course-details-portlet

DIFT2006

Big Data

Credits 7.5
Level Intermediate course, level II
Course start Autumn 2026
Duration 1 semester
Language of instruction Norwegian
Location Trondheim
Examination arrangement Portfolio

About

About the course

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.
  • 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

The teaching of the course consists of theory followed by practical problem solving. In addition, it is planned that the students will apply the competence they acquire in exercises.

Further on evaluation

(the information may be changed until June 15th)

The portfolio consists of six assignments:

  • All assignments carry equal weight.
  • The assignments are to be completed individually.
  • They are distributed evenly throughout the semester.

All assignments must be passed in order to receive a ‘pass’ grade for the portfolio.

In the case of voluntary retake, failure (‘Fail’), or valid absence, the entire portfolio must be retaken in a semester with teaching

Specific conditions

Admission to a programme of study is required:
Digital Business Development (ITBAITBEDR)

Course materials

Stated at the start of the semester

Credit reductions

Course code Reduction From
INFT2003 7.5 sp Autumn 2020
IINI3012 5 sp Autumn 2020
IFUD1123 5 sp Autumn 2020
IT6208 7.5 sp Autumn 2021
This course has academic overlap with the courses in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Computer Science

Contact information

Course coordinator

Lecturers

Department with academic responsibility

Department of Computer Science

Timetable

Timetable

Calendar view
List view
Show detailed timetable in TP Download .ics file iCal

Examination

Examination

Examination arrangement: Portfolio
Grade: Passed / Not Passed

Ordinary examination - Autumn 2026

Portfolio
Weighting 100/100

All about examinations at NTNU