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Opinto-opas 2014-2015
Data Engineering, 30 cr |
Type of the study block
Advanced Studies
Contact
Timo Aaltonen, Alpo Värri, Ari Visa, Juho Kanniainen, Serkan Kiranyaz, Ireneusz Defee
Learning Outcomes
| - | Students in data engineering will have: - good understanding on data engineering and data science - the ability to analyse big data - ability to design data mining applications. - knowledge of relevant computer systems and programming techniques |
Prerequisites
The students should pay attention to the prerequisite requirements of the courses they select to their Data Engineering module. For example, the basic signal processing, programming or pattern recognition courses are prerequisites to some of the courses in the module. ( Advisable )
Content
Compulsory courses
| Course | Credit points | Class |
| SGN-42006 Machine Learning | 5 cr | IV |
| SGN-43006 Knowledge Mining and Big Data | 5 cr | IV |
| Total | 10 cr |
Optional Compulsory Courses
Must be selected at least 9 credits of courses
| Course | Credit points | Alternativity | Class |
| SGN-41006 Signal Interpretation Methods | 4 cr | 3 | IV |
| SGN-81006 Signal Processing Innovation Project | 5-8 cr | 1 | V |
| TIE-13100 Tietotekniikan projektityö | 5-10 cr | 1 | V |
| TIE-20100 Tietorakenteet ja algoritmit | 5 cr | 2 | IV |
| TIE-20106 Data Structures and Algorithms | 5 cr | 2 | IV |
| TIE-23406 Distributed Systems | 5 cr | 3 | IV |
| TST-01606 Demola Project Work | 5-10 cr | 1 | V |
1.
Select 1 courses.
Choose one of the project courses
2.
Select 1 courses.
Choose one of the courses
3.
Select 1 courses.
Choose at least one of the courses
Complementary Courses
The students should acquire knowledge and skills to work with databases either by taking the 1-2 database courses offered or by self-studying them.
Should be completed to the minimum study module extent of 30 ETCS
Additional information
Data engineering concerns gathering, analysing and management of huge data sets available in the networked world from the Web, sensors, social media. Data engineering includes thus: text, web, media mining, temporal, spatial, scientific, statistical, financial and biological databases. Data engineering involves also management by metadata and XML, heterogeneous, and distributed databases and data warehouses and systems including security and integrity control.