Introduction to Artificial Intelligence and Data Science

MQF Level 5


Introduction to Artificial Intelligence and Data Science

October 2024
Module Type
ECTS Credits
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Module Description

The Module has two parts. The first part of the Module will equip learners with the basic understanding of what AI is, its importance and impact on the industry, business and society, and AI applications in various sectors including finance, healthcare, education, and government.

Learners will also learn various components of AI, including knowledge representation, reasoning, decision making and problem-solving. Learners will also get an exposure of some of the AI tools such as Rapid miner, Weka and R.

The second part of the Module will equip learners with the data science background required for AI and its applications.

It will include data exploration, preparation, representation, transformation, modelling, visualisation and presentation.

Entry Requirements

Candidates who apply for this course will possess:

• A related qualification at MQF Level 4


• Two ‘A’ Levels (MQF Level 4) or equivalent one of which should be related to Accounts, Business or Economics and a pass in English Language and Mathematics at MQF Level 3 (‘O’ Level or equivalent).

• Preference will be given to applicants having 1 year work experience related to the study programme.

In the case of students who do not possess all the formal required academic qualifications, then the Recognition of Prior Learning (RPL) process could be applied such that if evidence of equivalent learning is found then the applicant could still be accepted in the course.

Such RPL process will subject applicants to an interview held with a board of experts within the field, chosen specifically by IDEA Academy, so as to verify their experiences and prior learning.

Students whose first language is not English and do not possess an ‘O’ level pass in English Language will be required to demonstrate English language capability at IELTS level 6.0 or equivalent. 

Target Audience

  • Individuals seeking to advance their academic and professional knowledge in Accounting.
  • Individuals wanting to pursue a wide range of accounting, consulting, financial and managerial career paths.
  • Individuals wanting to enhance their ability to interpret, assess and communicate financial related data.

Module / Unit Instructions

The proposed structure comprises a blended approach promoting the building of a community of practice via lecturer-to-student and peer-to-peer learning.

The structure uses primarily two dimensions of teaching-learning modes:

Module Components Structure: · Face-to-face sessions: 18 hours · Online Learning Activities: 12 hours.

Face-to-face sessions include lectures, tutorials, discussions, presentations and workshop activities promoting peer-to-peer learning.

Online learning activities incorporate tutorials and asynchronous discussions.

These may consist of active interaction, participation and contributions in fora discussions, sharing resources and self-reflection exercises.

Learners also contribute to the building of the community of practice by providing feedback to their peers as critical friends, enhancing the learner’s critical engagement throughout the study period.

How you’ll be assessed

This module consists of two individual assessments , each carrying a weighting as below:

a) Assessment 1 carries 30% of total module mark achieved. b) Assessment 2 carries 70% of total module mark achieved.

The overall grade achieved for each module is calculated as the sum of: • 30% of the mark achieved for Assessment 1; and • 70% of the mark achieved for the Assessment 2.

Both Assessment 1 tasks and Assessment 2 tasks aim to provide the learner an opportunity to produce evidence of his/her competences aligned to the learning outcomes of each individual Module.

Assessment 1 and Assessment 2 may include in-class assignments and home-based written assignments using diverse assessment tools which may take the form of online and in-class discussions, examinations of 2-3 hours, case studies, reports, proposals, essays, and presentations.

For successful completion of a study module the student is required to achieve an overall pass mark of 50%


Module Intake Dates

October 2024

Learning Outcomes


At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

  • Identify the need for AI technologies and its role and impact in an industry or business.
  • Propose how AI technologies can be best exploited for an industry or business.
  • Examine the requirements for AI transition/ implementation in an industry or business.
  • Outline potential challenges and risks AI can bring in the future, especially to remain competitive in an industry or business sector.


At the end of the module/unit the learner will have been exposed to the following:

  • Critically discuss AI definitions, capabilities, challenges and AI impact on the industry, business and society.
  • Categorise and explain broad range applications of AI.
  • Describe Data Science as a discipline including its foundations, Data Growth-issues and challenges and data science process and application.
  • Compare and contrast 4 levels of data, forms of data and modern databases.

At the end of the module/unit the learner will have been exposed to the following indicative content:

  • Introduction to Artificial Intelligence
  • Knowledge, Reasoning, Planning and Learning, Communicating, Perceiving and Acting
  • AI Applications
  • AI Applications Cont.
  • Introduction to Data Science
  • Data Exploration, Preparation, Representation and Transformation
  • Data Science Applications


At the end of the module/unit the learner will have acquired the following skills:

  • Analyse the need for AI in their business of Industry domain.
  • Appraise AI applications and their impact on business, industry and society.
  • Review the four levels of data.
  • Examine the different forms of data.
  • Critically discuss the existing AI challenges.

Judgment Skills and Critical Abilities

At the end of the module/unit the learner will be able to:

  • Collect, analyse, and interpret data/information to support arguments, and to develop and apply ideas.

Module-Specific Communication Skills:

At the end of the module/unit the learner will be able to:

  • Communicate ideas, problems, and solutions effectively to both specialist and non-specialist audiences verbally or in writing (PowerPoint presentations, reports, etc.).
  • Participate in class/online discussions and in organized workshops.
  • Present ideas, work, and findings to peers, lecturers, and specialist and non-specialist audiences.
  • Contribute to team work as required.

Module-Specific Learner Skills:

At the end of the module/unit the learner will be able to:

  • Undertake independent and self-directed study through primary and secondary research.

Module-Specific Digital Skills and Competences:

At the end of the module/unit, the learner will be able to:

  • Work with modern databases.
  • Navigate through the online learning platform to find assignments, discussion boards, literature, tutorials etc.
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