155-0906/01 – Systems Supporting the Strategic Decision Making (SPSR)
| Gurantor department | Department of Applied Informatics | Credits | 10 |
| Subject guarantor | prof. Ing. Dušan Marček, CSc. | Subject version guarantor | prof. Ing. Dušan Marček, CSc. |
| Study level | postgraduate | Requirement | Choice-compulsory |
| Year | | Semester | winter + summer |
| | Study language | Czech |
| Year of introduction | 2014/2015 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of the course is to improve theoretical and methodological knowledge in the use of information technologies to support information systems and the development of methods for strategic decision-making.
Teaching methods
Lectures
Individual consultations
Summary
Information technology, information systems, information management,.Components of information management. Concepts of gaining a strategic advantage through IT. IS strategy formulation. IT architecture. IS implementation strategy. Information system planning, structure and content of the process, case studies. Database. Data models, conceptual and logical. Balanced Scorecard (BSC) as a modern approach to strategic management.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Exam: oral questions from the given topics
E-learning
Other requirements
Elaborating a written work that has a close relation to the PhD thesis topic.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
The main topics of the course are:
1. Concepts of systemic approach to solving strategic issues, systemic analysis of conditions for preparation of strategic decisions;
2. Data warehouse architecture and its components, organization and data mining, data warehouse implementation, data models, hierarchy, granulation, changing dimensions, metric additives, data warehouse use for decision-making at tactical and strategic management level, methods, flexible tools and the SW products of BI for strategic decision support;
3. Supervized, unsupervized and hybrid learning from data, machine learning and its understanding, SVM, logistic regression, decision trees;
4. Fuzzy systems and fuzzy referral systems, LSP and CWW, fuzzy time series, fuzzy system for identification and prediction of I/O functions of sytems;
5. Prediction of high-frequency data using soft coping methods, learning methods of hybrid UNS based on error correction concept, BP, GA, MGA, heuristics and B-J approach used in dynamic time series modelling.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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