Business Analytics

MQF Level 7

6 Credits (ECTS)

Business Analytics

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

Module Description

This module is designed to equip participants with the fundamental skills in data analytics, which are increasingly vital for effectively managing organizations that generate large volumes of data during their day-to-day operations. In today’s data-driven world, the ability to analyze and interpret data is paramount for making informed decisions and gaining competitive advantages.

Throughout this module, participants will delve into various concepts and methodologies essential for data analytics, covering a comprehensive range of topics. From understanding the basics of data collection and processing to advanced techniques in statistical analysis and predictive modeling, this module provides a robust framework for harnessing the power of data.

By immersing themselves in real-world case studies and practical exercises, participants will develop a deeper understanding of how data analytics can drive strategic decision-making processes across different organizational functions. They will learn how to extract actionable insights from complex datasets, identify trends and patterns, and ultimately leverage data-driven conclusions to optimize performance and achieve organizational objectives.

Moreover, this module fosters a more rigorous thinking methodology among participants, emphasizing the importance of evidence-based decision-making. By adopting a data-centric approach, participants can mitigate risks, capitalize on opportunities, and drive innovation within their organizations.

In summary, this module empowers participants to become proficient in data analytics, equipping them with the knowledge and skills needed to navigate the complexities of today’s data-rich environments. By embracing data-driven decision-making, participants can position themselves and their organizations for sustained success in an increasingly competitive landscape.

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 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:

  • Critically analyse and evaluate the importance of data analytics for business.
  • Appropriately apply data analysis skills.
  • Rigorously appraise different data analysis techniques & methodologies.
  • Report the findings from data analysis to specialist and non-specialist audiences.


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

  • Examine the fundamentals and importance of business analytics.
  • Discuss the data analysis plan and data gathering.
  • Compare and contrast Data analysis techniques & methodologies.
  • Discuss real world use cases of business analytics.

Key indicative topic areas cover:

  • The fundamentals and importance of data analytics
  • Business Analytics in practice
  • Big Data
  • Descriptive Statistics
  • Data visualisation and reporting


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

  • Gather data-based case study and present it using data visualization and reporting techniques.
  • Apply various data analysis techniques and methodologies.
  • Judiciously apply report writing skills.
  • Thoroughly analyse quantitative research results and present them to the specialist and non-specialist audience.

Judgment Skills and Critical Abilities:

The learner will be able to:

  • Collect, analyse and interpret data/information to support arguments, and to develop solutions and apply problem-solving related to business analytics.

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.

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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