Business Intelligence and Data Analytics
MODULE CODE
CREDIT VALUE
Module Aims
Aim 1
The main aim of the module is to provide a comprehensive, up-to-date guide to modern management support system technologies, and showcase how they can be used for better decision-making.The main objectives of the module are to:· Provide an in-depth knowledge of the use of enterprise systems· Study the type of data that the enterprise systems generate· Study how that data might be used to support decision making within an enterprise
Module Content
Decision Making and Analytics: Business Environment, Computerized Support, Managerial Decision Making, Decision Support Systems, BI Methodology, Analytics Overview
Descriptive Analytics: Data Warehouses, Data Mining Concepts, Business Reporting, Visual Analytics, and Business Performance Management
Predictive Analytics: Data Mining, CRISP-DM, SEMMA, KDD, Tools, Techniques for Predictive Modelling, Text Analytics, Text Mining, Sentiment Analysis, Web Analytics, Web Mining, and Social Analytics
Prescriptive Analytics: Model-Based Decision Making: Optimization and Multi-Criteria Systems, Modelling and Analysis: Heuristic Search Methods and Simulation, Automated Decision Systems and Expert Systems, Knowledge Management and Collaborative Systems
Big Data and Future Directions for Business Analytics: Big Data Analytics, Emerging Trends and Future Impacts
Learning Outcomes
On successful completion of this module, a student will be able to:
Teaching Methods
The module tutor will deliver live online lectures through Adobe Connect. During the live lectures the participating students will have the opportunity to engage in discussions, present their views and ask questions. The lecture sessions will be recorded and made available to the students through Blackboard. Students who cannot participate in a live lecture will have the opportunity to answer and reflect on guided questions in the subsequent live lectures or participate asynchronously on discussion boards. The module tutor will provide appropriate feedback to students’ comments, as a result of the discussions. Tutor feedback will primarily be provided in an asynchronous manner through Blackboard and emails, but when the need arises, the module tutor will schedule live sessions to provide further feedback. Where appropriate, students will be also provided with relevant further reading, web links and resources for independent study. Speakers from leading organizations will be invited, where possible, to deliver invited talks and enhance the students’ experience.
Students will also be provided with bi-weekly self-assessment quizzes, so that they can reflect on their progress. Students will be provided with access to specialised software/ datasets/ scripts/ programmes, through which they will be able to complete the practical components of the module. The students will obtain the practical sheets from Blackboard and they are expected to follow the instructions included in the practical sheets to complete the lab work. If students have difficulties with a particular exercise, they are expected to contact the module tutor or post a question on the discussion forum, where the module tutor and/or their peers can provide feedback. Different means of communication will be utilized by the tutor to offer support to the students based on the reported issue, i.e., email, Skype, Adobe Connect, etc.
Assessment Methods
This module is assessed through one Portfolio and one Examination.
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Compulsory
