- The main goal is to provide participants with a broader understanding of Information Management, including its impact on business models, sources of competitive advantage, and organizational structure.
- Define information systems and describe various types of information systems
- Understand the importance of managing knowledge and understand the role of IT/IS innovation
- Identify the major enterprise internal and external information systems and relate them to managerial functions
- Understand business process management and how to enhance effectiveness
- Analyse the benefits and issues of integrating functional information systems
- Learn about Intelligent Data Analysis and its applications
- Gain a stronger grasp of AI to address and exploit its opportunities
- Learn how to install and use Python programming language to create machine learning algorithms and use them for decision making
- Learn from case studies and applications
- Course Introduction.
- Information and the information culture
- Information Technologies: the current state, role and the evolution tendency
- Information technologies and Organization management.
- Ethical and privacy challenges. Ethical considerations in the use of ICT. Societal impacts
- Information system, company management system and data architecture
- Concepts of creating, development and implementation of information systems
- Artificial Intelligence and systems in company management
- Review of the course. The main key points addressed in the course.
- Individual assignmentExercises, case-studies, quizzes, panel discussion, and presentations
- Written examination1,5-hour exam. Exam is held as a written test based on all course issues and materials. Participants have to show their knowledge or ability in a particular subject, or to obtain a qualification.
- A group research projectIncluds the production of a report (15%) and group presentation (15%) - 30%
- Python online coursehttps://stepik.org/course/102668
- 2022/2023 2nd moduleThe final assessment is composed as follows: written examination (1,5-hour exam) - 30%; coursework - 70% (Individual assignment - 20%; A group research project including the production of a report (15%) and group presentation (15%) - 30%; Python online course – 20%)
- Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning. Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1827695
- Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
- Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968
- Yang, Y. (2005). Information Theory, Inference, and Learning Algorithms. David J. C. MacKay. Journal of the American Statistical Association, 1461. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.jnlasa.v100y2005p1461.1462