Data management and analysis in e-business

Theoretical classes

Data science. Data life cycle – identification of data sources, data collection process – qualitative and quantitative methods. Sampling methods from online populations and links to statistical inference. Data types and measurement scales. Methods of presenting and interpreting data. Data validation: methods for determining the reliability, accuracy and quality of data, metadata. Sources of bias in data. Data preparation: coding, procedures for identification of missing values, analysis of extreme values. Data transformation and synchronization. Analysis of unstructured data. Data processing. Structured data analysis. Inference methods. Reporting and communication: visualization – infographics, dashboard. Data management: establishing strategic frameworks and platforms, data management process, organization of data storage and access, legal and ethical codes.

Practical teaching

Practical classes follow theoretical classes and consist of laboratory exercises and case study analysis related to the use of real data. Laboratory exercises are performed using specialized software packages and tools: SPSS, R, Python, Excel and Google Sheets

In the process of electronic business, companies must ensure the correct interpretation and use of quality and timely information in order to operate more efficiently, primarily through decision support and increase operational efficiency, while respecting the positive legal regulations related to the use of data. The aim of the course is to master the knowledge and skills of working with data in the field of electronic business, monitoring the life cycle of data, the application of modern methods and approaches.

By mastering the subject matter, students acquire practical knowledge and skills necessary for independent management and analysis of data generated in the internal and external online environment, especially in the web sphere, social media and social networks.

1.B. Radenković, M. Despotović-Zrakić, Z. Bogdanović, D. Barać, A. Labus, E-business, ISBN 978-86-7680-304-0; Faculty of Organizational Sciences, Belgrade, 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. Selected professional and scientific work