IT6208 - Introduction to Big Data


Examination arrangement

Examination arrangement: Mini project
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Mini project 100/100

Course content

Business value of big data, types of analytics, capabilities, and applications of bigdata. Introduction to big data techniques and programming. The course contents are adapted with relevant teaching materials for the industry, e.g. using relevant datasets, chosen examples, applications and cases relevant for the industrial sector.

Learning outcome

The candidate:

- understands business value of big data.

- Knows about content, capabilities and applications of big data.

- knows about techniques for analysis and visualization of big data. - is familiar with big data architecture.

- understands privacy and trust issues in big data.


The candidate:

- can articulate and communicate with stakeholders the business value of big data.

- can structure the process of big data analytics and compose big data analytics teams.

- can propose and use relevant big data techniques in practical projects.

General competence:

The candidate:

- has an understanding of the significance of big data in companies and society at large.

- can take part in planning and implementation of big data projects.

- can identify, plan and implement individual tasks in big data projects.

Learning methods and activities

The course is fully online and consists of lectures with compulsory exercises.

Compulsory assignments

  • Excercises

Further on evaluation

Mandatory work requirements must be approved before the assessment can be carried out.

Mandatory work requirement contains:

  • All exercises must be approved.

The assessment consists of:

  • Mini-project (pass/fail) Repeat at the next completion of the course.

Specific conditions

Admission to a programme of study is required:

Course materials

Teaching materials are articles, book chapters, as well as a number of lecture notes developed by the instructors. More information will be available at the start of the course.

Credit reductions

Course code Reduction From To
IINI3012 5.0 AUTUMN 2021
INFT2003 7.5 AUTUMN 2021
DIFT2006 7.5 AUTUMN 2021
More on the course



Version: 1
Credits:  7.5 SP
Study level: Further education, lower degree level


Term no.: 1
Teaching semester:  AUTUMN 2023

Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Applied Information and Communication Technology
  • Computer Systems
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science

Department with administrative responsibility
Pro-Rector for Education


Examination arrangement: Mini project

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Mini project 100/100 INSPERA
Room Building Number of candidates
Spring ORD Mini project 100/100 INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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