Introduction to Artificial Intelligence and Data Science

MQF Level 7

6 Credits (ECTS)

Introduction to Artificial Intelligence and Data Science

October 2024
Module Type
ECTS Credits
6 Credits (ECTS)
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€683 refund on this module

Module Description

This 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 must possess one of the following: 

  • a Level 6 degree related to AI/Computer-Science/Mathematics/Electronics; 


  • a Level 6 degree not related to AI/Computer Science/Mathematics/Electronics and a minimum of two years’ relevant experience; 


  • a Level 5 diploma or higher diploma and five years’ of relevant work experience.   

Target Audience

This course is targeted at: 

  • Industry professionals working in different domains, including Technology, Engineering, Science, IT, Finance, Accountancy, Management, Marketing, Insurance, Banking, Gaming, Healthcare, Medicine, Pharmaceutical, Human Resources, Psychology, Blockchain, Legal, Administration, Policy Making, Digital Art, Archaeology, Architecture, Education and other related areas. 
  • Recent graduates with degrees in Computer Science, Technology, Marketing, Finance, Economics, Accountancy, Management, HR, Law, Engineering, Science, Medicine, Psychology, Digital Art, Game Development, Archaeology, Architecture or Business. 
  • Mid-career-break professionals looking for opportunities to return to or change their career.  

The target group may also be extended to positions such as that of wedding manager, transport manager, maintenance manager, operations manager, marketing manager, conference manager and even that of general manager. 

Module / Unit Instructions

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

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

  1. Face-to-face sessions: 18 hours.
  2. Online Learning Activities: 12 hours.

Face to Face sessions

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

Online Learning Activities

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.

The tutor provides continual support during both teaching -learning modes by providing information, readings and tasks relevant to the module in question.

The tutor provides continuous formative feedback as an on-going guidance during the student’s learning experience in preparation for their summative assessment.

How you’ll be assessed

Assessment of each module consists of two assignments, each carrying a weighting as below:

a) One Formative assignment carries 20% of total module mark achieved. b) One Summative assignment carries 80% of total module mark achieved.

For successful completion of a study module the student is required to achieve a minimum of 41% pass mark in both the formative and the summative assignment.

The overall grade achieved for each module is calculated as the sum of:  20% of the mark achieved for formative assignment; and  80% of the mark achieved for the summative assignment.

All assignment tasks of both formative and summative assessment aim to provide the learner an opportunity to produce evidence of his/her competences aligned to the learning outcomes of each individual Module.


a) Formative assessment tasks are provided in the form of structured online discussions that support learners in their development throughout all of the modules studied.

Such discussions are facilitated and monitored by lecturer who provides students with constructive feedback to help them improve and prepare for summative assignment and dissertation.

Formative assessment tasks will contribute to the student’s final mark to acknowledge their work and give chance to improve.

This method allows students to 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

b) Summative assessment is done via one assignment at the end of each module. The mode of assessment varies and may include in-class assignments and home-based written assignments.

For module 1 the recommended assessment tool is using case studies or proposals. Other assessment tools which may be used are presentations or reports.

Word count range: 3000 ± 10% For successful completion of a study module, the student is required to achieve a minimum of 41% pass mark in the summative assignment



Module Intake Dates

October 2024

Additional Info

Reading for the entire Master of Science (M.Sc.) in Artificial Intelligence as presented in this brochure costs €9,775.*  

Upon successful completion of this course, students will be eligible for a 70% refund of the cost through the ‘Get Qualified’ scheme.** 

Due to the modular structure of the course, you may also opt to take individual modules as stand-alone. The entry requirements still apply.***  

*Prices are applicable to students who reside in Malta at the time of applying. 

**Terms and conditions apply.  

*** For the price of individual modules, please contact the IDEA Academy team.  

Learning Outcomes


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

  • Evaluate 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.
  • Rigorously analyze the requirements for AI transition/ implementation in an industry or business.
  • Assess potential challenges and risks AI can bring in the future, especially to remain competitive in an industry or business sector.
  • Research-based diagnose data-oriented problems and data-driven decisions in an industry or business in terms of data exploration and visualisation.


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

  • Discuss AI definitions, capabilities, challanges 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.

Introduction to Artificial Intelligence

  • Definitions of AI
  • Problem-solving
  • Importance and impact of AI on the industry, business and society
  • AI Capabilities
  • Industrialising AI
  • Future of AI
  • AI Challenge

Knowledge, Reasoning, Planning and Learning, Communicating, Perceiving and Acting

  • Classical Planning
  • Knowledge Representation and Reasoning
  • Learning from examples
  • Knowledge in learning
  • Communication
  • Perception

AI Applications

  • Speech Recognition
  • Natural Language Processing
  • Natural Language understanding
  • Dimensionality reduction
  • Virtual Personal Assistants/ Chatbots

AI Applications Cont.

  • Image Recognition
  • Computer Vision
  • Objects Recognition
  • Describing images
  • Feature Selection
  • Feature Extraction
  • Business Applications of AI
  • Health Care Applications of AI
  • Education Applications of AI
  • Finance Applications of AI
  • Robotics
  • Autonomous Transportation
  • Gaming

Introduction to Data Science

  • Data Science-a discipline
  • Landscape-Data to Data science
  • Data Growth-issues and challenges
  • Data science process
  • Foundations of data science

Data Exploration, Preparation, Representation and Transformation

  • Structured vs unstructured data
  • Quantitative vs qualitative data.
  • Four levels of data – nominal, ordinal, interval, ration. Messy data, Anomalies and artifacts in datasets. Cleaning data.
  • Forms of data-tabular, text data, graph-based data.
  • Modern databases- text files, spreadsheets, SQL databases, NoSQL databases, distributed databases, live data streams.

Data Science Applications

  • In Business
  • In Insurance
  • In Energy
  • In Health care
  • In Biotechnology
  • In Manufacturing
  • In Utilities
  • In Telecommunication
  • In Travel
  • In Governance
  • In Gaming
  • In Pharmaceuticals
  • In Geospatial analytics and modeling.


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

  • Carry out an engaging “data-story” to communicate the problem and the inference.
  • Present a case on the need for AI in their business of Industry domain.
  • Thoroughly understand and apply AI-based planning and decision making.
  • Judiciously apply data science in the relevant Industry domain.
  • Rigorously analyze AI applications and their impact on business, industry and society.

Judgment Skills and Critical Abilities:

The learner will be able to:

  • Master problem- solving skills by analysing case studies and working out the issues presented in real life situations.
  • Evaluate and critically appropriate to discipline being studied.
  • Collect, analyse and interpret data/information to support arguments, and to develop and apply ideas.

Module-Specific Communication Skills:

The learner will be able to:

  • Communicates ideas, problems, and solutions to both specialist and non-specialist audiences using a range of techniques involving qualitative and quantitative information to sustain arguments.

Module-Specific Learner Skills:

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

Module-Specific Digital Skills and Competences:

The learner will be able to:

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