Intelligent data management in e-business

The aim of the course is to master the necessary methodological concepts of analysis and use of data in the field of electronic business on the basis of modern analytical methods. In particular, methods for identifying and eliminating bias in the use of data in e-business are being studied, thus reducing the risk of erroneous conclusions and decisions.

By mastering the subject matter, students gain the methodological basis necessary for research and independent implementation of methods and techniques for data analysis that are generated in the online environment, especially on social media and social networks.

Theoretical classes:

Methodology of scientific research work with special emphasis on intelligent data management in electronic business. Mathematical foundations of data science. E-business metrics; Data life cycle – methodological bases for data collection in the online sphere; Methods of descriptive analysis and presentation of data; Data validation: methods for determining and monitoring the reliability, accuracy and quality of data, metadata, sampling; Sources of data bias and statistical evaluation and inference; Data processing methods: coding, procedures for identifying missing values, analysis of extreme values; Data transformation and synchronization: normalization, imputation, weighting; Big data analytics; Data management; Reporting methods: Visualization – infographics, Dashboard. Analysis of case studies related to the use of real data, using specialized software packages and tools: SPSS, R, Python, Excel and Google Sheets. An overview of the most important works and projects in the field of intelligent data management in e-business. Analysis of open research problems.

Seminary work

The topics of seminar papers are chosen by students based on the analysis of open scientific research problems presented in lectures. Students are required to publish the results of the seminar paper at a scientific conference or journal of national or international importance.

1. Albright, S.C, W. L. Winston (2017) Business Analytics, Data Analysis and Decision Making, Sixth Editition, Cengage Learning.
2. Baker, S and P. Sjoberg (2018). Intelligent Data Governance For Dummies, Hitachi Vantara Special Edition, John Wiley & Sons, Inc., Hoboken, New Jersey
3. 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:
4. Cleff, T. (2014). Exploratory Data Analysis in Business and Economics, An Introduction Using SPSS, Stata, and Excel, Springer
5. 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
6. 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
7. Kamki, J. (2016). Digital Analytics, Data Driver Decision Making in Digital World, Notion Press
8. McKinney, W. (2018). Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython, O’Reilly Media, Inc.
9. Milton, M. (2009). Head First Data Analysis, O’Reilly Media, Inc.,
10. 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
11. . Rafter, C. (2019). A complete guide to cleaning and preparing data for analysis using Excel™ and Google Sheets™, Inzata Analytics. Published by DSM Media
12. Radenković, B., Despotović-Zrakić, M., Bogdanović, Z., Barać, D. & Labus, A (2015). Electronic business, Faculty of Organizational Sciences, Belgrade
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. Selected professional and scientific papers