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Course info
KMA / SR1
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Course description
Department/Unit / Abbreviation
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KMA
/
SR1
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Academic Year
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2024/2025
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Academic Year
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2024/2025
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Title
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Scientific Reading 1
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Form of course completion
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Pre-Exam Credit
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Form of course completion
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Pre-Exam Credit
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Accredited / Credits
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Yes,
2
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
2
[Hours/Week]
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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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No
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Included in study average
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NO
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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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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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NO
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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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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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English
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Internship duration
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0
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No. of hours of on-premise lessons |
0
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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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Discussion of selected publication outputs in Data Science.
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Requirements on student
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Active participation in the seminar - presentation of a scientific article.
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Content
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The seminar leads students to read professional texts. The seminars may include prestigious scientific periodicals that are not narrowly defined in the field (Nature, Science, ...). Each week we choose one article, we all read it and one of the students presents it at the next seminar. A discussion will follow. The seminar will spread scientific horizons, but has the ability to communicate on topics that are outside the field of specialization. To be experts in Data Science, this is a key competence, because all the data the graduates will have the opportunity to meet, will occur naturally from scientific fields, where the graduates will not be experts.
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Activities
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Fields of study
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Guarantors and lecturers
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Guarantors:
prof. RNDr. Karel Hron, Ph.D. ,
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Seminar lecturer:
Raj Narayan Dhara, Ph.D. (16%),
doc. RNDr. Eva Fišerová, Ph.D. (100%),
RNDr. Tomáš Fürst, Ph.D. (16%),
prof. RNDr. Karel Hron, Ph.D. (16%),
RNDr. Pavel Ludvík, Ph.D. (100%),
RNDr. Rostislav Vodák, Ph.D. (16%),
RNDr. Pavel Ženčák, Ph.D. (100%),
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Literature
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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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Attendace
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26
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Homework for Teaching
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20
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Total
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46
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Prerequisites - other information about course preconditions |
Interest in Data Science. |
Competences acquired |
Synthesis
Orientation in hot Data Science topics. |
Teaching methods |
- Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Assessment methods |
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