SylabUZ
Course name | IT systems in business management |
Course ID | 11.9-WE-INFD-ITSysinBusMan-Er |
Faculty | Faculty of Engineering and Technical Sciences |
Field of study | computer science |
Education profile | academic |
Level of studies | Erasmus programme |
Beginning semester | winter term 2017/2018 |
Semester | 2 |
ECTS credits to win | 6 |
Course type | obligatory |
Teaching language | english |
Author of syllabus |
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The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
Lecture | 30 | 2 | - | - | Credit with grade |
Laboratory | 30 | 2 | - | - | Credit with grade |
Familiarize students with the principles of the ERP systems and methods of implementation of such systems in the enterprise.
Development of skills in planning and building analytical systems.
Familiarize students with the methods of business data mining.
Enterprise resource planning systems: ERP architectures, Characterization of functional modules of ERP systems, Best business practices for ERP systems, ERP implementation methodologies. Overview and characteristics of popular ERP systems.
Analytical systems: Data sources, Data integration, Overview and characteristics of typical data transformation operations, Design and implementation of data transformation processes, Gathering data in a data warehouse, Multidimensional data structures, Presentation of the results of the analysis in the form of reports.
Data mining: Data cleaning, Outlier detection and handling missing data, Discovering association rules and sequences using Apriori and Frequent Pattern Growth, Generalized Sequential Pattern and PrefixSpan algorithms, Data clustering using hierarchical and iterative optimization algorithms, Data classification using knearest neighbor, decision trees and naive Bayes classifier, Time series analysis using parametric models, Overview of systems for data mining.
Lecture, laboratory exercises.
Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – the passing condition is to obtain a positive mark from the final test.
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.
Calculation of the final grade: lecture 50% + laboratory 50%
Modified by dr inż. Anna Pławiak-Mowna, prof. UZ (last modification: 04-05-2017 11:04)