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Course info
KEM / DSSN
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Course description
Department/Unit / Abbreviation
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KEM
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DSSN
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Decision Support Methods and Systems
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
10
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Seminar
24
[Hours/Semester]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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NO
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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NO
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Winter semester
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2 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
S|N |
Periodicity |
každý rok
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Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The course aims to familiarize students with the decision support methods, tools and systems. Students will gain skills in utilizing tools using the presented methods and learn how to apply them in business decision making.
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Requirements on student
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To take an active part in at least two workshops (block tuition), self-study of recommended sources, and e-learning. To write a term paper according to the student´s specialization (the extent of the term paper is from 15 to 20 standard pages). The exam is in the form of a defence of the student´s term paper including the topics mentioned in course contents - see STAG.
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Content
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The course is intended for doctoral studies.
Use of information and knowledge in decision-making processes.
Processes of data mining in databases, CRISP-DM methodology.
Sources of data mining: databases, statistical methods and machine learning.
Machine learning methods: decision trees, decision rules, association rules, neural networks, genetic algorithms, and Bayesian learning methods.
Use of statistical tools and machine learning tools in Statistica SW and Mathematica SW.
Evaluation methods of designed models.
Methods of data preparation.
Overview of systems for data mining in databases.
Principles of Decision Support Systems (DSS).
Tools for creating Decision Support Systems.
Predictive markets, their principles and applications in DSS.
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Activities
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Fields of study
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e-learning (Moodle)
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Guarantors and lecturers
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Literature
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Basic:
Rud, Olivia Parr. Data mining : praktický průvodce dolováním dat pro efektivní prodej, cílený marketing a podporu zákazníků (CRM). Vyd. 1. Praha : Computer Press, 2001. ISBN 80-7226-577-6.
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Basic:
Berka, Petr. Dobývání znalostí z databází. Vyd. 1. Praha : Academia, 2003. ISBN 80-200-1062-9.
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Basic:
Fotr, Jiří; Hájek, Jiří; Vrbová, Lucie. Počítačová podpora manažerského rozhodování. Vydání první. 2016. ISBN 978-80-245-2135-0.
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Basic:
Hebák, Petr. Statistické myšlení a nástroje analýzy dat. Vyd. 1. Praha : Informatorium, 2013. ISBN 978-80-7333-105-4.
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Recommended:
Jensen, Finn V. Bayesian networks and decision graphs. New York : Springer, 2001. ISBN 0-387-95259-4.
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Recommended:
Rokach, Lior; Maimon, Oded. Data mining with decision trees : theory and applications. Hackensack : World Scientific, 2008. ISBN 978-981-277-171-1.
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Recommended:
Turban, Efraim; Aronson, Jay E.; Liang, Ting-Peng. Decision support systems and intelligent systems. 7th ed. Upper Saddle River : Pearson/Prentice Hall, 2005. ISBN 0-13-046106-7.
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Recommended:
Mitchell, Tom Michael. Machine learning. Boston : McGraw-Hill, 1997. ISBN 0-07-042807-7.
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Recommended:
Vaughan Williams, Leighton. Prediction markets : theory and applications. New York : Routledge, 2011. ISBN 978-0-415-57286-6.
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Recommended:
Gangur, Mikuláš. Prediktivní trhy : principy, struktura a využití prediktivních trhů : pobídkové a motivační systémy prediktivních trhů : problematika implementace prediktivního trhu. Vydání první. 2015. ISBN 978-80-7478-847-5.
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Recommended:
Hendl, Jan. Přehled statistických metod : analýza a metaanalýza dat. 2015. ISBN 978-80-262-0981-2.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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E-learning [dáno e-learningovým kurzem]
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80
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Presentation preparation (report in a foreign language) (10-15)
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20
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Team project (50/number of students)
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25
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Preparation for comprehensive test (10-40)
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40
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Individual project (40)
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60
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Contact hours
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24
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Attendance on a field trip (number of real hours - maximum 8h/day)
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11
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Total
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260
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
Build basic SQL statements to obtain the required data. |
Apply knowledge acquired in courses KEM / STA, KEM / SZD and KEM / ADM. |
Apply in practice the theory of statistical testing of hypotheses. |
Analyze the dependence of two variables, apply the theory of regression functions of one explanatory variable. |
Perform exploratory data analysis and verify their quality. |
Work with covariance and correlation matrices. |
Work with matrices, know their properties and apply matrix operations, find eigenvalues and eigenvectors. |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
Work with the selected database system to obtain the required data. |
Work with an extended list of statistical functions in MS Excel. |
Work with basic functions of SW Statistica (data loading, data modification, basic statistics, graphs). |
Work with basic functions of SW Mathematica (basics of working with a laptop, inserting functions, working with hints, graphic functions). |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
create thesis in demanded structure
use statistical methods on master level
search articles in English and Czech
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
Get an overview of selected data processing methods. |
Understand the principles of selected methods, know the assumptions of their use. |
Understand the outputs and know the procedures of subsequent interpretation. |
Understand the principles of data mining and understand the criteria for selecting appropriate methods. |
Skills - skills resulting from the course: |
Choose the right methods with regard to the analysis of the problem. |
Analyze and verify data quality. |
Test models and compare them, interpret results based on outputs, apply results in their own decisions.
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Use practically selected methods in selected SW (Statistica, Mathematica, MS Excel). |
Apply results in your own decision making. |
Competences - competences resulting from the course: |
use advanced statistical and data mining methods
apply suitable DSS software on practical problems
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Oral exam |
Project |
Individual presentation at a seminar |
Skills - skills achieved by taking this course are verified by the following means: |
Skills demonstration during practicum |
Project |
Seminar work |
Individual presentation at a seminar |
Competences - competence achieved by taking this course are verified by the following means: |
Seminar work |
Skills demonstration during practicum |
Project |
Individual presentation at a seminar |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture supplemented with a discussion |
Seminar |
E-learning |
Self-study of literature |
One-to-One tutorial |
Group discussion |
Individual study |
Skills - the following training methods are used to achieve the required skills: |
Seminar |
Interactive lecture |
One-to-One tutorial |
Group discussion |
Competences - the following training methods are used to achieve the required competences: |
One-to-One tutorial |
Seminar |
Discussion |
Students' portfolio |
Interactive lecture |
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