Course syllabus

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MMS285 Digitalization and AI for future shipping: Fundamentals and applications lp4 VT25 (7.5 hp)

Course purpose

The course aims to give students knowledge in the digitalization of the maritime sector with an emphasis on how digitalization can be used in daily operations and for decision-making transport. For this, the course aims to enhance students' understanding and skills in big data and AI/machine learning tools used in shipping digitization, as well as in visualizing and analyzing data within environmentally sustainable maritime transport and management. Through actual industry practice in environmentally sustainable shipping, the course also aims to provide students with an in-depth understanding of challenges and opportunities related to digitalization and AI in the maritime transport industry. During the course, participants' skills in PYTHON programming will be further developed.

 

Schedule

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Course literature

The following literature will be used in the course:

  • Gruner, J. (2021). Digital Transformation in Shipping: The Hapag-Lloyd Story. In: Seebacher, U.G. (eds) B2B Marketing. Management for Professionals. Springer, Cham.
  • Martelli, A, Ravencroft, Holden, S. McGuire, P. (2023). Python in a Nutshell (4th Edn), OReilly.
  • Computer laboratory manual and instructions are provided during the course.

 

Course design

The course is organized around,

  • Lectures and guest lectures
  • Computer laboratory exercises,
  • Project assignments
  • Seminars

 

Learning objectives and syllabus

Learning objectives:

  • Explain challenges and opportunities in digitalization in maritime transport sector
  • Explain use of big data/AI at stakeholders within shipping sector
  • Apply PYTHON in big data/AI analysis
  • Explain application of programming scrips in big data/AI analysis
  • Show understanding in the use of big data tools in digitalization of the maritime transport sector
  • Develop machine learning models for decision making
  • Explain and reflect on the impact of digitalization/on students own learning 

 

Examination form

The examination of the course consists of the following elements,

  • Course element 1: Project assignments presented and reported during a final seminar (4,5 credits)
  • Course element 2: Laboratory exercises, PYTHON programming (3 credits)

Course summary:

Date Details Due