Data Analytics modules
This core module focuses on the statistical techniques that are expected of a data analyst, and provides students with the opportunity to apply these techniques using the industry standard software SAS. This module lays fundamental core knowledge that will be built upon in other modules in the programme, to enable students to effectively recognise and use statistics in problem solving.
Business Intelligence Systems Concepts and Method
The module introduces Business Intelligence (BI) systems, which other modules can draw upon when studying more a detailed BI system component or the development of such systems. Specifically, it introduces students to the Business Intelligence (BI) system concept and its application within organisations. The historical and current relationships between BI systems and other types of computer-based Information Systems (IS), such as decision and management support systems, data warehouses and artificial intelligence systems, are discussed, in addition to assessing the reasons why and how organisations utilise BI systems, and their overall architecture and expected vs. actual impact/effect.
This module provides grounding in the research methods required at MSc level, looking at both quantitative and qualitative approaches including laboratory evaluation, surveys, case studies and action research. Example research studies from appropriate areas are analysed to obtain an understanding of types of research problems and applicable research methods.
Data Warehouse Design and OLAP
This module covers the design of data warehouses and how an On-Line Analytical Processing (OLAP) tool can provide access to data within a data warehouse. It builds on the student’s prior knowledge of Relational Databases and Relational Database Management Systems (DBMS) to consider the data requirements, underpinned by an appropriate technical infrastructure, for a data warehouse in response to a particular business situation.
Business Intelligence Systems Application and Development
The module builds on the BI systems knowledge already gained by students from previous programme modules, concentrating on the predictive nature of such applications and on their development. Real life case studies will be used to illustrate a range of BI applications (such as demand forecasting, fraud detection, risk analysis, simulation and optimisation). The models used to carry out the processing within the system will be introduced so that students gain an understanding of the underlying (often) mathematically-based model.
Big Data Analytics
Big Data analytics is the process of collecting, storing and accessing large volumes of unstructured heterogenous data in order to uncover useful patterns, trends and correlations. Big Data differentiates from the tradional view of a dataset by the so-called big V’s (Volume, Variety, Velocity and Veracity), where modern computing systems allow businesses, governments and scientists to gather a vast array of unstructured data rapidly. Processing such data has provided its own considerable challenges, leading to a wide spread of new technologies that are constantly changing and improving. This module will introduce students to state-of-the-art approaches to Big Data problems. It will utilise the Hadoop Distributed File System (HDFS) and Apache Spark to demonstrate data mining and machine learning algorithms for knowledge discovery and for presenting the newly acquired information in meaningful ways. Parallel computing in the cloud will be a key aspect incorporated throughout.
Data Mining Techniques and Applications
Data mining is a collection of tools, methods and statistical techniques for exploring and modelling relationships in large amounts of data, to enable meaningful information to be extracted for decision making purposes. The aim of this module is to review the data mining methods and techniques available for uncovering important information from large data sets and to know when and how to use a particular technique effectively. The module will enable the student to develop an in-depth knowledge of applying data mining methods and techniques and interpreting the statistical results in relevant problem domains. This is a practical module, where the emphasis is on students gaining practical experience of using the data mining software, SAS Enterprise Miner, to build sensible models and then for the students to apply their knowledge to interpret the statistical results, to make informed decisions.
Decision making requires appropriate and representative information and data to be collected and analysed. Typically, more effective decisions can be made using large rather than small amounts of data. It is virtually impossible to perform even the most basic statistical techniques by hand. Instead, data can be entered and analysed using a computer software package. One such statistical software package is SAS. This is a very comprehensive package which combines data entry and manipulation capabilities with report production, graphical display and statistical analysis facilities. This module provides students with the opportunities to explore the SAS software package and its capabilities. As well as covering how SAS procedures are used to summarise and display data for inclusion in reports, the module introduces the application of SAS programming to basic statistical analyses, much of which will be made use of in the IMAT 5238 Data Mining module. Case studies will be used to illustrate how datasets from external sources are imported into SAS and how these datasets can be combined together and how new variables can be created.