| Course title | Data Science Seminar 1 |
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| Course code | KMA/DSS1 |
| Organizational form of instruction | Seminar |
| Level of course | Bachelor |
| Year of study | not specified |
| Semester | Winter |
| Number of ECTS credits | 2 |
| Language of instruction | Czech |
| Status of course | Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
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| Course content |
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The seminar will deal with current trends in bayesian inference, machine learning, applied mathematics, Data Science, etc. The aim of the seminar is to provide a platfomr for the communication among students, academic staff and professionals from outside Academia
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| Learning activities and teaching methods |
| Dialogic Lecture (Discussion, Dialog, Brainstorming) |
| Learning outcomes |
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To follow what is currently happening in the field of data Science, if possible outside academia
Orientation in the world of Data Science |
| Prerequisites |
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Interest in the field of Data Science
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| Assessment methods and criteria |
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Student performance
Active participation at the seminar |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics - Specialization in Data Science (2020) | Category: Mathematics courses | 3 | Recommended year of study:3, Recommended semester: Winter |
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics - Specialization in Industrial Mathematics (2020) | Category: Mathematics courses | 3 | Recommended year of study:3, Recommended semester: Winter |
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics - Specialization in Business Mathematics (2021) | Category: Mathematics courses | 3 | Recommended year of study:3, Recommended semester: Winter |