Data Management and Machine Learning
The aim of this unit is to develop the student’s knowledge in the areas of data management including online analytical processing; data architectures such as data warehousing and the process and application of machine learning algorithms to data.
- Data Management Overview [15%] - Example content includes database modelling/querying (relational/noSQL), graph data modelling, applications.
- Online Analytical Processing (OLAP) [15%] - Including the representation of multi-dimensional views of data; Technologies and Architectures; Categories of OLAP tools, Business Intelligence Tools.
- Data Warehousing [10%] - Methodologies, architectures, modelling techniques; Data Warehousing Project Management; The Extraction, Transformational and Loading Process;
- Machine Learning Overview [10%] - The machine learning process, Applications of machine Learning.
- Machine Learning Algorithms [50%] - For example, artificial neural networks, naïve bayes, decision trees, clustering, association rules, text mining, fuzzy systems, application, analysis and validation.
Management of Projects Fundamentals
The aim of this Unit is to develop student knowledge in the fundamentals of project management and the management of projects through the Body of Knowledge (APM) and Book of Knowledge (PMI).
Management of Projects Professional Practice
The aim of this Unit is to develop student knowledge in the management of projects in a professional practice setting via problem based learning. Students will be expected to analyse, critique and recommend solutions for an existing project.
Using a problem based learning approach, with a case study provided by an industrial partner, students will explore the management strategies, processes and stakeholders involved in the project, and develop communication and coordination plans to monitor and control the project information.
Project Delivery 1: Project Scheduling, Finance and Resource Management
The success of complex infrastructure projects depends on effective management of the trichotomy of scheduling, finance and resources.
Students will learn project scheduling techniques and strategies based on time, resource and costs; the various public and private financing mechanism for projects; manage available resources and its impact throughout the project lifecycle.
Project Delivery 2: Managing Risks, Quality and Benefits
Effective Risk, Quality and Benefit management is essential to deliver a successful project. Not all risk is negative, but not managing risk will have a negative impact on quality and reduce the benefits of a project.
Students will learn how to identify individual risk events and overall risk and how to manage them proactively, minimising threats and maximising opportunities to optimise success with the wide range of stakeholders throughout the project lifecycle. Students will also consider sustainability principals in managing projects.
Probability and Data Visualisation
Advanced Unit in probability and data visualisation for non-mathematics/statistics graduates. The unit will address both the theoretical as well as the applied aspects of probability. The visualisation component will make extensive use of statistical software.
An introductory unit in data analysis for non-mathematics/statistics graduates. The unit will cover topics of statistics and its applications on real-world data with the use of statistical software.