Techniques for Data Analytics
Module level Masters (07)
Credit rating for module 20
School School of Computing and Digital Media
Total study hours 200
152 hours Guided independent study
48 hours Scheduled learning & teaching activities
Assessment components
Type Weighting Qualifying mark Description
Coursework 60%   Coursework
Unseen Examination 40%   2 hour unseen exam
Running in 2017/18
Period Campus Day Time Module Leader
Autumn semester North Thursday Morning

Module summary

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.

Module aims

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)

Learning and teaching

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.

Learning outcomes

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.

Assessment strategy

The module will be assessed by a practical piece of coursework (60%) and a 2-hour unseen examination (40%).

The coursework is designed mainly to assess the practical aspects of the module:

It will provide students with the opportunity to undertake research on current issues and practical techniques in data modelling, SQL and OLAP for data analytical [LO1, LO2, LO3, LO4, LO5, LO6].
It will also enable students to apply their knowledge to a practical data analytical problem, demonstrating their skills for problem-solving and critical thinking/evaluation [LO2, LO3, LO4].

The unseen examination will provide an opportunity for students to demonstrate their understanding of data modelling, data warehouse, and OLAP concepts and techniques and their ability to apply these techniques appropriately to the solution of given problems/scenarios [LO1, LO2, LO3, LO5].
The examination will test the students' retention, understanding and insight of material drawn from the module.


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 [Last accessed: 10/12/2015]