spring 2024
MFA-2101 Maritime Data Analytics - 10 ECTS

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Type of course

The course is reserved for students at Bachelor in Ocean Engineering and cannot be taken as singular course.

Course overlap

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

BED-2056 Introduction to Data Science 5 ects

Course content

This course content is aimed to give the necessary skills for handling data in the maritime sector. This will give solid understanding in how collected data should be handled and analysed with emphasis on the following topics:

  • vessel IoTs and data collecting platforms
  • data collection and storage onboard and onshore storage communication and storage facilities.
  • different techniques for preprocessing, data cleaning, merging and transforming data sets will be treated.
  • identification of data anomalies and recovering such data anomalies
  • techniques for exploratory data analytics and visualization.
  • utilization of big data in ship emission reduction and energy efficiency applications.
  • concepts of Big Data handling, architecture and technologies and knowledge of it impact on the maritime sector

Recommended prerequisites

MAT-0001 Calculus in Applications, MAT-1050 Mathematics 1 for Engineers, MAT-1052 Mathematics 2 for Engineers, MAT-1060 Computational programming and Statistics, MFA-2100 Maritime Digitalization

Objectives of the course

Knowledge

The student ...

  • has knowledge of data set retrieval, requirements for preprocessing, explorative data analysis, cleaning, transforming, merging and transforming data and methods for handling missing data.
  • has knowledge of how data is collected from the different maritime business areas, what characterizes the datasets as size, volume, velocity, variation and veracity
  • has knowledge of how to utilize different techniques for handling Big Data.

Skills

The student ...

  • can carry out data analyses for datasets typical of the maritime field with the use of programming language Python
  • can assess which machine learning techniques are suitable for analyzing datasets generated from the maritime field
  • can evaluate, visualize and communicate results from data analysis for shipping applications.

General competence

The student ...

  • can perform a data analysis of a dataset generated from a maritime business area and communicate its results.
  • can utilize big data techniques to improve and enhance energy efficiency and emission reduction for shipping applications.

Language of instruction and examination

English

Teaching methods

The course is taught with traditional weekly lectures evenly throughout the semester. Practice will include exercises and discussions. Lecturing hours each week is 3 hours.

Examination

Examination: Duration: Grade scale:
School exam 4 Hours A–E, fail F

Coursework requirements:

To take an examination, the student must have passed the following coursework requirements:
Assignment Approved – not approved
Assignment Approved – not approved
Assignment Approved – not approved
Assignment Approved – not approved
UiT Exams homepage

More info about the coursework requirements

In order to take the exam, 3 out of 4 compulsory assignments, consisting of assignments and group assignments, must be completed and approved.

Re-sit examination

Re-sit exam is granted to the students who have failed the last ordinary arranged exam.
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: MFA-2101
  • Earlier years and semesters for this topic