Subject: Big data management and analysis (17.EK552 )
Native organizations units: No data
Study programmes of the course:
| Type of studies | Title |
|---|---|
| Master Academic Studies | Information Engineering (Godina: 1, Winter) |
Educational goal:
Introducing students with computer and network infrastructure for storage of large amounts of data, great heterogeneity and large arrival speed. Acquiring knowledge and mastering practical skills in apply different algorithms for analyzing and managing large sets of data (Big Data).
Educational outcome:
Identifying value in data and discovering knowledge from data.Construction of physical and virtual storage for storing large amounts of data. Students will be able to use the distributed file systems and MapReduce as a tool for creating parallel algorithms that succeed on big data. Understanding and implementation of the algorithms for big data management and combining and evaluating algorithms for mining big data sets.
Course content:
Data warehousing. Distributed file systems (Hadoop, Spark). Virtual warehouses and communications. Database virtualization. Management of large databases on cloud. MapReduce program model for distributed data processing. Data Ssearches (Similar Samples, Frequency Sample Samples). Data in the form of graphs, link analysis, local and global topological attributes. Machine learning algorithms for big data. Data visualization.
Teaching methods:
Lectures, computer lab sessions (Matlab, Python), homework, consultations, active learning, project and research based learning, students' competitions.
Literature:
| Authors | Title | Year | Publisher | Language |
|---|---|---|---|---|
| 2017 | English | |||
| 2010 | English | |||
| 2005 | English | |||
| 2014 | English | |||
| 2015 | English |
Knowledge evaluation:
| Course activity | Pre-examination | Obligations | Number of points |
|---|---|---|---|
| Homework | Yes | Yes | 5.00 |
| Homework | Yes | Yes | 5.00 |
| Homework | Yes | Yes | 5.00 |
| Homework | Yes | Yes | 5.00 |
| Project | Yes | Yes | 50.00 |
| Theoretical part of the exam | No | Yes | 30.00 |
Lecturers:
doc. Suzić Siniša
Assistant Professor
Computational classes
Asistent Lazić Ivan
Assistant - Master
Practical classes
doc. Suzić Siniša
Assistant Professor
Practical classes
vanr. prof. dr Bojović Živko
Associate Professor
Lectures
prof. dr Lončar-Turukalo Tatjana
Full Professor
Lectures
Asistent Lazić Ivan
Assistant - Master
Computational classes
Faculty of Technical Sciences
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Contact:
Address: Trg Dositeja Obradovića 6, 21102 Novi Sad
© 2024. Faculty of Technical Sciences.