Data on the course
Show instruction and examinations
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 
TypeCourse   
  Grading1 - 5, pass, fail 
 
   
Unit Computer Science and Engineering DP 

Teachers
Name
Ekaterina Gilman 

Description
ECTS Credits 

5 ECTS cr

 
Language of instruction 

English

 
Timing 

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

 
Learning outcomes 

Upon completion of the course, the student:

  1. is able to explain the big data phenomenon, its challenges and opportunities.
  2. is able to explain the requirements and common principles for data intensive systems design and implementation, and evaluate the benefits, risks and restrictions of available solutions.
  3. can explain the principles of big data management and processing technologies and utilize them on a basic level.
 
Contents 

General introduction into big data, namely: big data fundatmenals, data storage, batch and stream data processing, data analysis, privacy and security, big data use cases.

 
Mode of delivery 

Face-to-face teaching, independent and group work

 

 
Learning activities and teaching methods 

Lectures, exercises, seminars, independent and group work

 
Target group 

M.Sc. students (computer science and engineering) and other Students of the University of Oulu

 
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 slides and exercise material will be provided. Each lecture will include the refernce list for recommended reading. Instructions to necessary installations will be given.

 
Assessment methods and criteria 

This course assesses students continuously by the completion of exercises, seminar presentations and short reports on a selected topic (group work), and answering two quizzes during the course. To pass the course, it is enough to get 50% of available points for each part. No exam.

 

 
Grading 

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 

The course includes also invited lectures 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  11.03.19 -24.04.19

Future examinations
No examinations in WebOodi