Course - Introduction to Big Data - IT6208
IT6208 - Introduction to Big Data
About
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.
Skills:
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:
- (ITIDIEVU)
Recommended previous knowledge
The teaching assumes basic skills in the use of a computer, and some programming experience is an advantage. Students must have their own computer.
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 |
No
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
- Applied Information and Communication Technology
- Computer Systems
Department with academic responsibility
Department of Computer Science
Department with administrative responsibility
Pro-Rector for Education
Examination
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"