Course - Big Data - DIFT2006
DIFT2006 - Big Data
About
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
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 portfolio consists only of mandatory exercises that are approved during the semester. All exercises must be approved to get the portifolio 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:
Digital Business Development (ITBAITBEDR)
Recommended previous knowledge
Some programming experience is an advantage
Course materials
Stated at the start of the semester
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| INFT2003 | 7.5 | AUTUMN 2020 | |
| IINI3012 | 5.0 | AUTUMN 2020 | |
| IFUD1123 | 5.0 | AUTUMN 2020 | |
| IT6208 | 7.5 | AUTUMN 2021 |
No
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
- Computer Science
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Portfolio
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Autumn
ORD
Portfolio
100/100
Submission
2024-11-22
12:00 -
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"