Katedra za elektronsko poslovanje Univerzitet u Beogradu, Fakultet organizacionih nauka
| Studijski program | Nastavnici | Status predmeta | Semestar | ESPB |
|---|---|---|---|---|
| Elektronsko poslovanje | Dragan V. Vukmirović | Obavezni | 1 | 6 |
Cilj predmeta
U procesu elektronskog poslovanja, preduzeća moraju da osiguraju pravilnu interpretaciju i upotrebu kvalitetnih i pravovremenih informacija u cilju efikasnijeg poslovanja, prvenstveno kroz podršku odlučivanju i povećanje operativne efikasnosti, uz poštovanje pozitivnih zakonskih propisa vezanih za upotrebu podataka. Cilj predmeta je ovladavanje znanjima i veštinama rada sa podacima u oblasti elektronskog poslovanja, praćenjem životnog ciklusa podataka, primenom savremenih metoda i pristupa.
Ishod predmeta
Savladavanjem materije predmeta studenti stiču praktična znanja i veštine koja su neophodna za samostalno upravljanje i analizu podataka koji se generišu u internom i eksternom onlajn okruženju, posebno u veb sferi, društvenim medijima i društvenim mrežama.
Literatura
1. B. Radenković, M. Despotović-Zrakić, Z. Bogdanović, D. Barać, A. Labus, Elektronsko poslovanje, ISBN 978-86-7680-304-0; Fakultet organizacionih nauka, Beograd, 2015.
2. Albright, S.C, W. L. Winston (2017) Business Analytics, Data Analysis and Decision Making, Sixth Editition, Cengage Learning.
3. Baker, S and P. Sjoberg (2018). Intelligent Data Governance For Dummies, Hitachi Vantara Special Edition, John Wiley & Sons, Inc., Hoboken, New Jersey
4. Beręsewicz, M., R. Lehtonen, F. Reis,L. di Consiglio and M. Karlberg (2018). An overview of methods for treating selectivity in Big data sources, Publications Office of the European Union, Luxembourg:
5. Cleff, T. (2014). Exploratory Data Analysis in Business and Economics, An Introduction Using SPSS, Stata, and Excel, Springer
6. Hemann, C., K. Burbary (2018). Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World: Making Sense of Consumer Data in a Digital World (Que Biz-Tech), 2 edition, Que Publishing
7. Holmes, M. H. (2016). Introduction to Scientific Computing and Data Analysis, editors: Timothy J. Barth Michael Griebel, David E. Keyes, Risto M. Nieminen, Dirk Roose And Tamar Schlick, Springer International Publishing Switzerland
8. Kamki, J. (2016). Digital Analyitics, Data Driver Decision Making in Digital World, Notion Press
9. McKinney, W. (2018). Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython, O’Reilly Media, Inc.
10. Milton, M. (2009). Head First Data Analysis, O’Reilly Media, Inc.,
11. Pimpler, E. (2017). Data Visualization and Exploration with R. A practical guide to using R, R Studio, and Tidyverse for data visualization, exploration, and data science applications, Geospatial Training Services, Boerne, TX
12. Rafter, C. (2019). A complete guide to cleaning and preparing data for analysis using Excel™ and Google Sheets™, Inzata Analytics. Published by DSM Media
13. Sleeper, R. (2018). Practical Tableau, O’Reilly Media, Inc.
14. Wexler, S., J. Shaffer and A. Cotgreave (2017). The Big Book of Dashboards, Visualizing Your Data Using Real-World Business Scenarios, John Wiley & Sons, Inc
15. Yockey, R. D. (2016). SPSS demystified, A Step-by-Step Guide to Successful Data Analysis For SPSS Version 18.0, Second Edition, Published 2016 by Routledge, Taylor & Francis Group
16. Odabrani stručni i naučni radovi
