Skip to main content


MAR8183 : Commercial Awareness and Data Analytics

  • Offered for Year: 2024/25
  • Module Leader(s): Professor D John Mangan
  • Lecturer: Dr Yongchang Pu
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 10
ECTS Credits: 5.0
European Credit Transfer System


This module aims to:
-       Introduce commercial awareness and the basics of key business and management concepts, tools
and techniques;
-       Demonstrate the importance of management, interpersonal and communications skills and how these can be
-       Highlight the value of data and how it can be analysed and managed
-       Learn and understand the Python programming language and machine learning techniques and their
application to both research and practice.

Outline Of Syllabus

Specialist knowledge in the maritime domain is not enough to allow you to thrive in your future career in the maritime and related sectors. Understanding key business fundamentals, appreciating the value of data (‘the new gold’), and enhancing your skills sets are all increasingly essential in addition to specialist domain knowledge.

The syllabus comprises a series of pillar topics chosen and sequenced to allow you to learn and grow to become a competent professional:
-       Management and Leadership
-       Societal imperatives: ESG (Environmental, Social and Governance), EDI (Equality, Diversity and Inclusion), CSR (Corporate Social Responsibility), Security, Net Zero;
-       Management Skills and Self Awareness;
-       Innovation and Entrepreneurship, Business Strategy, Marketing, Operations, Human Resource Management;
-       Data management, Data Protection and Security;
-       Python programming language, Machine learning techniques and applications.

The assessments (formative and summative) will allow you to test and develop the key knowledge and skills requirements of the module and see them in the context of your target career trajectory.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture32:006:00Lectures on Python and machine learning.
Scheduled Learning And Teaching ActivitiesLecture52:0010:00Lectures on key business and management topics
Guided Independent StudyAssessment preparation and completion130:0030:00Preparation of the assessed report
Structured Guided LearningAcademic skills activities120:0020:00Review of online learning materials and completion of the formative assessments.
Scheduled Learning And Teaching ActivitiesSmall group teaching12:002:00Skills development workshop
Scheduled Learning And Teaching ActivitiesSmall group teaching32:006:00Tutorials on Python and machine learning
Guided Independent StudyIndependent study126:0026:00General reading and consolidation of module notes
Teaching Rationale And Relationship

The lectures are designed to provide a comprehensive introduction to the key pillar topics on the module. These are then augmented with both tutorials and online learning materials. The skills workshop provides a safe learning space for students to enhance key business skills.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report2M100Individual report of 2000 words
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Prob solv exercises2M(1) Online self awareness questionnaires (2) Python exercises (3) Online training courses on GDPR and EDI.
Assessment Rationale And Relationship

Students have a choice to complete an individual report on either the data / python part of the module or the commercial awareness part of the module and will apply their module learning to a specific company or data set of their choice thus enhancing and contextualising their learning opportunity from this module (note that the formative assessments will ensure that students will study both parts of the module regardless of which half they choose to focus upon for the formative assessment).

Reading Lists