Program Overview

University of Novi Sad

Faculty of Technical Sciences

The Faculty of Technical Sciences is a higher education institution located in Novi Sad, Serbia. The faculty offers a range of academic programs in various fields of technical sciences.

Departments

The faculty is organized into several departments, including:

• Department of Energetics, Electronics, and Telecommunications
• Department of Mechanical Engineering and Constructional Engineering
• Department of Architecture and Urban Planning
• Department of Production Engineering
• Department of Environmental Engineering and Occupational Safety
• Department of General Disciplines in Engineering
• Department of Civil Engineering and Geodesy
• Department of Technical Mechanics
• Department of Computer Science and Automation
• Department of Industrial Engineering and Management
• Department of Transportation
• Department of Energy and Process Engineering
• Department of Graphic Engineering and Design

Study Programs

The faculty offers various study programs, including:

• Bachelor's and master's degree programs in fields such as electrical engineering, mechanical engineering, architecture, and computer science
• PhD programs in technical sciences

Accreditation

The faculty's study programs are accredited by the relevant authorities, ensuring that they meet the highest standards of quality and academic excellence.

Research Areas

The faculty's research areas include:

• Energy and environmental engineering
• Mechanical engineering and constructional engineering
• Architecture and urban planning
• Computer science and automation
• Industrial engineering and management
• Transportation engineering

Courses

Some of the courses offered by the faculty include:

• Big Data - Management and Analysis (17.EK552)
  • Category: Theoretical-methodological
  • Narrow scientific field: Telecommunications and signal processing
  • ECTS: 5
  • Goal: To introduce students to computer and network infrastructure for storing large amounts of data, and to teach them practical skills in applying various algorithms for analyzing and managing big data.
  • Content: Data storage, distributed file systems (Hadoop, Spark), virtual storage and communication, data management on the cloud, MapReduce programming model, data search, graph data analysis, machine learning algorithms on big data, data visualization.
  • Methods of instruction: Lectures, computer exercises (Matlab, Python), homework, consultations, active learning, project-based learning, student competitions.
  • Literature:
    • Michael Manoochehri, Data Just Right: Introduction to Large-Scale Data & Analytics, 2014, Addison-Wesley, English
    • Tom Clark, Storage Virtualization: Technologies for Simplifying Data Storage and Management, 2005, Addison Wesley, English
    • Greg Schulz, Software-Defined Data Infrastructure Essentials: Cloud, Converged, and Virtual Fundamental Server Storage I/O Tradecraft, 2017, CRC Press, English
    • J. Leskovac, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets, 2010, Cambridge University Press, English
    • Tom White, Hadoop: Definitive Guide, 4, 2015, OReilly Media, English
  • Assessment:
    • Project: 50 points
    • Homework: 5 points
    • Homework: 5 points
    • Homework: 5 points
    • Theoretical part of the exam: 30 points
    • Homework: 5 points

Faculty Members

The faculty has a team of experienced and qualified professors, associate professors, and assistants, including:

• Prof. Dr. Tatjana Lonar-Turukalo, Full Professor
• Vanr. Prof. Dr. Tamara kori, Associate Professor
• Doc. Dr. Sinia Suzi, Assistant Professor
• Asist. Ivan Lazi, Assistant