Faculty of Technical Sciences

Subject: Business Analytics (17.IM2821)

Native organizations units: Department of Industrial Engineering and Engineering Management, Chair of Production Systems, Organization and Management
General information:
 
Category Professional-applicative
Scientific or art field
  • Menadžment i investicije u inženjerstvu
  • Proizvodni i uslužni sistemi, organizacija i menadžment
ECTS 4

This course aims at acquiring practical and theoretical knowledge and competencies for problem-solving in various business situations. Some of the areas that will be covered include working with business data, their transformation, visualization as well as designing business models (optimization, simulation and regression). The primary focus of the course will be solving problems that require descriptive, predictive and prescriptive analytics.

Students will learn to work individually and in teams at private and public companies that use business analytics. Graduate engineer-master of industrial engineering and management acquires the competencies for big data analysis using statistics, probability and simulations as analytical tools.

Introduction to statistics and Excel. Decision-making in business. Monte Carlo simulation. Linear regression. Time series analysis. Forecasting. Data visualization. Data mining. Linear optimization models.

Classes include lectures that include practical examples of business modeling and simulation. Tutorial sessions encourage team work on analysis of simulations and data processing by using various methods. Tutorial sessions are held in computer labs.

Authors Title Year Publisher Language
Daniel T. Larose Discovering Knowledge in Data: An Introduction to Data Mining (2nd Ed.) 2017 Wiley English
Camm, Cochran, Fry, Ohlmann, Anderson, Sweeney, and Williams Essentials of Business Analytics (2nd Ed.) 2017 Cengage Learning English
Gradojević N., Đaković V. Poslovna analitika, elektronska skripta 2017 Fakultet tehničkih nauka, Novi Sad English
Edward Tufte The Visual Display of Quantitative Information (2nd Ed.) 2001 Cheshire, CT: Graphics Press English
Course activity Pre-examination Obligations Number of points
Project task Yes Yes 25.00
Lecture attendance Yes Yes 10.00
Project Yes Yes 15.00
Written part of the exam - tasks and theory No Yes 50.00
API Image

Prof. Gradojević Nikola

Full Professor

Lectures
API Image

Prof. Đaković Vladimir

Full Professor

Lectures
API Image

Prof. Gradojević Nikola

Full Professor

Computational classes
API Image

Prof. Đaković Vladimir

Full Professor

Computational classes
API Image

Science Associate Todić Vladimir

Science Associate

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.