Module: Digitalisation & Business Analytics in Aviation

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Digitalisation & Business Analytics in Aviation
Module Code	MT5242 (ITS) / MNA1146 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Viktoriia Ivannikova
Module Teachers	Marina Efthymiou
NFQ level	9	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

Technology and the rise of big data and data analytics are having a significant impact on business decision-making, including operations. This microcredential module introduces participants to the concepts of digitalisation of aviation operations and the principles of data analytics. The main objective of this micro- credential module is to introduce the student to the main digital technologies used in aviation and the fundamental tools and techniques of using data analytics ethically to support sustainable business decisions. This micro is benefited by industry engagement and involvement in the form of guest lectures and case studies.

Learning Outcomes

1. Explain the key concepts of big data and data analytics, the business case for data analytics, and how it interacts with international aviation operations and strategies in practice
2. Describe the types of analytical tools and statistical modelling techniques available to analyse business data and assess their suitability for different types of business problems.
3. Explain what the digital transformation is, and how the digitalisation of services and products has evolved within the aviation industry
4. Critically evaluate different digitalisation options, their push and pull factors as well as their value for the industry

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	21	Lecture (on-campus and synchronous online)
Assignment Completion	76	Completion of research for assignment and assignment preparation and documentation
Directed learning	60	Reading assigned materials for class, such as cases, articles and web sources.
Independent Study	30.5	Reading, research, library work
Total Workload: 187.5

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Big Data and Analytics
What are Big Data and Data Analytics? Growth of Big Data; Business Case for Big Data; Barriers to using Big Data, including Ethical Concerns; Big Data and aviation.

Data Types and Structure and data analytics tools
What are the different types of data types? What is the difference between structured and unstructured data? Which types of data sources are aviation businesses using to make decisions? How can digital operations be integrated with data analytics? An overview of the variety of Statistical Software, Data Programmes, Databases & languages, Business Intelligence Tools, and Visualisation Tools available to analyze business data.

Introduction to digitalization
What is digitalisation? What is the business case for digitalization? Which operations can be digitalized and with what criteria? What are the push and pull factors?

Digitalisation in aviation practices
What is BlockChain? Biometrics for airport security? Airport 3.0 for optimized flow monitoring? Internet of Things (IoT)? AI and VR for pilot training? 5G?

Case Studies and Guest Lectures
A variety of case studies and guest lectures focused on how aviation is/can be digitalized and how data analytics is applied in practice in the aviation world.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	The students need to develop/discuss a case study for an aviation company on the digitalization of operations and the use of business analytics principles.	100%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Azizul Hassan,Nor Aida Abdul Rahman: 2022, Digital Transformation in Aviation, Tourism and Hospitality in Southeast Asia, Routledge, 9781032324654
Thomas L. Seamster,Barbara G. Kanki: 0, Aviation Information Management, 9781138258280
Soraya Sedkaoui,Mounia Khelfaoui,Nadjat Kadi: 0, Big Data Analytics, 9781771889568
SERGEY V. SAMOILENKO: 2022, Digitalization, Routledge, 9781032114095

Other Resources

None