Study Guide

Learning outcomes

The student is able to
-describe and recognise big data’s key components, technologies and opportunities
-plan big data’s use from the perspective of competitive advantage and flexibility
-discuss the impact of big data and be able to justify their view

Implementation and methods of teaching

The course is four day intensive implementation.
The teaching methods used are contact lecturing, and applying assignments based on examples. The course also includes pre and post tasks.

Learning material and recommended literature

The student searches independently literature references, and reliable internet sources. The course includes some presentation material.

Exam retakes

The course does not include an examination.

Learning environment

The informing and organization of the course is handled through virtual learning environment. Exercises are done using Virtual machine (e.g VirtualBox), which has Hadoop installed.

Contents

The course includes
- describing the landscape of Big Data
- explaining the V's of Big Data
- the basics of HDFS
- the basics of using Hadoop with MapReduce
- IoT and Big Data.

Assessment criteria

The evaluation is based on to level of the returnings of the given assignments. Also active course participation and group work have an impact to the evaluation.

Assessment scale

1-5

Failed (0)

The student has not achieved the learning objectives of the course.

Assessment criteria: level 1 (assessment scale 1–5)

A student will be able to
- utilize resources available and make independent decisions to some extent.
- utilize big data networks.
Active participation to the course is required in
- discussion and
- in team work.
Assignments
- the level of returnings can be considered as passed.

Assessment criteria: level 3 (assessment scale 1–5)

A student will be able to
- independently utilize and apply resources available.
- effectively utilize big data networks.
- set and justify objectives that can help develop the contents of the course.
Active participation to the course is required:
- discussion
- team work: give a helping hand if needed
Assignment
- the level of returnings is the desired level (correct and passed) of given assignments

Assessment criteria: level 5 (assessment scale 1–5)

A student will be able to
- independently manage the course as a process to learn different aspects of big data as a whole from start to finish.
- utilize, critically appraise and apply resources available.
- utilize big data networks in a diverse and innovative manner.
- set and justify strategic objectives that help reform the contents of the course.
Active participation to the course is required:
- discussion: be a positive interlocutor, advance the discussion forward into relevant topics, justify opinions
- team work: recognize a need for helping hand of a fellow student, and give one if needed
Assignments
- the level of returnings exceed the required level of given assignments.