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521283S Big Data Processing and Applications, 5 ECTS cr 
Code 521283S  Validity 01.08.2015 -
Name Big Data Processing and Applications  Abbreviation Big Data Proces 
Scope5 ECTS cr   
TypeAdvanced Studies Discipline4307 Information Engineering 
  Grading1 - 5, pass, fail 
Unit Computer Science and Engineering DP 

Ekaterina Gilman 

ECTS Credits 

5 ECTS cr

Language of instruction 



Period IV. It is recommended that the course is taken on the fourth year Spring.

Learning outcomes 

1. Can explain the big data phenomena and the possibilities it has in data processing and exploitation

2. Can analyse concrete technologies for big data management and processing

3. Can explain the common solutions available for cloud services, and understand the benefits, risks and restrictions of them

4. Will learn the principles of the current new technologies and utilize them on a basic level


1. Creating understanding for the Big data framework, 2. The central technologies, solutions and services, 3. Big data and cloud services, 4. Big Data processing and applications, 5. Knowledge extraction, data mining and pattern recognition from Big Data, 6. Design exercise, 7. Seminar.

Mode of delivery 

Face-to-face teaching, seminar and opponent work.


Learning activities and teaching methods 

12h lectures, 27h exercises, 32h seminar, independent studying.

Target group 

M.Sc. level students of Computer Science and Engineering; other students of the university of Oulu are accepted if there is space in the classes.

Prerequisites and co-requisites 

The Bachelor level studies of Computer science and engineering study programmes or respective knowledge, the exercises do not require programming skills but they are an advantage.

Recommended optional programme components 

Finishing 521290S Distributed Systems, 521497S Pattern recognition and neural networks, and 521286A Computer Systems is beneficial.

Recommended or required reading 

Lecture hand-out and exercise material will be provided. The course book will be announced in the beginning of the course. Instructions to necessary installations will be given.


Assessment methods and criteria 

Attending lectures and finishing a design exercise wit a report. The reports will be evaluated in seminar work through student opponents.



The course utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail.


Person responsible 
Ekaterina Gilman 


Working life cooperation 

Lecturers from industry.

Other information 



Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Big Data Processing and Applications   Course  Ekaterina Gilman  12.03.18 -08.05.18

Future examinations
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