MQF Level 7

6 Credits (ECTS)


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

Module Description

This module will equip learners with the basic understanding of algorithms, their importance, their building blocks, their complexity evaluation, and designing effective and efficient algorithms, and their applications.

Learners will also learn various data structures such as Arrays, Graphs, and memory representations.

Learners will learn various Algorithm Design Techniques such as Divide and Conquer Algorithms, Greedy Algorithms, Dynamic Programming, Back Tracking Algorithms.

They will also get exposure to some of the popular sorting, searching, graph, pathfinding, and network flow algorithms.

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 this module , the recommended assessment tool is Algorithm design project and quiz (MCQ) or case studies.

Other assessment tools which may be used are reports and proposals. Word count range: 3000 ± 10% (except quizzes)

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:

  • Critically analyse algorithms, their complexity, and their efficiency.
  • Thoroughly understand Graphs, its representation and various operations performed.
  • Rigorously assess various sorting, searching, graph, pathfinding, and network flow algorithms.
  • Judiciously Examine various techniques to select the most suitable algorithms for a given programming task.
  • Exhaustively evaluate (apply) various algorithm design techniques.


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

  • Discuss Data Structure and Algorithms including its efficiency, best-case, average-case and worst-case analysis.
  • Examine components of sorting and searching algorithms.
  • Discuss different types of algorithms and their applicability.
  • Explain Algorithm Design Techniques.

Key indicative topic areas cover:

  • Introduction to Data Structure and Algorithm
  • Sorting Algorithms
  • Searching Algorithms
  • Graph Algorithms
  • Path Finding Algorithms
  • Network Flow Algorithms
  • Algorithm Design Techniques


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

  • Rigorously examine and use various sorting and search algorithms.
  • Thoroughly assess and apply the different graph and pathfinding algorithms.
  • Judiciously analyse and apply various network flow algorithms.
  • Critically evaluate and apply various algorithm design techniques.

Judgment Skills and Critical Abilities:

The learner will be able to:

  • Master problem- solving skills related to algorithms and working out the issues presented in real-life situations.

Module-Specific Communication Skills:

The learner will be able to:

  • Communicates ideas, problems, and solutions related to algorithms to both specialist and non-specialist audiences.

Module-Specific Learner Skills:

The learner will be able to:

  • 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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