| ECTS Credits |
5 ECTS credits |
| 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 :
- 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.
|
| 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. |
| 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. No exam.
Read more about assessment criteria at the University of Oulu webpage.
|
| 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. |
