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Course info
USII / FDM2
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Course description
Department/Unit / Abbreviation
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USII
/
FDM2
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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 Mining II
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Form of course completion
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Examination
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Form of course completion
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Examination
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Accredited / Credits
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Yes,
5
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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Lecture
2
[HRS/WEEK]
Tutorial
2
[HRS/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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No
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Included in study average
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YES
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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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No
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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 / 4
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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 |
A|B|C|D|E|F |
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 |
A|B|C|D|E|F |
Evaluation scale for credit before examination |
S|N |
Substituted course
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USII/PDM2
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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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Requirements on student
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The assignment is granted upon elaboration of given tasks at seminars (minimum achievement of 60 percent is required) and submitting the seminar paper.
Assessment methods: oral, written.
The oral examination is based on defence of the seminar paper. The final assessment is comprised of the following proportions: work at seminars - 40 percent, defence of the seminar paper - 60 percent; the written examination might also be considered.
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Content
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Úloha a pojmy DM, metodika CRISP-DM.
Přístup k datům a datové manipulace.
Modelování a vizualizace.
Seskupovací analýza, analýza nákupního koše, asociační pravidla.
Lineární regrese, logistická regrese.
Rozhodovací stromy.
Neuronové sítě.
Faktorová analýza.
Text Mining.
Web Mining.
Využití DM a sw nástroje.
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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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Basic:
Petr, Pavel. Data Mining.. Pardubice: Univerzita Pardubice, 2006. ISBN 80-7194-886-1.
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Basic:
WITTEN, I.H., FRANK, E., HALL, M.A. Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, 2011.
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Basic:
Berka, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.
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Basic:
Petr, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2015. ISBN 978-80-7395-873-2.
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Basic:
Petr, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2014. ISBN 978-80-7395-872-5.
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Further literature:
Resources to Internet
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Recommended:
GUIDICI P. Applied Data Mining - Statistical Methods for Business and Industry. Guildford, John Wiley & Sons, 2003, 364 s.. 2003.
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Recommended:
BERRY, M. - LINOFF G. Data Mining Techniques - For Marketing, Sales, and Customer Relationship Management. Indianapolis, John Wiley & Sons, 2004, 643 s.. 2004.
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Recommended:
PYLE, D. Data Preparation for Data Mining. San Diego, Academic Press, 1999, 540 s.. San Diego, 1999.
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Time requirements
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Full-time form of study
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Activities
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Time requirements for activity [h]
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Příprava na zkoušku
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20
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Příprava na zápočet
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20
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Kontaktní výuka
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52
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Domácí příprava na výuku
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18
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Semestrální práce
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40
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Total
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150
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Prerequisites - other information about course preconditions |
Základy práce s databází, pravděpodobnost a statistika. |
Competences acquired |
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Teaching methods |
- Monologic (reading, lecture, briefing)
- Work with text (with textbook, with book)
- Methods of individual activities
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Assessment methods |
- Oral examination
- Written examination
- Work-related product analysis
- Self project defence
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