course-details-portlet

INFT2003 - Big Data

About

New from the academic year 2020/2021

Examination arrangement

Examination arrangement: Home examination
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Home examination 100/100 6 hours ALLE

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 compulsory exercises

Compulsory assignments

  • Mandatory assignments

Further on evaluation

Re-sit examination in May/June. Re-sit exam may be given as alternatively oral exam.

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Admission to a programme of study is required:
Information Technology (ITBAINFO)

Credit reductions

Course code Reduction From To
DIFT2006 7.5 01.09.2020
IINI3012 5.0 01.09.2020
IFUD1123 7.5 01.09.2020
More on the course

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2020

Language of instruction: English, Norwegian

Location: Trondheim

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

Department with academic responsibility
Department of Computer Science

Phone:

Examination

Examination arrangement: Home examination

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Home examination 100/100 ALLE

Release 2020-12-01

Submission 2020-12-01

Release 09:00

Submission 15:00

INSPERA
Room Building Number of candidates
Spring UTS Home examination 100/100 ALLE 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.
Examination

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

More on examinations at NTNU