MIS41410 Data, Artificial Intelligence and Analytics

Academic Year 2024/2025

Virtually every aspect of business now is open to data collection. This broad availability of data has led to increasing interest in methods for extracting useful information and knowledge from data – the realm of data science and data analytics. This module will help you view business problems from a data perspective and understand principles of extracting useful knowledge from data.

The module will feature analytics case studies, and example simulation/role-play to help frame and contextualise discussion of the goals, methods, benefits, and limitations of different data analytics techniques.

Topics:
• What is data analytics?
• The analytics organisation and data-analytic thinking
• What are businesses trying to achieve? (measurement, understanding, prediction, segmentation, optimisation, decision-making). Business problems and data analytics solutions
• The CRISP data mining process, iteration is the rule rather than the exception
• Choosing the right analytics tool for the job
• Expected value as a framework for data analytics solution design
• The importance of careful curation of data science capability
• Approaches to evaluation of proposals for data analytics projects
• Privacy and ethics issues

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Curricular information is subject to change

Learning Outcomes:

By the end of the module, students will be able to:
Define business analytics and explain in detail its roles in the organisation
Define a number of sub-topics of business analytics and explain how they relate to the organisation’s goal
Map from verbal descriptions of business problems to appropriate analytics approaches
Explain iterative nature of analytics process and importance of business engagement
Discuss critical role of humans in data analytical solution success
Discuss issues of privacy and ethics as they relate to business analytics and data. Be aware of main regulatory frameworks related to application of business analytics
To critically review data analytics project proposals

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Specified Learning Activities

70
Autonomous Student Learning

30
Total

124
Approaches to Teaching and Learning:
The module will feature analytics case studies, and example simulation/role-play to help frame and contextualise discussion of the goals, methods, benefits, and limitations of different data analytics techniques. 
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

Not yet recorded.

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
• Peer review activities

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Gilyana Borlikova Lecturer / Co-Lecturer