1 What this Module is About?
1.1 Introduction from the Module Leader
A quote from SiSense: “Data intelligence refers to all the analytical tools and methods companies employ to form a better understanding of the information they collect to improve their services or investments.”.
This module equips you with advanced data analytics, predictive analytics and data intelligence as well as analytics skills. It will support the development of an in-depth, systematic and critical understanding of the current research issues relating to Data Analytics and Intelligence.
1.2 Module Aims
On behalf of the module team, I would like to welcome you to this module, which we hope you will find both challenging and rewarding.
The module is based around the use of data analytics for informatics, decision making support, and intelligent systems. We will investigate the models, approaches, issues, techniques and technologies to support data analytics and intelligence. At MSc level, the style of learning is very self-directed, you are expected to investigate and explore topics independently and report back to the group. It is important to be able to understand and explain your findings.
Topics such as data analysis, visualisation, analytical method and big data will be discussed. The module provides you with an in-depth, systematic and critical understanding of the current research issues concerning Data Analytics, Data Intelligence and Knowledge Discovery.
The foundations of this module are based on the theories and practical issues associated with Analytics and Intelligence. We will use the python toolset for the application of theories – note that there are many other software products that can be used for analytics, visualisation, statistics, data mining, machine learning and decision support making.
We hope this will be a valuable learning experience for you. This module builds on students’ database design experience to achieve learning outcomes as follows.
1.3 Module Learning Outcomes
On completion of this module you should be able to have:
LO1 A critical awareness of current problems and research issues in tools used for Data Analytics and Representation.
LO2 A comprehensive understanding of current advanced scholarship and research in data mining, data analysis, data intelligence, and how this may contribute to the effective design and implementation of data representation applications.
LO3 The ability to consistently apply knowledge concerning current research and advanced scholarship in area of data mining, data analysis, and data intelligence in an original manner and produce work which is at the forefront of current developments.
LO4 Critically evaluate basic principles and motivations behind the idea of data Intelligence, understanding different perspectives relating to data Intelligence and develop ideas regarding the ways in which data intelligence (e.g. Business Intelligence) principles can be translated into feasible and effective practices for and decision support systems.
1.4 Module Learning Activities
Online recorded lectures: you will need to view online recorded powerpoint slides. If you have any query, raise them during face-to-face sessions or online live tutorial sessions.
Face-to-face scheduled sessions: fortnightly face-to-face scheduled sessions are for discussion of any queries you have about the lectures, lab exercises, and assignment/phase test.
Online live tutorial with tutor and group: discussion of problems that you encounter when working through the lab exercises.
Self-paced unscheduled tutorial sessions: go through fully guided step-by-step lab exercises. Tutorial sessions will be individual-based practical sessions that allow students to analyse, evaluate, and implement solutions. Such practical sessions will help strengthen your hand-on experience in developing solutions to support decision making or intelligent systems. We aim to train you as a consultant, an analyst and a solution provider for you and your future employers. Extensive independent research and application will be expected as students will need to read around the subject in order to gain a wider understanding of the theory application of the technologies covered. There will be a report to describe the mini research that you have undertaken. Students are expected to be disciplined and self regulated, completing activities and tasks set for each week in order to keep to the schedule in Section 2.
1.5 Graduate Attributes Developed and Assessed
The ENTERPRISE graduate attribute is developed and assessed via problem-solving activities.
The DIGITAL LITERACY graduate attribute is both assessed and developed via interpreting and evaluating data.
The ANALYTICAL graduate attribute is developed and assessed via data analytics involving statistics, prediction, optimisation, machine learning – a means to develop knowledge discovery and yielding business intelligence and analytics.
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