| Laajuus |
5 ECTS credits |
| Opetuskieli |
English |
| Ajoitus |
Period IV. It is recommended that the course is taken on the fourth year Spring. |
| Osaamistavoitteet |
Upon completion of the course, the student :
- is able to explain the big data phenomenon, its challenges and opportunities.
- 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.
- can explain the principles of big data management and processing technologies and utilize them on a basic level.
|
| Sisältö |
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. |
| Järjestämistapa |
Face-to-face teaching, independent and group work |
| Toteutustavat |
Lectures, exercises, seminars, independent and group work |
| Kohderyhmä |
M.Sc. students (computer science and engineering) and other Students of the University of Oulu
|
| Esitietovaatimukset |
The Bachelor level studies of Computer science and engineering study programmes or respective knowledge. |
| Yhteydet muihin opintojaksoihin |
Finishing 521290S Distributed Systems, 521497S Pattern recognition and neural networks, and 521286A Computer Systems is beneficial. |
| Oppimateriaali |
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. |
| Suoritustavat ja arviointikriteerit |
This course assesses students continuously by the completion of small project work, seminar presentations and short reports on a selected topic (group work). Answering two quizzes during the course is optional and provides additional points for final grade. To pass the course, it is enough to get 50 % of available points. No exam.
Read more about assessment criteria at the University of Oulu webpage.
|
| Arviointiasteikko |
The course utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail.
|
| Vastuuhenkilö |
Ekaterina Gilman |
| Työelämäyhteistyö |
The course includes also invited lectures from industry. |
| Lisätiedot |
Course is in Moodle. |
