Faculty of Technical Sciences

Subject: High Performance Computing in Data Science (17.RVP07)

Native organizations units: No data
General information:
 
Category Scientific-professional
Scientific or art field Applied Computer Science and Informatics
Interdisciplinary No
ECTS 6
Educational goal:

Introducing students to the possibillities and techniques of practical application of achitectures, algorithms, and methods of high performance computing in data science.

Educational outcome:

Students acquire advanced knowledge about applications of high performance computing in data science. Acquired knowledge is used in practice.

Course content:

Fundamental concepts in data analytics. Implementation and application of selected techniques for data analysis (classification – nearest neighbors, decision trees, support vector machine; clustering – k-means, hierarchical) in HPC systems. Application of HPC in Big Data. Design patterns in HPC and Big Data. Application of Hadoop and Elasticsearch in data analytics. Selected use cases – business analytics, prediction of trends and behaviors, analysis of Web data.

Teaching methods:

Teaching is performed through lessons, oral, and computer exercises (in the computer classroom), as well as consultations. Through the teaching process, students are constantly motivated to an intensive discussion, problem oriented reasoning, independent study work, and active participation in the whole lecturing process. The prerequisite to enter final exam is to complete all the pre-exam assignments by earning at least 30 points.

Literature:
Authors Title Year Publisher Language
Gheorge, R., Hinman, M. L., Russo, R. Elasticsearch in Action 2015 Manning Publications English
Provost, F., Fawcett, T. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 2013 O’Reilly Media, Sebastopol English
Witten, I. H., Frank, E., Hall, M. A. Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition 2011 Morgan Kaufmann English
White, T. Hadoop: The Definitive Guide, 4th edition 2015 O’Reilly Media English
Knowledge evaluation:
Course activity Pre-examination Obligations Number of points
Test Yes Yes 10.00
Oral part of the exam No Yes 30.00
Test Yes Yes 10.00
Test Yes Yes 10.00
Complex exercises Yes Yes 30.00
Test Yes Yes 10.00
Lecturers:

vanr. prof. dr Kordić Slavica

Associate Professor

Computational classes

vanr. prof. dr Kordić Slavica

Associate Professor

Lectures

vanr. prof. dr Ivančević Vladimir

Associate Professor

Lectures

vanr. prof. dr Ivančević Vladimir

Associate Professor

Computational classes

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

Contact:

Address: Trg Dositeja Obradovića 6, 21102 Novi Sad

Phone:  (+381) 21 450 810
(+381) 21 6350 413

Fax : (+381) 21 458 133
Emejl: ftndean@uns.ac.rs

© 2024. Faculty of Technical Sciences.