Faculty of Technical Sciences

Subject: Introduction to data science (17.ESI056)

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

Mastering students with basic concepts of data science. Students should apply acquired knowledge in anaysis, study and solving real problems.

Educational outcome:

Acquisition of modern knowledge and skills in data science. The student is trained to analyze, study and solve real problems using the acquired knowledge.

Course content:

Basic concepts of data science. Algorithms in data science. Application of mathematical statistics methods in data science. Statistical deriving conclusion. Statistical tests. Sampling correlation and regression. Modeling based on computer intelligence (artificial neural networks, decision trees, associative rules, fuzzy logic, support vector machine, genetic algorithm, etc.). Expert systems. Application of artificial intelligence methods in data science. Big data analysis. Predictions and evaluations. Classification. Application of data science in different fields. Analysis of concrete examples.

Teaching methods:

Lectures. Computer excercises. Consultations. The theoretical part of knowledge is exposed in the lectures. At the same time, practical part of knowledge is exposed in the computer excercises.

Literature:
Authors Title Year Publisher Language
M. Magdon-Ismail, Y. AbuMostafa Learning from Data 2012 AMLBook English
ONeil C., Schutt R. Doing Data Science 2013 OReilly Media, Inc. English
Marz, N., Warren, J. Big Data : Principles and best practices of scalable realtime data systems 2015 Manning Publications, New York English
Sean Gerrish How Smart Machines Think 2018 MIT Press English
Ethem Alpaydin Introduction to Machine Learning 2004 MIT Press English
Cotton R. Learning R 2013 O’Reilly Media, Inc. English
Adžić, N. Statistika 2006 Fakultet tehničkih nauka, Novi Sad Serbian language
Berthold, M.R. Bisociative Knowledge Discovery : An Introduction to Concept, Algorithms, Tools, and Applications 2012 Springer English
Stojaković, M. Verovatnoća, statistika i slučajni procesi 2007 Symbol, Novi Sad Serbian language
Srinivas Sajja, P., Akerkar, R. Advanced Knowledge Based Systems : Models, Applications & Research 2010 TMRF e-Book English
Knowledge evaluation:
Course activity Pre-examination Obligations Number of points
Theoretical part of the exam No Yes 30.00
Complex exercises Yes Yes 70.00
Lecturers:

Saradnik u nastavi Gordić Nebojša

Teaching Associate

Computational classes
API Image

prof. dr Kupusinac Aleksandar

Full Professor

Lectures

Asistent Dragaš Bojana

Assistant - Master

Computational classes

Saradnik u nastavi Despotović Predrag

Teaching Associate

Computational classes

doc. Vrbaški Dunja

Assistant Professor

Lectures

Asistent Manasijević Aleksandar

Assistant - Master

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.