IB94V-15 Data Analytics and Artificial Intelligence
Introductory description
In the new era of the "data economy", the value of high-quality, granular, and rich data assets is a critical success factor for transformative decision making in many industries and the leveraging of artificial intelligence (AI).
Module aims
This module aims to equip students with fundamental knowledge regarding the effective use of enterprise and web data to meet the needs of modern organizations in the digital age and introduce technologies such as AI. The strategic value of data resources, as a means of enabling firms to achieve competitive advantage, is first considered, in order to understand the rationale behind the adoption of data management and business analytics practices. The challenges and opportunities of adopting various business analytics techniques, including using artificial intelligence for many analytical tasks, are investigated in order to obtain critical understanding of the building of organisational capability in these areas.
Students will gain a sound appreciation of salient factors affecting the successful deployment of data-driven decision making in organizations. They will get hands-on experiences using visual analytics tools and data analytics tools to handle real-world datasets and inform decisions.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Strategic nature of data and relevant organizational practices and business models emerging from these.
Management of data and relevant technologies (big data, AI, business intelligence practices, etc.)
Business Analytics techniques including data analytics (use of software) and use in organisations for decision-making (business performance management, dashboards, etc.)
Introduction of artificial intelligence, techniques, and applications (supervised/unsupervised learning, machine learning, text mining, clustering, etc.)
Issues around the quality of data and bias, risks, and business opportunities.
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of the core concepts and theories of data analytics and artificial intelligence and their implications for contemporary enterprises
- Demonstrate a comprehensive understanding of methodology for developing data visualizations and analytical models, and the managerial issues associated with the selection and adoption of such data analytics technologies
- Develop a comprehensive understanding of the managerial and organisational issues associated with the use of data and artificial intelligence practices
- Demonstrate critical analytics skills in the information- and data-driven business environment
Indicative reading list
Reading lists can be found in Talis
Interdisciplinary
This module is the intersection between business, statistics and computer science, and the goal of the module is to introduce the business applications of AI in different sectors. However, to ensure students’ full understanding of the AI models, some basic knowledge in statistics and computer science will be involved
International
Data analytics and AI is a global trend. In many case studies, the students will need to consider how cultural difference influences the implementation and applications of AI in organisations
Subject specific skills
- Evaluate management practices and recommend measures to enable an organisation to exploit data assets and AI-related information technologies effectively
- Evaluate and exploit real-world datasets and make business recommendations using data analytics tools
Transferable skills
Written and oral communication
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 1 hour (13%) |
| Practical classes | 9 sessions of 2 hours (23%) |
| Private study | 50 hours (64%) |
| Total | 78 hours |
Private study description
Self study to include pre-reading before lectures and preparation for assessment
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| 10 Min Presentation + 1000 word report | 20% | Yes (extension) | |
Reassessment component is the same |
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Assessment component |
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| Written Exam | 80% | No | |
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Reassessment component is the same |
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Feedback on assessment
Individual feedback provided via the online feedback system.
Courses
This module is Core for:
- Year 1 of TIBS-G5N4 Postgraduate Taught Management of Information Systems and Digital Innovation