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Course info
KEM / AADM
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Course description
Department/Unit / Abbreviation
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KEM
/
AADM
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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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Data Analysis and Models in English
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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,
8
Cred.
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Type of completion
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Written
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Type of completion
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Written
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Time requirements
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Lecture
3
[Hours/Week]
Tutorial
1
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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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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YES
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Language of instruction
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English
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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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YES
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Winter semester
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0 / -
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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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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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10
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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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English
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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 |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
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 |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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KEM/ADM
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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 aim of this course is to broaden both practical and theoretical knowledge of stochastic methods (data processing using selected robust methods, time series analysis, nonlinear regression, Markov chains, queuing systems, inventory models) and to teach application of these models to managerial decision-making using sw Mathematica for choice.
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Requirements on student
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The conditions for credit prior to examination: 1. An approved seminar paper elaborated according to the tutor instructions. 2. Total number of points having been accumulated from all activities (including final credit test) must be at least at 65 % of maximum.
The exam: Exam is written, the examination paper consists of calculation of examples concerning topics explained in lectures, covered by basic literature, elaborated in tutorials.
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Content
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Introduction of the course
Stochastic processes
Data processing by selected robust methods
Time series and trend functions
Regression methods
Multidimensional and nonlinear regression
Markov chains and decision processes
Application of Markov chains in economics and finance
Waiting line models and their optimization
Deterministic inventory models
Stochastic inventory models
Usage of sw Mathematica for selected methods nad models
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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-
Extending:
ANDERSON, D.R., SWEENEY, D.J., WILLIAMS, T.A. An Introduction to Management Science - Quantitative Approaches to Decision Making.
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Recommended:
DANIEL, W. W., TERRELL, J. C. Business statistics : for management and economics. 7th ed. Boston : Houghton Mifflin Co., 1995. ISBN 0-395-71231-9.
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Recommended:
RENDER, B., HANNA, M. E., STAIR, R. M. Jr. Quantitative analysis for management. 1eight ed. Upper Saddle River : Prentice Hall, 2003. ISBN 0-13-049543-3.
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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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Contact hours
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52
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Preparation for an examination (30-60)
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60
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Preparation for comprehensive test (10-40)
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35
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Graduate study programme term essay (40-50)
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50
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Preparation for formative assessments (2-20)
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10
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Total
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207
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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: |
understand knowledge in the scope of a two-semester university course in mathematics, a course in statistics, one-semester courses in economic statistics and operations research |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
N/A |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
Use advanced probabilistic and statistical methods |
Select a proper method for practical application |
To verify validity of its usability assumptions and apply it for practical problem solution including economic or financial interpretation of results |
Apply time series analysis methods in practice including statistical description of demand |
Apply Markov chains with various applications in marketing, business, and finance |
Apply waiting line models with managerial applications |
Apply various classic inventory models both deterministic and stochastic ones |
Get acquainted sw Mathematica basics for solving selected problems |
Skills - skills resulting from the course: |
N/A |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Written exam |
Portfolio |
Skills - skills achieved by taking this course are verified by the following means: |
Written exam |
Competences - competence achieved by taking this course are verified by the following means: |
Written exam |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Individual study |
Interactive lecture |
Discussion |
E-learning |
Skills - the following training methods are used to achieve the required skills: |
Individual study |
Interactive lecture |
Discussion |
Competences - the following training methods are used to achieve the required competences: |
Discussion |
Interactive lecture |
Individual study |
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