SBUS46050 Data, Artificial Intelligence and Analytics

Academic Year 2023/2024

The broad objective of this module is to give participants the knowledge, tools, and insights to demystify AI and analytics, and explore their potential in organizations, particularly in the context of digital transformation. Real-world examples will be presented to demonstrate the capabilities of, the limitations of, and opportunities provided by these technologies. Participants in the module also will discover how to leverage these technologies to create a competitive edge for their business and learn how to anticipate trends and outcomes and make informed decisions. Importantly, participants will explore what it takes for success with AI and analytics and the critical role of information. The module will also address the ethical considerations for AI (fairness, transparency, accountability, etc.); the regulatory landscape for AI (GDPR, CCPA, etc.), and best practices for ethical AI development and deployment.

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Curricular information is subject to change

Learning Outcomes:

Understand the language of artificial intelligence, machine learning and analytics;
Appreciate the fundamental concepts of analytics and AI (e.g., distinguish between AI, ML and analytics, supervised versus unsupervised versus reinforcement learning, reasoning, intelligence, etc);
Recognise and analyse the capabilities and limitations of AI and analytics and explore their potential strategic and operational impact;
Appreciate the potential opportunities through exploring a variety of application areas;
Be aware of the benefits of a strategy-driven versus a data-driven approach;
Understand ethics, and the regulatory frameworks governing the use of data and algorithms, as well as legislation coming down the line;
Understand the capabilities organisations require to successfully leverage artificial intelligence and analytics;
Appreciate the challenges organisations face in realising expected outcomes as they look to adopt AI and analytics.

Indicative Module Content:

What is business analytics? What is AI/Machine learning (ML)? What problems can be addressed with analytics, AI, and ML? process mining, privacy, risks, ethics, regulatory environment, requirements for success.

Student Effort Hours: 
Student Effort Type Hours
Lectures

16
Specified Learning Activities

40
Autonomous Student Learning

60
Total

116
Approaches to Teaching and Learning:
The module has a detailed study guide. Students are required to complete the module pre-reading or pre-work prior to attending the face-to-face seminar sessions. The sessions themselves will be a combination of lectures, group discussions, in-class presentations, case study discussions and classroom exercises. A heavy emphasis in the seminars is on teasing out the implications of theory for practical application in a workplace context. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Attendance: Module Participation Unspecified n/a Graded No

20
Assignment: Post face-to-face seminar written assignment. Unspecified n/a Graded No

80

Carry forward of passed components
Yes
 
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Written feedback to be provided within 20 days of assignment deadline.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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