INFT2003 - Big Data


Examination arrangement

Examination arrangement: Portfolio
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio 100/100

Course content

Business value of big data. Content, capabilities and applications of big data. Introduction to big data techniques and programming.

Learning outcome

Knowledge (kunnskaper)

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.

Skills (ferdigheter)

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 (generell kompetanse)

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

Lessons, video and exercises

Further on evaluation

The portfolio consists of exercises that are approved during the semester. All exercises must be approved to get the portofolio PASSED.

In the event of voluntary repetition, fail (F) or valid absence, the entire portfolio must be retaken in a semester with teaching.

Specific conditions

Admission to a programme of study is required:
Computer Science - Engineering (BIDATA)
Information Technology (ITBAINFO)

Course materials

Stated at the start of the semester

Credit reductions

Course code Reduction From To
DIFT2006 7.5 AUTUMN 2020
IINI3012 5.0 AUTUMN 2020
IFUD1123 7.5 AUTUMN 2020
IT6208 7.5 AUTUMN 2021
More on the course



Version: 1
Credits:  7.5 SP
Study level: Intermediate course, level II


Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: Norwegian

Location: Trondheim

Subject area(s)
  • Computer Science
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science


Examination arrangement: Portfolio

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Portfolio 100/100
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"

More on examinations at NTNU